Sample records for potential explanatory variables
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Spatial regression analysis on 32 years of total column ozone data
NASA Astrophysics Data System (ADS)
Knibbe, J. S.; van der A, R. J.; de Laat, A. T. J.
2014-08-01
Multiple-regression analyses have been performed on 32 years of total ozone column data that was spatially gridded with a 1 × 1.5° resolution. The total ozone data consist of the MSR (Multi Sensor Reanalysis; 1979-2008) and 2 years of assimilated SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) ozone data (2009-2010). The two-dimensionality in this data set allows us to perform the regressions locally and investigate spatial patterns of regression coefficients and their explanatory power. Seasonal dependencies of ozone on regressors are included in the analysis. A new physically oriented model is developed to parameterize stratospheric ozone. Ozone variations on nonseasonal timescales are parameterized by explanatory variables describing the solar cycle, stratospheric aerosols, the quasi-biennial oscillation (QBO), El Niño-Southern Oscillation (ENSO) and stratospheric alternative halogens which are parameterized by the effective equivalent stratospheric chlorine (EESC). For several explanatory variables, seasonally adjusted versions of these explanatory variables are constructed to account for the difference in their effect on ozone throughout the year. To account for seasonal variation in ozone, explanatory variables describing the polar vortex, geopotential height, potential vorticity and average day length are included. Results of this regression model are compared to that of a similar analysis based on a more commonly applied statistically oriented model. The physically oriented model provides spatial patterns in the regression results for each explanatory variable. The EESC has a significant depleting effect on ozone at mid- and high latitudes, the solar cycle affects ozone positively mostly in the Southern Hemisphere, stratospheric aerosols affect ozone negatively at high northern latitudes, the effect of QBO is positive and negative in the tropics and mid- to high latitudes, respectively, and ENSO affects ozone negatively between 30° N and 30° S, particularly over the Pacific. The contribution of explanatory variables describing seasonal ozone variation is generally large at mid- to high latitudes. We observe ozone increases with potential vorticity and day length and ozone decreases with geopotential height and variable ozone effects due to the polar vortex in regions to the north and south of the polar vortices. Recovery of ozone is identified globally. However, recovery rates and uncertainties strongly depend on choices that can be made in defining the explanatory variables. The application of several trend models, each with their own pros and cons, yields a large range of recovery rate estimates. Overall these results suggest that care has to be taken in determining ozone recovery rates, in particular for the Antarctic ozone hole.
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Ribic, C.A.; Miller, T.W.
1998-01-01
We investigated CART performance with a unimodal response curve for one continuous response and four continuous explanatory variables, where two variables were important (ie directly related to the response) and the other two were not. We explored performance under three relationship strengths and two explanatory variable conditions: equal importance and one variable four times as important as the other. We compared CART variable selection performance using three tree-selection rules ('minimum risk', 'minimum risk complexity', 'one standard error') to stepwise polynomial ordinary least squares (OLS) under four sample size conditions. The one-standard-error and minimum-risk-complexity methods performed about as well as stepwise OLS with large sample sizes when the relationship was strong. With weaker relationships, equally important explanatory variables and larger sample sizes, the one-standard-error and minimum-risk-complexity rules performed better than stepwise OLS. With weaker relationships and explanatory variables of unequal importance, tree-structured methods did not perform as well as stepwise OLS. Comparing performance within tree-structured methods, with a strong relationship and equally important explanatory variables, the one-standard-error-rule was more likely to choose the correct model than were the other tree-selection rules 1) with weaker relationships and equally important explanatory variables; and 2) under all relationship strengths when explanatory variables were of unequal importance and sample sizes were lower.
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Pacholewicz, Ewa; Swart, Arno; Wagenaar, Jaap A; Lipman, Len J A; Havelaar, Arie H
2016-12-01
This study aimed at identifying explanatory variables that were associated with Campylobacter and Escherichia coli concentrations throughout processing in two commercial broiler slaughterhouses. Quantative data on Campylobacter and E. coli along the processing line were collected. Moreover, information on batch characteristics, slaughterhouse practices, process performance, and environmental variables was collected through questionnaires, observations, and measurements, resulting in data on 19 potential explanatory variables. Analysis was conducted separately in each slaughterhouse to identify which variables were related to changes in concentrations of Campylobacter and E. coli during the processing steps: scalding, defeathering, evisceration, and chilling. Associations with explanatory variables were different in the slaughterhouses studied. In the first slaughterhouse, there was only one significant association: poorer uniformity of the weight of carcasses within a batch with less decrease in E. coli concentrations after defeathering. In the second slaughterhouse, significant statistical associations were found with variables, including age, uniformity, average weight of carcasses, Campylobacter concentrations in excreta and ceca, and E. coli concentrations in excreta. Bacterial concentrations in excreta and ceca were found to be the most prominent variables, because they were associated with concentration on carcasses at various processing points. Although the slaughterhouses produced specific products and had different batch characteristics and processing parameters, the effect of the significant variables was not always the same for each slaughterhouse. Therefore, each slaughterhouse needs to determine its particular relevant measures for hygiene control and process management. This identification could be supported by monitoring changes in bacterial concentrations during processing in individual slaughterhouses. In addition, the possibility that management and food handling practices in slaughterhouses contribute to the differences in bacterial contamination between slaughterhouses needs further investigation.
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Baldwin, Austin K.; Robertson, Dale M.; Saad, David A.; Magruder, Christopher
2013-01-01
In 2008, the U.S. Geological Survey and the Milwaukee Metropolitan Sewerage District initiated a study to develop regression models to estimate real-time concentrations and loads of chloride, suspended solids, phosphorus, and bacteria in streams near Milwaukee, Wisconsin. To collect monitoring data for calibration of models, water-quality sensors and automated samplers were installed at six sites in the Menomonee River drainage basin. The sensors continuously measured four potential explanatory variables: water temperature, specific conductance, dissolved oxygen, and turbidity. Discrete water-quality samples were collected and analyzed for five response variables: chloride, total suspended solids, total phosphorus, Escherichia coli bacteria, and fecal coliform bacteria. Using the first year of data, regression models were developed to continuously estimate the response variables on the basis of the continuously measured explanatory variables. Those models were published in a previous report. In this report, those models are refined using 2 years of additional data, and the relative improvement in model predictability is discussed. In addition, a set of regression models is presented for a new site in the Menomonee River Basin, Underwood Creek at Wauwatosa. The refined models use the same explanatory variables as the original models. The chloride models all used specific conductance as the explanatory variable, except for the model for the Little Menomonee River near Freistadt, which used both specific conductance and turbidity. Total suspended solids and total phosphorus models used turbidity as the only explanatory variable, and bacteria models used water temperature and turbidity as explanatory variables. An analysis of covariance (ANCOVA), used to compare the coefficients in the original models to those in the refined models calibrated using all of the data, showed that only 3 of the 25 original models changed significantly. Root-mean-squared errors (RMSEs) calculated for both the original and refined models using the entire dataset showed a median improvement in RMSE of 2.1 percent, with a range of 0.0–13.9 percent. Therefore most of the original models did almost as well at estimating concentrations during the validation period (October 2009–September 2011) as the refined models, which were calibrated using those data. Application of these refined models can produce continuously estimated concentrations of chloride, total suspended solids, total phosphorus, E. coli bacteria, and fecal coliform bacteria that may assist managers in quantifying the effects of land-use changes and improvement projects, establish total maximum daily loads, and enable better informed decision making in the future.
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Data Mining in Institutional Economics Tasks
NASA Astrophysics Data System (ADS)
Kirilyuk, Igor; Kuznetsova, Anna; Senko, Oleg
2018-02-01
The paper discusses problems associated with the use of data mining tools to study discrepancies between countries with different types of institutional matrices by variety of potential explanatory variables: climate, economic or infrastructure indicators. An approach is presented which is based on the search of statistically valid regularities describing the dependence of the institutional type on a single variable or a pair of variables. Examples of regularities are given.
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Multiple causes of nonstationarity in the Weihe annual low-flow series
NASA Astrophysics Data System (ADS)
Xiong, Bin; Xiong, Lihua; Chen, Jie; Xu, Chong-Yu; Li, Lingqi
2018-02-01
Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, through a nonstationary frequency analysis framework with the generalized linear model (GLM) to consider time-varying distribution parameters, the multiple explanatory variables were incorporated to explain the variation in low-flow distribution parameters. These variables are comprised of the three indices of human activities (HAs; i.e., population, POP; irrigation area, IAR; and gross domestic product, GDP) and the eight measuring indices of the climate and catchment conditions (i.e., total precipitation P, mean frequency of precipitation events λ, temperature T, potential evapotranspiration (EP), climate aridity index AIEP, base-flow index (BFI), recession constant K and the recession-related aridity index AIK). This framework was applied to model the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise regression for the optimal explanatory variables show that the variables related to irrigation, recession, temperature and precipitation play an important role in modeling. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that the nonstationary distribution model with any one of all explanatory variables is better than the one without explanatory variables, the nonstationary gamma distribution model with four optimal variables is the best model and AIK is of the highest relative importance among these four variables, followed by IAR, BFI and AIEP. We conclude that the incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to analyze future occurrences of low-flow extremes in similar areas.
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Austin, Peter C; Steyerberg, Ewout W
2012-06-20
When outcomes are binary, the c-statistic (equivalent to the area under the Receiver Operating Characteristic curve) is a standard measure of the predictive accuracy of a logistic regression model. An analytical expression was derived under the assumption that a continuous explanatory variable follows a normal distribution in those with and without the condition. We then conducted an extensive set of Monte Carlo simulations to examine whether the expressions derived under the assumption of binormality allowed for accurate prediction of the empirical c-statistic when the explanatory variable followed a normal distribution in the combined sample of those with and without the condition. We also examine the accuracy of the predicted c-statistic when the explanatory variable followed a gamma, log-normal or uniform distribution in combined sample of those with and without the condition. Under the assumption of binormality with equality of variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the product of the standard deviation of the normal components (reflecting more heterogeneity) and the log-odds ratio (reflecting larger effects). Under the assumption of binormality with unequal variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the standardized difference of the explanatory variable in those with and without the condition. In our Monte Carlo simulations, we found that these expressions allowed for reasonably accurate prediction of the empirical c-statistic when the distribution of the explanatory variable was normal, gamma, log-normal, and uniform in the entire sample of those with and without the condition. The discriminative ability of a continuous explanatory variable cannot be judged by its odds ratio alone, but always needs to be considered in relation to the heterogeneity of the population.
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Sharpening method of satellite thermal image based on the geographical statistical model
NASA Astrophysics Data System (ADS)
Qi, Pengcheng; Hu, Shixiong; Zhang, Haijun; Guo, Guangmeng
2016-04-01
To improve the effectiveness of thermal sharpening in mountainous regions, paying more attention to the laws of land surface energy balance, a thermal sharpening method based on the geographical statistical model (GSM) is proposed. Explanatory variables were selected from the processes of land surface energy budget and thermal infrared electromagnetic radiation transmission, then high spatial resolution (57 m) raster layers were generated for these variables through spatially simulating or using other raster data as proxies. Based on this, the local adaptation statistical relationship between brightness temperature (BT) and the explanatory variables, i.e., the GSM, was built at 1026-m resolution using the method of multivariate adaptive regression splines. Finally, the GSM was applied to the high-resolution (57-m) explanatory variables; thus, the high-resolution (57-m) BT image was obtained. This method produced a sharpening result with low error and good visual effect. The method can avoid the blind choice of explanatory variables and remove the dependence on synchronous imagery at visible and near-infrared bands. The influences of the explanatory variable combination, sampling method, and the residual error correction on sharpening results were analyzed deliberately, and their influence mechanisms are reported herein.
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NASA Astrophysics Data System (ADS)
Bichler, Andrea; Neumaier, Arnold; Hofmann, Thilo
2014-11-01
Microbial contamination of groundwater used for drinking water can affect public health and is of major concern to local water authorities and water suppliers. Potential hazards need to be identified in order to protect raw water resources. We propose a non-parametric data mining technique for exploring the presence of total coliforms (TC) in a groundwater abstraction well and its relationship to readily available, continuous time series of hydrometric monitoring parameters (seven year records of precipitation, river water levels, and groundwater heads). The original monitoring parameters were used to create an extensive generic dataset of explanatory variables by considering different accumulation or averaging periods, as well as temporal offsets of the explanatory variables. A classification tree based on the Chi-Squared Automatic Interaction Detection (CHAID) recursive partitioning algorithm revealed statistically significant relationships between precipitation and the presence of TC in both a production well and a nearby monitoring well. Different secondary explanatory variables were identified for the two wells. Elevated water levels and short-term water table fluctuations in the nearby river were found to be associated with TC in the observation well. The presence of TC in the production well was found to relate to elevated groundwater heads and fluctuations in groundwater levels. The generic variables created proved useful for increasing significance levels. The tree-based model was used to predict the occurrence of TC on the basis of hydrometric variables.
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2012-01-01
Background When outcomes are binary, the c-statistic (equivalent to the area under the Receiver Operating Characteristic curve) is a standard measure of the predictive accuracy of a logistic regression model. Methods An analytical expression was derived under the assumption that a continuous explanatory variable follows a normal distribution in those with and without the condition. We then conducted an extensive set of Monte Carlo simulations to examine whether the expressions derived under the assumption of binormality allowed for accurate prediction of the empirical c-statistic when the explanatory variable followed a normal distribution in the combined sample of those with and without the condition. We also examine the accuracy of the predicted c-statistic when the explanatory variable followed a gamma, log-normal or uniform distribution in combined sample of those with and without the condition. Results Under the assumption of binormality with equality of variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the product of the standard deviation of the normal components (reflecting more heterogeneity) and the log-odds ratio (reflecting larger effects). Under the assumption of binormality with unequal variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the standardized difference of the explanatory variable in those with and without the condition. In our Monte Carlo simulations, we found that these expressions allowed for reasonably accurate prediction of the empirical c-statistic when the distribution of the explanatory variable was normal, gamma, log-normal, and uniform in the entire sample of those with and without the condition. Conclusions The discriminative ability of a continuous explanatory variable cannot be judged by its odds ratio alone, but always needs to be considered in relation to the heterogeneity of the population. PMID:22716998
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The effect of topography on arctic-alpine aboveground biomass and NDVI patterns
NASA Astrophysics Data System (ADS)
Riihimäki, Henri; Heiskanen, Janne; Luoto, Miska
2017-04-01
Topography is a key factor affecting numerous environmental phenomena, including Arctic and alpine aboveground biomass (AGB) distribution. Digital Elevation Model (DEM) is a source of topographic information which can be linked to local growing conditions. Here, we investigated the effect of DEM derived variables, namely elevation, topographic position, radiation and wetness on AGB and Normalized Difference Vegetation Index (NDVI) in a Fennoscandian forest-alpine tundra ecotone. Boosted regression trees were used to derive non-parametric response curves and relative influences of the explanatory variables. Elevation and potential incoming solar radiation were the most important explanatory variables for both AGB and NDVI. In the NDVI models, the response curves were smooth compared with AGB models. This might be caused by large contribution of field and shrub layer to NDVI, especially at the treeline. Furthermore, radiation and elevation had a significant interaction, showing that the highest NDVI and biomass values are found from low-elevation, high-radiation sites, typically on the south-southwest facing valley slopes. Topographic wetness had minor influence on AGB and NDVI. Topographic position had generally weak effects on AGB and NDVI, although protected topographic position seemed to be more favorable below the treeline. The explanatory power of the topographic variables, particularly elevation and radiation demonstrates that DEM-derived land surface parameters can be used for exploring biomass distribution resulting from landform control on local growing conditions.
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This paper explores the potential of time-frequency wavelet analysis in resolving beach bacteria concentration and possible explanatory variables across multiple time scales with temporal information still preserved. The wavelet scalograms of E. coli concentrations and the explan...
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Modification of selected South Carolina bridge-scour envelope curves
Benedict, Stephen T.; Caldwell, Andral W.
2012-01-01
Historic scour was investigated at 231 bridges in the Piedmont and Coastal Plain physiographic provinces of South Carolina by the U.S. Geological Survey in cooperation with the South Carolina Department of Transportation. These investigations led to the development of field-derived envelope curves that provided supplementary tools to assess the potential for scour at bridges in South Carolina for selected scour components that included clear-water abutment, contraction, and pier scour, and live-bed pier and contraction scour. The envelope curves consist of a single curve with one explanatory variable encompassing all of the measured field data for the respective scour components. In the current investigation, the clear-water abutment-scour and live-bed contraction-scour envelope curves were modified to include a family of curves that utilized two explanatory variables, providing a means to further refine the assessment of scour potential for those specific scour components. The modified envelope curves and guidance for their application are presented in this report.
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Multiple Use One-Sided Hypotheses Testing in Univariate Linear Calibration
NASA Technical Reports Server (NTRS)
Krishnamoorthy, K.; Kulkarni, Pandurang M.; Mathew, Thomas
1996-01-01
Consider a normally distributed response variable, related to an explanatory variable through the simple linear regression model. Data obtained on the response variable, corresponding to known values of the explanatory variable (i.e., calibration data), are to be used for testing hypotheses concerning unknown values of the explanatory variable. We consider the problem of testing an unlimited sequence of one sided hypotheses concerning the explanatory variable, using the corresponding sequence of values of the response variable and the same set of calibration data. This is the situation of multiple use of the calibration data. The tests derived in this context are characterized by two types of uncertainties: one uncertainty associated with the sequence of values of the response variable, and a second uncertainty associated with the calibration data. We derive tests based on a condition that incorporates both of these uncertainties. The solution has practical applications in the decision limit problem. We illustrate our results using an example dealing with the estimation of blood alcohol concentration based on breath estimates of the alcohol concentration. In the example, the problem is to test if the unknown blood alcohol concentration of an individual exceeds a threshold that is safe for driving.
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Galloway, Joel M.
2014-01-01
The Red River of the North (hereafter referred to as “Red River”) Basin is an important hydrologic region where water is a valuable resource for the region’s economy. Continuous water-quality monitors have been operated by the U.S. Geological Survey, in cooperation with the North Dakota Department of Health, Minnesota Pollution Control Agency, City of Fargo, City of Moorhead, City of Grand Forks, and City of East Grand Forks at the Red River at Fargo, North Dakota, from 2003 through 2012 and at Grand Forks, N.Dak., from 2007 through 2012. The purpose of the monitoring was to provide a better understanding of the water-quality dynamics of the Red River and provide a way to track changes in water quality. Regression equations were developed that can be used to estimate concentrations and loads for dissolved solids, sulfate, chloride, nitrate plus nitrite, total phosphorus, and suspended sediment using explanatory variables such as streamflow, specific conductance, and turbidity. Specific conductance was determined to be a significant explanatory variable for estimating dissolved solids concentrations at the Red River at Fargo and Grand Forks. The regression equations provided good relations between dissolved solid concentrations and specific conductance for the Red River at Fargo and at Grand Forks, with adjusted coefficients of determination of 0.99 and 0.98, respectively. Specific conductance, log-transformed streamflow, and a seasonal component were statistically significant explanatory variables for estimating sulfate in the Red River at Fargo and Grand Forks. Regression equations provided good relations between sulfate concentrations and the explanatory variables, with adjusted coefficients of determination of 0.94 and 0.89, respectively. For the Red River at Fargo and Grand Forks, specific conductance, streamflow, and a seasonal component were statistically significant explanatory variables for estimating chloride. For the Red River at Grand Forks, a time component also was a statistically significant explanatory variable for estimating chloride. The regression equations for chloride at the Red River at Fargo provided a fair relation between chloride concentrations and the explanatory variables, with an adjusted coefficient of determination of 0.66 and the equation for the Red River at Grand Forks provided a relatively good relation between chloride concentrations and the explanatory variables, with an adjusted coefficient of determination of 0.77. Turbidity and streamflow were statistically significant explanatory variables for estimating nitrate plus nitrite concentrations at the Red River at Fargo and turbidity was the only statistically significant explanatory variable for estimating nitrate plus nitrite concentrations at Grand Forks. The regression equation for the Red River at Fargo provided a relatively poor relation between nitrate plus nitrite concentrations, turbidity, and streamflow, with an adjusted coefficient of determination of 0.46. The regression equation for the Red River at Grand Forks provided a fair relation between nitrate plus nitrite concentrations and turbidity, with an adjusted coefficient of determination of 0.73. Some of the variability that was not explained by the equations might be attributed to different sources contributing nitrates to the stream at different times. Turbidity, streamflow, and a seasonal component were statistically significant explanatory variables for estimating total phosphorus at the Red River at Fargo and Grand Forks. The regression equation for the Red River at Fargo provided a relatively fair relation between total phosphorus concentrations, turbidity, streamflow, and season, with an adjusted coefficient of determination of 0.74. The regression equation for the Red River at Grand Forks provided a good relation between total phosphorus concentrations, turbidity, streamflow, and season, with an adjusted coefficient of determination of 0.87. For the Red River at Fargo, turbidity and streamflow were statistically significant explanatory variables for estimating suspended-sediment concentrations. For the Red River at Grand Forks, turbidity was the only statistically significant explanatory variable for estimating suspended-sediment concentration. The regression equation at the Red River at Fargo provided a good relation between suspended-sediment concentration, turbidity, and streamflow, with an adjusted coefficient of determination of 0.95. The regression equation for the Red River at Grand Forks provided a good relation between suspended-sediment concentration and turbidity, with an adjusted coefficient of determination of 0.96.
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Curran, Christopher A.; Eng, Ken; Konrad, Christopher P.
2012-01-01
Regional low-flow regression models for estimating Q7,10 at ungaged stream sites are developed from the records of daily discharge at 65 continuous gaging stations (including 22 discontinued gaging stations) for the purpose of evaluating explanatory variables. By incorporating the base-flow recession time constant τ as an explanatory variable in the regression model, the root-mean square error for estimating Q7,10 at ungaged sites can be lowered to 72 percent (for known values of τ), which is 42 percent less than if only basin area and mean annual precipitation are used as explanatory variables. If partial-record sites are included in the regression data set, τ must be estimated from pairs of discharge measurements made during continuous periods of declining low flows. Eight measurement pairs are optimal for estimating τ at partial-record sites, and result in a lowering of the root-mean square error by 25 percent. A low-flow survey strategy that includes paired measurements at partial-record sites requires additional effort and planning beyond a standard strategy, but could be used to enhance regional estimates of τ and potentially reduce the error of regional regression models for estimating low-flow characteristics at ungaged sites.
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Spatial modelling of landscape aesthetic potential in urban-rural fringes.
Sahraoui, Yohan; Clauzel, Céline; Foltête, Jean-Christophe
2016-10-01
The aesthetic potential of landscape has to be modelled to provide tools for land-use planning. This involves identifying landscape attributes and revealing individuals' landscape preferences. Landscape aesthetic judgments of individuals (n = 1420) were studied by means of a photo-based survey. A set of landscape visibility metrics was created to measure landscape composition and configuration in each photograph using spatial data. These metrics were used as explanatory variables in multiple linear regressions to explain aesthetic judgments. We demonstrate that landscape aesthetic judgments may be synthesized in three consensus groups. The statistical results obtained show that landscape visibility metrics have good explanatory power. Ultimately, we propose a spatial modelling of landscape aesthetic potential based on these results combined with systematic computation of visibility metrics. Copyright © 2016 Elsevier Ltd. All rights reserved.
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ERIC Educational Resources Information Center
Ockey, Gary
2011-01-01
Drawing on current theories in personality, second-language (L2) oral ability, and psychometrics, this study investigates the extent to which self-consciousness and assertiveness are explanatory variables of L2 oral ability. Three hundred sixty first-year Japanese university students who were studying English as a foreign language participated in…
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A case study of alternative site response explanatory variables in Parkfield, California
Thompson, E.M.; Baise, L.G.; Kayen, R.E.; Morgan, E.C.; Kaklamanos, J.
2011-01-01
The combination of densely-spaced strong-motion stations in Parkfield, California, and spectral analysis of surface waves (SASW) profiles provides an ideal dataset for assessing the accuracy of different site response explanatory variables. We judge accuracy in terms of spatial coverage and correlation with observations. The performance of the alternative models is period-dependent, but generally we observe that: (1) where a profile is available, the square-root-of-impedance method outperforms VS30 (average S-wave velocity to 30 m depth), and (2) where a profile is unavailable, the topographic-slope method outperforms surficial geology. The fundamental site frequency is a valuable site response explanatory variable, though less valuable than VS30. However, given the expense and difficulty of obtaining reliable estimates of VS30 and the relative ease with which the fundamental site frequency can be computed, the fundamental site frequency may prove to be a valuable site response explanatory variable for many applications. ?? 2011 ASCE.
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Olea, Pedro P.; Mateo-Tomás, Patricia; de Frutos, Ángel
2010-01-01
Background Hierarchical partitioning (HP) is an analytical method of multiple regression that identifies the most likely causal factors while alleviating multicollinearity problems. Its use is increasing in ecology and conservation by its usefulness for complementing multiple regression analysis. A public-domain software “hier.part package” has been developed for running HP in R software. Its authors highlight a “minor rounding error” for hierarchies constructed from >9 variables, however potential bias by using this module has not yet been examined. Knowing this bias is pivotal because, for example, the ranking obtained in HP is being used as a criterion for establishing priorities of conservation. Methodology/Principal Findings Using numerical simulations and two real examples, we assessed the robustness of this HP module in relation to the order the variables have in the analysis. Results indicated a considerable effect of the variable order on the amount of independent variance explained by predictors for models with >9 explanatory variables. For these models the nominal ranking of importance of the predictors changed with variable order, i.e. predictors declared important by its contribution in explaining the response variable frequently changed to be either most or less important with other variable orders. The probability of changing position of a variable was best explained by the difference in independent explanatory power between that variable and the previous one in the nominal ranking of importance. The lesser is this difference, the more likely is the change of position. Conclusions/Significance HP should be applied with caution when more than 9 explanatory variables are used to know ranking of covariate importance. The explained variance is not a useful parameter to use in models with more than 9 independent variables. The inconsistency in the results obtained by HP should be considered in future studies as well as in those already published. Some recommendations to improve the analysis with this HP module are given. PMID:20657734
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Simoni-Wastila, Linda; Zuckerman, Ilene H; Singhal, Puneet K; Briesacher, Becky; Hsu, Van Doren
2005-03-01
The use of prescription drugs with addiction potential is an overlooked and growing problem among today's elderly. This paper provides national prevalence estimates of exposure to prescription drugs with addiction potential among community-dwelling elders and explores risk factors for such exposure. Using the Medicare Current Beneficiary Survey, a nationally-representative database of Medicare eligibles, we calculated the prevalence of abusable prescription drug use, overall, by therapeutic class, and by drug. Nearly 22% (7.22 million) of all community-dwelling Medicare elders used at least one prescription medication with addiction potential. Opioid analgesics were used most frequently (14.9%; 95% CI 14.0, 15.8%); central nervous system (CNS) depressants were used by 10.4% of the nation's elders (95% CI 9.5, 10.8%). Using logistic regression analysis, we examined the association of explanatory variables with three outcome variables: any controlled substances use, any opioid analgesic use, and any CNS depressant use. We found that females, whites, those aged 65-79, and those with non-spousal others, were significantly more likely to use one or more prescription drugs with addiction potential, controlling for health status and severity-of-illness. The significance and magnitude of several explanatory variables, including age, race, ethnicity, living arrangement, and health status, varied by therapeutic category. This paper provides an important first step in acknowledging the widespread use of abusable prescription drugs in elders, and provides a foundation for future research and practical solutions to preventing subsequent problem use of prescription drugs.
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Opdal, Anders Frugård; Jørgensen, Christian
2015-01-01
Harvesting may be a potent driver of demographic change and contemporary evolution, which both may have great impacts on animal populations. Research has focused on changes in phenotypic traits that are easily quantifiable and for which time series exist, such as size, age, sex, or gonad size, whereas potential changes in behavioural traits have been under-studied. Here, we analyse potential drivers of long-term changes in a behavioural trait for the Northeast Arctic stock of Atlantic cod Gadus morhua, namely choice of spawning location. For 104 years (1866–1969), commercial catches were recorded annually and reported by county along the Norwegian coast. During this time period, spawning ground distribution has fluctuated with a trend towards more northerly spawning. Spawning location is analysed against a suite of explanatory factors including climate, fishing pressure, density dependence, and demography. We find that demography (age or age at maturation) had the highest explanatory power for variation in spawning location, while climate had a limited effect below statistical significance. As to potential mechanisms, some effects of climate may act through demography, and explanatory variables for demography may also have absorbed direct evolutionary change in migration distance for which proxies were unavailable. Despite these caveats, we argue that fishing mortality, either through demographic or evolutionary change, has served as an effective driver for changing spawning locations in cod, and that additional explanatory factors related to climate add no significant information. PMID:25336028
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NASA Astrophysics Data System (ADS)
Abdussalam, Auwal; Monaghan, Andrew; Dukic, Vanja; Hayden, Mary; Hopson, Thomas; Leckebusch, Gregor
2013-04-01
Northwest Nigeria is a region with high risk of bacterial meningitis. Since the first documented epidemic of meningitis in Nigeria in 1905, the disease has been endemic in the northern part of the country, with epidemics occurring regularly. In this study we examine the influence of climate on the interannual variability of meningitis incidence and epidemics. Monthly aggregate counts of clinically confirmed hospital-reported cases of meningitis were collected in northwest Nigeria for the 22-year period spanning 1990-2011. Several generalized linear statistical models were fit to the monthly meningitis counts, including generalized additive models. Explanatory variables included monthly records of temperatures, humidity, rainfall, wind speed, sunshine and dustiness from weather stations nearest to the hospitals, and a time series of polysaccharide vaccination efficacy. The effects of other confounding factors -- i.e., mainly non-climatic factors for which records were not available -- were estimated as a smooth, monthly-varying function of time in the generalized additive models. Results reveal that the most important explanatory climatic variables are mean maximum monthly temperature, relative humidity and dustiness. Accounting for confounding factors (e.g., social processes) in the generalized additive models explains more of the year-to-year variation of meningococcal disease compared to those generalized linear models that do not account for such factors. Promising results from several models that included only explanatory variables that preceded the meningitis case data by 1-month suggest there may be potential for prediction of meningitis in northwest Nigeria to aid decision makers on this time scale.
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González Costa, J J; Reigosa, M J; Matías, J M; Covelo, E F
2017-09-01
The aim of this study was to model the sorption and retention of Cd, Cu, Ni, Pb and Zn in soils. To that extent, the sorption and retention of these metals were studied and the soil characterization was performed separately. Multiple stepwise regression was used to produce multivariate models with linear techniques and with support vector machines, all of which included 15 explanatory variables characterizing soils. When the R-squared values are represented, two different groups are noticed. Cr, Cu and Pb sorption and retention show a higher R-squared; the most explanatory variables being humified organic matter, Al oxides and, in some cases, cation-exchange capacity (CEC). The other group of metals (Cd, Ni and Zn) shows a lower R-squared, and clays are the most explanatory variables, including a percentage of vermiculite and slime. In some cases, quartz, plagioclase or hematite percentages also show some explanatory capacity. Support Vector Machine (SVM) regression shows that the different models are not as regular as in multiple regression in terms of number of variables, the regression for nickel adsorption being the one with the highest number of variables in its optimal model. On the other hand, there are cases where the most explanatory variables are the same for two metals, as it happens with Cd and Cr adsorption. A similar adsorption mechanism is thus postulated. These patterns of the introduction of variables in the model allow us to create explainability sequences. Those which are the most similar to the selectivity sequences obtained by Covelo (2005) are Mn oxides in multiple regression and change capacity in SVM. Among all the variables, the only one that is explanatory for all the metals after applying the maximum parsimony principle is the percentage of sand in the retention process. In the competitive model arising from the aforementioned sequences, the most intense competitiveness for the adsorption and retention of different metals appears between Cr and Cd, Cu and Zn in multiple regression; and between Cr and Cd in SVM regression. Copyright © 2017 Elsevier B.V. All rights reserved.
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Bustillos, Arnaldo Sanchez; Trigoso, Oswaldo Ortiz
2013-11-01
To examine access to health programs at workplace as a determinant of presenteeism among adults. Data source was a subsample of the 2009-2010 Canadian Community Health Survey. The outcome was self-reported reduced activities at work (presenteeism). The explanatory variable was self-reported access to a health program at workplace. Logistic regression was used to measure the association between outcome and explanatory variables adjusting for potential confounders. Adjusting for sex, age, education, income, work stress, and chronic conditions, presenteeism was not associated with having access to a health program at workplace (adjusted odds ratio, 1.23; 95% confidence interval, 0.91 to 1.65). The odds of presenteeism were higher in workers who reported high work stress and those with chronic medical conditions. This study found that access to health programs at workplace is not significantly associated with a decline in presenteeism.
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Remotely sensed vegetation moisture as explanatory variable of Lyme borreliosis incidence
NASA Astrophysics Data System (ADS)
Barrios, J. M.; Verstraeten, W. W.; Maes, P.; Clement, J.; Aerts, J. M.; Farifteh, J.; Lagrou, K.; Van Ranst, M.; Coppin, P.
2012-08-01
The strong correlation between environmental conditions and abundance and spatial spread of the tick Ixodes ricinus is widely documented. I. ricinus is in Europe the main vector of the bacterium Borrelia burgdorferi, the pathogen causing Lyme borreliosis (LB). Humidity in vegetated systems is a major factor in tick ecology and its effects might translate into disease incidence in humans. Time series of two remotely sensed indices with sensitivity to vegetation greenness and moisture were tested as explanatory variables of LB incidence. Wavelet-based multiresolution analysis allowed the examination of these signals at different temporal scales in study sites in Belgium, where increases in LB incidence were reported in recent years. The analysis showed the potential of the tested indices for disease monitoring, the usefulness of analyzing the signal in different time frames and the importance of local characteristics of the study area for the selection of the vegetation index.
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Blame Attribution as a Moderator of Perceptions of Sexual Orientation-Based Hate Crimes
ERIC Educational Resources Information Center
Cramer, Robert J.; Chandler, Joseph F.; Wakeman, Emily E.
2010-01-01
Blame attribution is a valuable mechanism explaining decision making. However, present literature mainly employs blame attribution as a dependent variable. The shortcoming of this fact is that blame attribution offers a potentially valuable explanatory mechanism for decision making. The authors designed two studies to investigate blame attribution…
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Motamarri, Srinivas; Boccelli, Dominic L
2012-09-15
Users of recreational waters may be exposed to elevated pathogen levels through various point/non-point sources. Typical daily notifications rely on microbial analysis of indicator organisms (e.g., Escherichia coli) that require 18, or more, hours to provide an adequate response. Modeling approaches, such as multivariate linear regression (MLR) and artificial neural networks (ANN), have been utilized to provide quick predictions of microbial concentrations for classification purposes, but generally suffer from high false negative rates. This study introduces the use of learning vector quantization (LVQ)--a direct classification approach--for comparison with MLR and ANN approaches and integrates input selection for model development with respect to primary and secondary water quality standards within the Charles River Basin (Massachusetts, USA) using meteorologic, hydrologic, and microbial explanatory variables. Integrating input selection into model development showed that discharge variables were the most important explanatory variables while antecedent rainfall and time since previous events were also important. With respect to classification, all three models adequately represented the non-violated samples (>90%). The MLR approach had the highest false negative rates associated with classifying violated samples (41-62% vs 13-43% (ANN) and <16% (LVQ)) when using five or more explanatory variables. The ANN performance was more similar to LVQ when a larger number of explanatory variables were utilized, but the ANN performance degraded toward MLR performance as explanatory variables were removed. Overall, the use of LVQ as a direct classifier provided the best overall classification ability with respect to violated/non-violated samples for both standards. Copyright © 2012 Elsevier Ltd. All rights reserved.
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Neglected children, shame-proneness, and depressive symptoms.
Bennett, David S; Sullivan, Margaret Wolan; Lewis, Michael
2010-11-01
Neglected children may be at increased risk for depressive symptoms. This study examines shame-proneness as an outcome of child neglect and as a potential explanatory variable in the relation between neglect and depressive symptoms. Participants were 111 children (52 with a Child Protective Services [CPS] allegation of neglect) seen at age 7. Neglected children reported more shame-proneness and more depressive symptoms than comparison children. Guilt-proneness, in contrast, was unrelated to neglect and depressive symptoms, indicating specificity for shame-proneness. The potential role of shame as a process variable that can help explain how some neglected children exhibit depressive symptoms is discussed.
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Garey, Lorra; Cheema, Mina K; Otal, Tanveer K; Schmidt, Norman B; Neighbors, Clayton; Zvolensky, Michael J
2016-10-01
Smoking rates are markedly higher among trauma-exposed individuals relative to non-trauma-exposed individuals. Extant work suggests that both perceived stress and negative affect reduction smoking expectancies are independent mechanisms that link trauma-related symptoms and smoking. Yet, no work has examined perceived stress and negative affect reduction smoking expectancies as potential explanatory variables for the relation between trauma-related symptom severity and smoking in a sequential pathway model. Methods The present study utilized a sample of treatment-seeking, trauma-exposed smokers (n = 363; 49.0% female) to examine perceived stress and negative affect reduction expectancies for smoking as potential sequential explanatory variables linking trauma-related symptom severity and nicotine dependence, perceived barriers to smoking cessation, and severity of withdrawal-related problems and symptoms during past quit attempts. As hypothesized, perceived stress and negative affect reduction expectancies had a significant sequential indirect effect on trauma-related symptom severity and criterion variables. Findings further elucidate the complex pathways through which trauma-related symptoms contribute to smoking behavior and cognitions, and highlight the importance of addressing perceived stress and negative affect reduction expectancies in smoking cessation programs among trauma-exposed individuals. (Am J Addict 2016;25:565-572). © 2016 American Academy of Addiction Psychiatry.
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Opdal, Anders Frugård; Jørgensen, Christian
2015-04-01
Harvesting may be a potent driver of demographic change and contemporary evolution, which both may have great impacts on animal populations. Research has focused on changes in phenotypic traits that are easily quantifiable and for which time series exist, such as size, age, sex, or gonad size, whereas potential changes in behavioural traits have been under-studied. Here, we analyse potential drivers of long-term changes in a behavioural trait for the Northeast Arctic stock of Atlantic cod Gadus morhua, namely choice of spawning location. For 104 years (1866-1969), commercial catches were recorded annually and reported by county along the Norwegian coast. During this time period, spawning ground distribution has fluctuated with a trend towards more northerly spawning. Spawning location is analysed against a suite of explanatory factors including climate, fishing pressure, density dependence, and demography. We find that demography (age or age at maturation) had the highest explanatory power for variation in spawning location, while climate had a limited effect below statistical significance. As to potential mechanisms, some effects of climate may act through demography, and explanatory variables for demography may also have absorbed direct evolutionary change in migration distance for which proxies were unavailable. Despite these caveats, we argue that fishing mortality, either through demographic or evolutionary change, has served as an effective driver for changing spawning locations in cod, and that additional explanatory factors related to climate add no significant information. © 2014 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.
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Robson, Andrew; Robson, Fiona
2015-01-01
To identify the combination of variables that explain nurses' continuation intention in the UK National Health Service. This alternative arena has permitted the replication of a private sector Australian study. This study provides understanding about the issues that affect nurse retention in a sector where employee attrition is a key challenge, further exacerbated by an ageing workforce. A quantitative study based on a self-completion survey questionnaire completed in 2010. Nurses employed in two UK National Health Service Foundation Trusts were surveyed and assessed using seven work-related constructs and various demographics including age generation. Through correlation, multiple regression and stepwise regression analysis, the potential combined effect of various explanatory variables on continuation intention was assessed, across the entire nursing cohort and in three age-generation groups. Three variables act in combination to explain continuation intention: work-family conflict, work attachment and importance of work to the individual. This combination of significant explanatory variables was consistent across the three generations of nursing employee. Work attachment was identified as the strongest marginal predictor of continuation intention. Work orientation has a greater impact on continuation intention compared with employer-directed interventions such as leader-member exchange, teamwork and autonomy. UK nurses are homogeneous across the three age-generations regarding explanation of continuation intention, with the significant explanatory measures being recognizably narrower in their focus and more greatly concentrated on the individual. This suggests that differentiated approaches to retention should perhaps not be pursued in this sectoral context. © 2014 John Wiley & Sons Ltd.
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Mercury concentrations in lentic fish populations related to ecosystem and watershed characteristics
Andrew L. Rypel
2010-01-01
Predicting mercury (Hg) concentrations of fishes at large spatial scales is a fundamental environmental challenge with the potential to improve human health. In this study, mercury concentrations were examined for five species across 161 lakes and ecosystem, and watershed parameters were investigated as explanatory variables in statistical models. For all species, Hg...
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A Strategy for Assessing the Impact of Time-Varying Family Risk Factors on High School Dropout
ERIC Educational Resources Information Center
Randolph, Karen A.; Fraser, Mark W.; Orthner, Dennis K.
2006-01-01
Human behavior is dynamic, influenced by changing situations over time. Yet the impact of the dynamic nature of important explanatory variables on outcomes has only recently begun to be estimated in developmental models. Using a risk factor perspective, this article demonstrates the potential benefits of regressing time-varying outcome measures on…
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A Unified Framework for Association Analysis with Multiple Related Phenotypes
Stephens, Matthew
2013-01-01
We consider the problem of assessing associations between multiple related outcome variables, and a single explanatory variable of interest. This problem arises in many settings, including genetic association studies, where the explanatory variable is genotype at a genetic variant. We outline a framework for conducting this type of analysis, based on Bayesian model comparison and model averaging for multivariate regressions. This framework unifies several common approaches to this problem, and includes both standard univariate and standard multivariate association tests as special cases. The framework also unifies the problems of testing for associations and explaining associations – that is, identifying which outcome variables are associated with genotype. This provides an alternative to the usual, but conceptually unsatisfying, approach of resorting to univariate tests when explaining and interpreting significant multivariate findings. The method is computationally tractable genome-wide for modest numbers of phenotypes (e.g. 5–10), and can be applied to summary data, without access to raw genotype and phenotype data. We illustrate the methods on both simulated examples, and to a genome-wide association study of blood lipid traits where we identify 18 potential novel genetic associations that were not identified by univariate analyses of the same data. PMID:23861737
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Brewster, Zachary W
2012-01-01
Despite popular claims that racism and discrimination are no longer salient issues in contemporary society, racial minorities continue to experience disparate treatment in everyday public interactions. The context of full-service restaurants is one such public setting wherein racial minority patrons, African Americans in particular, encounter racial prejudices and discriminate treatment. To further understand the causes of such discriminate treatment within the restaurant context, this article analyzes primary survey data derived from a community sample of servers (N = 200) to assess the explanatory power of one posited explanation—statistical discrimination. Taken as a whole, findings suggest that while a statistical discrimination framework toward understanding variability in servers’ discriminatory behaviors should not be disregarded, the framework’s explanatory utility is limited. Servers’ inferences about the potential profitability of waiting on customers across racial groups explain little of the overall variation in subjects’ self-reported discriminatory behaviors, thus suggesting that other factors not explored in this research are clearly operating and should be the focus of future inquires.
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ERIC Educational Resources Information Center
Melver, Toby A.
2011-01-01
The purpose of this mixed-methods study was to determine the factors that affect public school superintendent turnover in five western states. An explanatory theory was developed to cover all of the possible variables and show the relationship between those variables. The questions that guided this research study were: (1) What environmental…
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Baldwin, Austin K.; Graczyk, David J.; Robertson, Dale M.; Saad, David A.; Magruder, Christopher
2012-01-01
The models to estimate chloride concentrations all used specific conductance as the explanatory variable, except for the model for the Little Menomonee River near Freistadt, which used both specific conductance and turbidity as explanatory variables. Adjusted R2 values for the chloride models ranged from 0.74 to 0.97. Models to estimate total suspended solids and total phosphorus used turbidity as the only explanatory variable. Adjusted R2 values ranged from 0.77 to 0.94 for the total suspended solids models and from 0.55 to 0.75 for the total phosphorus models. Models to estimate indicator bacteria used water temperature and turbidity as the explanatory variables, with adjusted R2 values from 0.54 to 0.69 for Escherichia coli bacteria models and from 0.54 to 0.74 for fecal coliform bacteria models. Dissolved oxygen was not used in any of the final models. These models may help managers measure the effects of land-use changes and improvement projects, establish total maximum daily loads, estimate important water-quality indicators such as bacteria concentrations, and enable informed decision making in the future.
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Can we explain increases in young people’s psychological distress over time?
Sweeting, Helen; West, Patrick; Young, Robert; Der, Geoff
2010-01-01
This paper aims to explain previously described increases in self-reported psychological distress between 1987 and 2006 among samples identical in respect of age (15 years), school year and geographical location (West of Scotland). Such increases might be explained by changes in exposure (changes in levels of risk or protective factors) and/or by changes in vulnerability (changes in the relationship between risk/protective factors and psychological distress). Key areas of social change over this time period allow identification of potential explanatory factors, categorised as economic, family, educational, values and lifestyle and represented by variables common to each study. Psychological distress was measured via the 12-item General Health Questionnaire, Likert scored. Analyses were conducted on those with complete data on all variables (N = 3276 of 3929), and separately for males and females. Between 1987 and 2006, levels of almost every potential explanatory factor changed in line with general societal trends. Associations between explanatory factors and GHQ tended to be stronger among females, and at the later date. The strongest associations were with worries, arguments with parents, and, at the later date, school disengagement. The factors which best accounted for the increase in mean GHQ between 1987 and 2006 were arguments with parents, school disengagement, worry about school and, for females, worry about family relationships, reflecting both increasing exposure and vulnerability to these risk factors. A number of limitations to our analysis can be identified. However, our results reinforce the conclusions of others in highlighting the role of family and educational factors as plausible explanations for increases in young people’s psychological distress. PMID:20870334
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Brubacher, Jeffrey R.; Chan, Herbert; Erdelyi, Shannon; Schuurman, Nadine; Amram, Ofer
2016-01-01
Background British Columbia, Canada is a geographically large jurisdiction with varied environmental and socio-cultural contexts. This cross-sectional study examined variation in motor vehicle crash rates across 100 police patrols to investigate the association of crashes with key explanatory factors. Methods Eleven crash outcomes (total crashes, injury crashes, fatal crashes, speed related fatal crashes, total fatalities, single-vehicle night-time crashes, rear-end collisions, and collisions involving heavy vehicles, pedestrians, cyclists, or motorcyclists) were identified from police collision reports and insurance claims and mapped to police patrols. Six potential explanatory factors (intensity of traffic law enforcement, speed limits, climate, remoteness, socio-economic factors, and alcohol consumption) were also mapped to police patrols. We then studied the association between crashes and explanatory factors using negative binomial models with crash count per patrol as the response variable and explanatory factors as covariates. Results Between 2003 and 2012 there were 1,434,239 insurance claim collisions, 386,326 police reported crashes, and 3,404 fatal crashes. Across police patrols, there was marked variation in per capita crash rate and in potential explanatory factors. Several factors were associated with crash rates. Percent roads with speed limits ≤ 60 km/hr was positively associated with total crashes, injury crashes, rear end collisions, and collisions involving pedestrians, cyclists, and heavy vehicles; and negatively associated with single vehicle night-time crashes, fatal crashes, fatal speeding crashes, and total fatalities. Higher winter temperature was associated with lower rates of overall collisions, single vehicle night-time collisions, collisions involving heavy vehicles, and total fatalities. Lower socio-economic status was associated with higher rates of injury collisions, pedestrian collisions, fatal speeding collisions, and fatal collisions. Regions with dedicated traffic officers had fewer fatal crashes and fewer fatal speed related crashes but more rear end crashes and more crashes involving cyclists or pedestrians. The number of traffic citations per 1000 drivers was positively associated with total crashes, fatal crashes, total fatalities, fatal speeding crashes, injury crashes, single vehicle night-time crashes, and heavy vehicle crashes. Possible explanations for these associations are discussed. Conclusions There is wide variation in per capita rates of motor vehicle crashes across BC police patrols. Some variation is explained by factors such as climate, road type, remoteness, socioeconomic variables, and enforcement intensity. The ability of explanatory factors to predict crash rates would be improved if considered with local traffic volume by all travel modes. PMID:27099930
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Predicting High Quality AFQT with Youth Attitude Tracking Study Data
1991-12-01
for propensities. The history of the art of mental aptitude and psychological testing is long and convoluted. Names like Sir Francis Galton of England...Qualification Test . The explanatory variables reflect individual demographic, educational and labor market characteristics at the time of YATS interview. The...the fiftieth percentile on the Armed Forces Qualification Test . The explanatory variables reflect individual demographic, educational and labor market
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Ryberg, Karen R.
2007-01-01
This report presents the results of a study by the U.S. Geological Survey, done in cooperation with the North Dakota State Water Commission, to estimate water-quality constituent concentrations at seven sites on the Sheyenne River, N. Dak. Regression analysis of water-quality data collected in 1980-2006 was used to estimate concentrations for hardness, dissolved solids, calcium, magnesium, sodium, and sulfate. The explanatory variables examined for the regression relations were continuously monitored streamflow, specific conductance, and water temperature. For the conditions observed in 1980-2006, streamflow was a significant explanatory variable for some constituents. Specific conductance was a significant explanatory variable for all of the constituents, and water temperature was not a statistically significant explanatory variable for any of the constituents in this study. The regression relations were evaluated using common measures of variability, including R2, the proportion of variability in the estimated constituent concentration explained by the explanatory variables and regression equation. R2 values ranged from 0.784 for calcium to 0.997 for dissolved solids. The regression relations also were evaluated by calculating the median relative percentage difference (RPD) between measured constituent concentration and the constituent concentration estimated by the regression equations. Median RPDs ranged from 1.7 for dissolved solids to 11.5 for sulfate. The regression relations also may be used to estimate daily constituent loads. The relations should be monitored for change over time, especially at sites 2 and 3 which have a short period of record. In addition, caution should be used when the Sheyenne River is affected by ice or when upstream sites are affected by isolated storm runoff. Almost all of the outliers and highly influential samples removed from the analysis were made during periods when the Sheyenne River might be affected by ice.
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A site specific model and analysis of the neutral somatic mutation rate in whole-genome cancer data.
Bertl, Johanna; Guo, Qianyun; Juul, Malene; Besenbacher, Søren; Nielsen, Morten Muhlig; Hornshøj, Henrik; Pedersen, Jakob Skou; Hobolth, Asger
2018-04-19
Detailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration. To take into account the fine scale of the explanatory variables, we model the probabilities of different types of mutations for each position in the genome by multinomial logistic regression. We analyse 505 cancer genomes from 14 different cancer types and compare the performance in predicting mutation rate for both regional based models and site-specific models. We show that for 1000 randomly selected genomic positions, the site-specific model predicts the mutation rate much better than regional based models. We use a forward selection procedure to identify the most important explanatory variables. The procedure identifies site-specific conservation (phyloP), replication timing, and expression level as the best predictors for the mutation rate. Finally, our model confirms and quantifies certain well-known mutational signatures. We find that our site-specific multinomial regression model outperforms the regional based models. The possibility of including genomic variables on different scales and patient specific variables makes it a versatile framework for studying different mutational mechanisms. Our model can serve as the neutral null model for the mutational process; regions that deviate from the null model are candidates for elements that drive cancer development.
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Antheunis, Marjolijn L; Valkenburg, Patti M; Peter, Jochen
2007-12-01
The aims of this study were (a) to investigate the influence of computer-mediated communication (CMC) on interpersonal attraction and (b) to examine two underlying processes in the CMC-interpersonal attraction relationship. We identified two variables that may mediate the influence of CMC on interpersonal attraction: self-disclosure and direct questioning. Focusing on these potential mediating variables, we tested two explanatory hypotheses: the CMC-induced direct questioning hypothesis and the CMC-induced self-disclosure hypothesis. Eighty-one cross-sex dyads were randomly assigned to one of three experimental conditions: text-only CMC, visual CMC, and face-to-face communication. We did not find a direct effect of CMC on interpersonal attraction. However, we did find two positive indirect effects of text-only CMC on interpersonal attraction: text-only CMC stimulated both self-disclosure and direct questioning, both of which in turn enhanced interpersonal attraction. Results are discussed in light of uncertainty reduction theory and CMC theories.
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Waite, Ian R.
2014-01-01
As part of the USGS study of nutrient enrichment of streams in agricultural regions throughout the United States, about 30 sites within each of eight study areas were selected to capture a gradient of nutrient conditions. The objective was to develop watershed disturbance predictive models for macroinvertebrate and algal metrics at national and three regional landscape scales to obtain a better understanding of important explanatory variables. Explanatory variables in models were generated from landscape data, habitat, and chemistry. Instream nutrient concentration and variables assessing the amount of disturbance to the riparian zone (e.g., percent row crops or percent agriculture) were selected as most important explanatory variable in almost all boosted regression tree models regardless of landscape scale or assemblage. Frequently, TN and TP concentration and riparian agricultural land use variables showed a threshold type response at relatively low values to biotic metrics modeled. Some measure of habitat condition was also commonly selected in the final invertebrate models, though the variable(s) varied across regions. Results suggest national models tended to account for more general landscape/climate differences, while regional models incorporated both broad landscape scale and more specific local-scale variables.
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Spatial analysis of participation in the Waterloo Residential Energy Efficiency Project
NASA Astrophysics Data System (ADS)
Song, Ge Bella
Researchers are in broad agreement that energy-conserving actions produce economic as well as energy savings. Household energy rating systems (HERS) have been established in many countries to inform households of their house's current energy performance and to help reduce their energy consumption and greenhouse gas emissions. In Canada, the national EnerGuide for Houses (EGH) program is delivered by many local delivery agents, including non-profit green community organizations. Waterloo Region Green Solutions is the local non-profit that offers the EGH residential energy evaluation service to local households. The purpose of this thesis is to explore the determinants of household's participation in the residential energy efficiency program (REEP) in Waterloo Region, to explain the relationship between the explanatory variables and REEP participation, and to propose ways to improve this kind of program. A spatial (trend) analysis was conducted within a geographic information system (GIS) to determine the spatial patterns of the REEP participation in Waterloo Region from 1999 to 2006. The impact of sources of information on participation and relationships between participation rates and explanatory variables were identified. GIS proved successful in presenting a visual interpretation of spatial patterns of the REEP participation. In general, the participating households tend to be clustered in urban areas and scattered in rural areas. Different sources of information played significant roles in reaching participants in different years. Moreover, there was a relationship between each explanatory variable and the REEP participation rates. Statistical analysis was applied to obtain a quantitative assessment of relationships between hypothesized explanatory variables and participation in the REEP. The Poisson regression model was used to determine the relationship between hypothesized explanatory variables and REEP participation at the CDA level. The results show that all of the independent variables have a statistically significant positive relationship with REEP participation. These variables include level of education, average household income, employment rate, home ownership, population aged 65 and over, age of home, and number of eligible dwellings. The logistic regression model was used to assess the ability of the hypothesized explanatory variables to predict whether or not households would participate in a second follow-up evaluation after completing upgrades to their home. The results show all the explanatory variables have significant relationships with the dependent variable. The increased rating score, average household income, aged population, and age of home are positively related to the dependent variable. While the dwelling size and education has negative relationships with the dependent variable. In general, the contribution of this work provides a practical understanding of how the energy efficiency program operates, and insight into the type of variables that may be successful in bringing about changes in performance in the energy efficiency project in Waterloo Region. Secondly, with the completion of this research, future residential energy efficiency programs can use the information from this research and emulate or expand upon the efforts and lessons learned from the Residential Energy Efficiency Project in Waterloo Region case study. Thirdly, this research also contributes to practical experience on how to integrate different datasets using GIS.
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Temporal Trends and Hydrological Controls of Fisheries Production in the Madeira River (Brazil)
NASA Astrophysics Data System (ADS)
Kaplan, D. A.; Lima, M. A.; Doria, C.
2016-12-01
Amazonian river systems are characterized by a strongly seasonal flood pulse and important hydrologic effects have been observed in the dynamics of fish stocks and fishing yields. Changes in the Amazon's freshwater ecosystems from hydropower development will have a cascade of physical, ecological, and social effects and impacts on fish and fisheries are expected to be potentially irreversible. In this work we investigate shared trends and causal factors driving fish catch in the Madeira River (a major tributary of the Amazon) before dam construction to derive relationships between catch and natural hydrologic dynamics. We applied Dynamic Factor Analysis to investigate dynamics in fish catch across ten commercially important fish species in the Madeira River using daily fish landings data including species and total weight and daily hydrological data obtained from the Brazilian Geological Service. Total annual catch averaged over the 18-yr period (1990-2007) was 849 tons yr-1. Species with the highest catch included curimatã, dourada/filhote and pacu, highlighting the importance of medium and long-distance migratory species for fisheries production. We found a four-trend dynamic factor model (DFM) to best fit the observed data, assessed using the Akaike Information Criteria. Model goodness of fit was fair (R2=0.51) but highly variable across species (0.16 ≤ R2 ≤ 0.95). Fitted trends exhibited strong and regular year-to-year variation representative of the seasonal hydrologic pulsing observed on the Madeira River. Next, we considered 11 candidate explanatory time series and found the best DFM used four explanatory variables and only one common trend. While the model fit with explanatory variables was lower (R2=0.31) it removed much reliance on unknown common trends. The most important explanatory variable in this model was maximum water level followed by days flooded, river flow of the previous year and increment. We found unique responses to hydrological variations across the ten species, suggesting that dam operating rules need to closely mimic natural hydrologic regime in order to maintain the dynamics of these ecosystems. Future multidisciplinary analyses to understand the complex social-ecological effects of dams are needed to improve management practices and support sustainable livelihoods.
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Kessler, Ronald C.; Borges, Guilherme; Sampson, Nancy; Miller, Matthew; Nock, Matthew K.
2009-01-01
Controversy exists about whether the repeatedly-documented associations between smoking and subsequent suicide-related outcomes (SROs; ideation, plans, gestures, and attempts) are due to unmeasured common causes or to causal effects of smoking on SROs. We address this issue by examining associations of smoking with subsequent SROs with and without controls for potential explanatory variables in the National Comorbidity Survey (NCS) panel. The latter consists of 5001 people who participated in both the 199002 NCS and the 2001–03 NCS Follow-up Survey. Explanatory variables include socio-demographics, potential common causes (parental history of mental-substance disorders; other respondent childhood adversities) and potential mediators (respondent history of DSM-III-R mental-substance disorders). Small gross (i.e., without controls) prospective associations are found between history of early-onset nicotine dependence and both subsequent suicide ideation and, among ideators, subsequent suicide plans. None of the baseline smoking measures, though, predicts subsequent suicide gestures or attempts among ideators. The smoking-ideation association largely disappear, but the association of early-onset nicotine dependence with subsequent suicide plans persists (Odds-ratio = 3.0), after adjustment for control variables. However, the latter association is as strong with remitted as active nicotine dependence, arguing against a direct causal effect of nicotine dependence on suicide plans. Decomposition of the control variable effects, furthermore, suggests that these effects are due to common causes more than to mediators. These results refine our understanding of the ways in which smoking is associated with later SROs and for the most part argue against the view that these associations are due to causal effects of smoking. PMID:18645572
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Using Indirect Turbulence Measurements for Real-Time Parameter Estimation in Turbulent Air
NASA Technical Reports Server (NTRS)
Martos, Borja; Morelli, Eugene A.
2012-01-01
The use of indirect turbulence measurements for real-time estimation of parameters in a linear longitudinal dynamics model in atmospheric turbulence was studied. It is shown that measuring the atmospheric turbulence makes it possible to treat the turbulence as a measured explanatory variable in the parameter estimation problem. Commercial off-the-shelf sensors were researched and evaluated, then compared to air data booms. Sources of colored noise in the explanatory variables resulting from typical turbulence measurement techniques were identified and studied. A major source of colored noise in the explanatory variables was identified as frequency dependent upwash and time delay. The resulting upwash and time delay corrections were analyzed and compared to previous time shift dynamic modeling research. Simulation data as well as flight test data in atmospheric turbulence were used to verify the time delay behavior. Recommendations are given for follow on flight research and instrumentation.
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Cross, Paul C.; Klaver, Robert W.; Brennan, Angela; Creel, Scott; Beckmann, Jon P.; Higgs, Megan D.; Scurlock, Brandon M.
2013-01-01
Abstract. It is increasingly common for studies of animal ecology to use model-based predictions of environmental variables as explanatory or predictor variables, even though model prediction uncertainty is typically unknown. To demonstrate the potential for misleading inferences when model predictions with error are used in place of direct measurements, we compared snow water equivalent (SWE) and snow depth as predicted by the Snow Data Assimilation System (SNODAS) to field measurements of SWE and snow depth. We examined locations on elk (Cervus canadensis) winter ranges in western Wyoming, because modeled data such as SNODAS output are often used for inferences on elk ecology. Overall, SNODAS predictions tended to overestimate field measurements, prediction uncertainty was high, and the difference between SNODAS predictions and field measurements was greater in snow shadows for both snow variables compared to non-snow shadow areas. We used a simple simulation of snow effects on the probability of an elk being killed by a predator to show that, if SNODAS prediction uncertainty was ignored, we might have mistakenly concluded that SWE was not an important factor in where elk were killed in predatory attacks during the winter. In this simulation, we were interested in the effects of snow at finer scales (2) than the resolution of SNODAS. If bias were to decrease when SNODAS predictions are averaged over coarser scales, SNODAS would be applicable to population-level ecology studies. In our study, however, averaging predictions over moderate to broad spatial scales (9–2200 km2) did not reduce the differences between SNODAS predictions and field measurements. This study highlights the need to carefully evaluate two issues when using model output as an explanatory variable in subsequent analysis: (1) the model’s resolution relative to the scale of the ecological question of interest and (2) the implications of prediction uncertainty on inferences when using model predictions as explanatory or predictor variables.
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Dose-Response Calculator for ArcGIS
Hanser, Steven E.; Aldridge, Cameron L.; Leu, Matthias; Nielsen, Scott E.
2011-01-01
The Dose-Response Calculator for ArcGIS is a tool that extends the Environmental Systems Research Institute (ESRI) ArcGIS 10 Desktop application to aid with the visualization of relationships between two raster GIS datasets. A dose-response curve is a line graph commonly used in medical research to examine the effects of different dosage rates of a drug or chemical (for example, carcinogen) on an outcome of interest (for example, cell mutations) (Russell and others, 1982). Dose-response curves have recently been used in ecological studies to examine the influence of an explanatory dose variable (for example, percentage of habitat cover, distance to disturbance) on a predicted response (for example, survival, probability of occurrence, abundance) (Aldridge and others, 2008). These dose curves have been created by calculating the predicted response value from a statistical model at different levels of the explanatory dose variable while holding values of other explanatory variables constant. Curves (plots) developed using the Dose-Response Calculator overcome the need to hold variables constant by using values extracted from the predicted response surface of a spatially explicit statistical model fit in a GIS, which include the variation of all explanatory variables, to visualize the univariate response to the dose variable. Application of the Dose-Response Calculator can be extended beyond the assessment of statistical model predictions and may be used to visualize the relationship between any two raster GIS datasets (see example in tool instructions). This tool generates tabular data for use in further exploration of dose-response relationships and a graph of the dose-response curve.
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Canonical Commonality Analysis.
ERIC Educational Resources Information Center
Leister, K. Dawn
Commonality analysis is a method of partitioning variance that has advantages over more traditional "OVA" methods. Commonality analysis indicates the amount of explanatory power that is "unique" to a given predictor variable and the amount of explanatory power that is "common" to or shared with at least one predictor…
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Cinner, Joshua E; Graham, Nicholas A J; Huchery, Cindy; Macneil, M Aaron
2013-06-01
Coral reef fisheries support the livelihoods of millions of people but have been severely and negatively affected by anthropogenic activities. We conducted a systematic review of published data on the biomass of coral reef fishes to explore how the condition of reef fisheries is related to the density of local human populations, proximity of the reef to markets, and key environmental variables (including broad geomorphologic reef type, reef area, and net productivity). When only population density and environmental covariates were considered, high variability in fisheries conditions at low human population densities resulted in relatively weak explanatory models. The presence or absence of human settlements, habitat type, and distance to fish markets provided a much stronger explanatory model for the condition of reef fisheries. Fish biomass remained relatively low within 14 km of markets, then biomass increased exponentially as distance from reefs to markets increased. Our results suggest the need for an increased science and policy focus on markets as both a key driver of the condition of reef fisheries and a potential source of solutions. © 2012 Society for Conservation Biology.
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Bingham, P; Verlander, N Q; Cheal, M J
2004-09-01
This paper examines why Snow's contention that cholera was principally spread by water was not accepted in the 1850s by the medical elite. The consequence of rejection was that hundreds in the UK continued to die. Logistic regression was used to re-analyse data, first published in 1852 by William Farr, consisting of the 1849 mortality rate from cholera and eight potential explanatory variables for the 38 registration districts of London. Logistic regression does not support Farr's original conclusion that a district's elevation above high water was the most important explanatory variable. Elevation above high water, water supply and poor rate each have an independent significant effect on district cholera mortality rate, but in terms of size of effect, it can be argued that water supply most strongly 'invited' further consideration. The science of epidemiology, that Farr helped to found, has continued to advance. Had logistic regression been available to Farr, its application to his 1852 data set would have changed his conclusion.
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Demand impact and policy implications from taxing nitrogen fertilizer
DOE Office of Scientific and Technical Information (OSTI.GOV)
Foltz, J.C.
1992-12-01
Recent concern has focused on nitrogen fertilizer as a potential contaminant of groundwater. A demand function for fertilizer was developed using the quantity of fertilizer purchased, corn yield, real price of nitrogen fertilizer, lagged fertilizer purchases, a land value variable and the real price of corn as explanatory variables. Short and long-run price elasticities of demand were estimated to be inelastic. Support was found for the hypothesis that demand for nitrogen fertilizer has become more price inelastic over time. From a policy standpoint, a tax on nitrogen fertilizer may not be the most effective method to reduce consumption.
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Lombard, Pamela J.; Hodgkins, Glenn A.
2015-01-01
Regression equations to estimate peak streamflows with 1- to 500-year recurrence intervals (annual exceedance probabilities from 99 to 0.2 percent, respectively) were developed for small, ungaged streams in Maine. Equations presented here are the best available equations for estimating peak flows at ungaged basins in Maine with drainage areas from 0.3 to 12 square miles (mi2). Previously developed equations continue to be the best available equations for estimating peak flows for basin areas greater than 12 mi2. New equations presented here are based on streamflow records at 40 U.S. Geological Survey streamgages with a minimum of 10 years of recorded peak flows between 1963 and 2012. Ordinary least-squares regression techniques were used to determine the best explanatory variables for the regression equations. Traditional map-based explanatory variables were compared to variables requiring field measurements. Two field-based variables—culvert rust lines and bankfull channel widths—either were not commonly found or did not explain enough of the variability in the peak flows to warrant inclusion in the equations. The best explanatory variables were drainage area and percent basin wetlands; values for these variables were determined with a geographic information system. Generalized least-squares regression was used with these two variables to determine the equation coefficients and estimates of accuracy for the final equations.
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Biodiversity response to natural gradients of multiple stressors on continental margins
Sperling, Erik A.; Frieder, Christina A.; Levin, Lisa A.
2016-01-01
Sharp increases in atmospheric CO2 are resulting in ocean warming, acidification and deoxygenation that threaten marine organisms on continental margins and their ecological functions and resulting ecosystem services. The relative influence of these stressors on biodiversity remains unclear, as well as the threshold levels for change and when secondary stressors become important. One strategy to interpret adaptation potential and predict future faunal change is to examine ecological shifts along natural gradients in the modern ocean. Here, we assess the explanatory power of temperature, oxygen and the carbonate system for macrofaunal diversity and evenness along continental upwelling margins using variance partitioning techniques. Oxygen levels have the strongest explanatory capacity for variation in species diversity. Sharp drops in diversity are seen as O2 levels decline through the 0.5–0.15 ml l−1 (approx. 22–6 µM; approx. 21–5 matm) range, and as temperature increases through the 7–10°C range. pCO2 is the best explanatory variable in the Arabian Sea, but explains little of the variance in diversity in the eastern Pacific Ocean. By contrast, very little variation in evenness is explained by these three global change variables. The identification of sharp thresholds in ecological response are used here to predict areas of the seafloor where diversity is most at risk to future marine global change, noting that the existence of clear regional differences cautions against applying global thresholds. PMID:27122565
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Walter, Donald A.; Starn, J. Jeffrey
2013-01-01
Statistical models of nitrate occurrence in the glacial aquifer system of the northern United States, developed by the U.S. Geological Survey, use observed relations between nitrate concentrations and sets of explanatory variables—representing well-construction, environmental, and source characteristics— to predict the probability that nitrate, as nitrogen, will exceed a threshold concentration. However, the models do not explicitly account for the processes that control the transport of nitrogen from surface sources to a pumped well and use area-weighted mean spatial variables computed from within a circular buffer around the well as a simplified source-area conceptualization. The use of models that explicitly represent physical-transport processes can inform and, potentially, improve these statistical models. Specifically, groundwater-flow models simulate advective transport—predominant in many surficial aquifers— and can contribute to the refinement of the statistical models by (1) providing for improved, physically based representations of a source area to a well, and (2) allowing for more detailed estimates of environmental variables. A source area to a well, known as a contributing recharge area, represents the area at the water table that contributes recharge to a pumped well; a well pumped at a volumetric rate equal to the amount of recharge through a circular buffer will result in a contributing recharge area that is the same size as the buffer but has a shape that is a function of the hydrologic setting. These volume-equivalent contributing recharge areas will approximate circular buffers in areas of relatively flat hydraulic gradients, such as near groundwater divides, but in areas with steep hydraulic gradients will be elongated in the upgradient direction and agree less with the corresponding circular buffers. The degree to which process-model-estimated contributing recharge areas, which simulate advective transport and therefore account for local hydrologic settings, would inform and improve the development of statistical models can be implicitly estimated by evaluating the differences between explanatory variables estimated from the contributing recharge areas and the circular buffers used to develop existing statistical models. The larger the difference in estimated variables, the more likely that statistical models would be changed, and presumably improved, if explanatory variables estimated from contributing recharge areas were used in model development. Comparing model predictions from the two sets of estimated variables would further quantify—albeit implicitly—how an improved, physically based estimate of explanatory variables would be reflected in model predictions. Differences between the two sets of estimated explanatory variables and resultant model predictions vary spatially; greater differences are associated with areas of steep hydraulic gradients. A direct comparison, however, would require the development of a separate set of statistical models using explanatory variables from contributing recharge areas. Area-weighted means of three environmental variables—silt content, alfisol content, and depth to water from the U.S. Department of Agriculture State Soil Geographic (STATSGO) data—and one nitrogen-source variable (fertilizer-application rate from county data mapped to Enhanced National Land Cover Data 1992 (NLCDe 92) agricultural land use) can vary substantially between circular buffers and volume-equivalent contributing recharge areas and among contributing recharge areas for different sets of well variables. The differences in estimated explanatory variables are a function of the same factors affecting the contributing recharge areas as well as the spatial resolution and local distribution of the underlying spatial data. As a result, differences in estimated variables between circular buffers and contributing recharge areas are complex and site specific as evidenced by differences in estimated variables for circular buffers and contributing recharge areas of existing public-supply and network wells in the Great Miami River Basin. Large differences in areaweighted mean environmental variables are observed at the basin scale, determined by using the network of uniformly spaced hypothetical wells; the differences have a spatial pattern that generally is similar to spatial patterns in the underlying STATSGO data. Generally, the largest differences were observed for area-weighted nitrogen-application rate from county and national land-use data; the basin-scale differences ranged from -1,600 (indicating a larger value from within the volume-equivalent contributing recharge area) to 1,900 kilograms per year (kg/yr); the range in the underlying spatial data was from 0 to 2,200 kg/yr. Silt content, alfisol content, and nitrogen-application rate are defined by the underlying spatial data and are external to the groundwater system; however, depth to water is an environmental variable that can be estimated in more detail and, presumably, in a more physically based manner using a groundwater-flow model than using the spatial data. Model-calculated depths to water within circular buffers in the Great Miami River Basin differed substantially from values derived from the spatial data and had a much larger range. Differences in estimates of area-weighted spatial variables result in corresponding differences in predictions of nitrate occurrence in the aquifer. In addition to the factors affecting contributing recharge areas and estimated explanatory variables, differences in predictions also are a function of the specific set of explanatory variables used and the fitted slope coefficients in a given model. For models that predicted the probability of exceeding 1 and 4 milligrams per liter as nitrogen (mg/L as N), predicted probabilities using variables estimated from circular buffers and contributing recharge areas generally were correlated but differed significantly at the local and basin scale. The scale and distribution of prediction differences can be explained by the underlying differences in the estimated variables and the relative weight of the variables in the statistical models. Differences in predictions of exceeding 1 mg/L as N, which only includes environmental variables, generally correlated with the underlying differences in STATSGO data, whereas differences in exceeding 4 mg/L as N were more spatially extensive because that model included environmental and nitrogen-source variables. Using depths to water from within circular buffers derived from the spatial data and depths to water within the circular buffers calculated from the groundwater-flow model, restricted to the same range, resulted in large differences in predicted probabilities. The differences in estimated explanatory variables between contributing recharge areas and circular buffers indicate incorporation of physically based contributing recharge area likely would result in a different set of explanatory variables and an improved set of statistical models. The use of a groundwater-flow model to improve representations of source areas or to provide more-detailed estimates of specific explanatory variables includes a number of limitations and technical considerations. An assumption in these analyses is that (1) there is a state of mass balance between recharge and pumping, and (2) transport to a pumped well is under a steady state flow field. Comparison of volumeequivalent contributing recharge areas under steady-state and transient transport conditions at a location in the southeastern part of the basin shows the steady-state contributing recharge area is a reasonable approximation of the transient contributing recharge area after between 10 and 20 years of pumping. The first assumption is a more important consideration for this analysis. A gradient effect refers to a condition where simulated pumping from a well is less than recharge through the corresponding contributing recharge area. This generally takes place in areas with steep hydraulic gradients, such as near discharge locations, and can be mitigated using a finer model discretization. A boundary effect refers to a condition where recharge through the contributing recharge area is less than pumping. This indicates other sources of water to the simulated well and could reflect a real hydrologic process. In the Great Miami River Basin, large gradient and boundary effects—defined as the balance between pumping and recharge being less than half—occurred in 5 and 14 percent of the basin, respectively. The agreement between circular buffers and volume-equivalent contributing recharge areas, differences in estimated variables, and the effect on statisticalmodel predictions between the population of wells with a balance between pumping and recharge within 10 percent and the population of all wells were similar. This indicated process-model limitations did not affect the overall findings in the Great Miami River Basin; however, this would be model specific, and prudent use of a process model needs to entail a limitations analysis and, if necessary, alterations to the model.
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Bień-Barkowska, Katarzyna; Doroszkiewicz, Halina; Bień, Barbara
2017-01-01
The aim of this article was to identify the best predictors of distress suffered by family carers (FCs) of geriatric patients. A cross-sectional study of 100 FC-geriatric patient dyads was conducted. The negative impact of care (NIoC) subscale of the COPE index was dichotomized to identify lower stress (score of ≤15 on the scale) and higher stress (score of ≥16 on the scale) exerted on FCs by the process of providing care. The set of explanatory variables comprised a wide range of sociodemographic and care-related attributes, including patient-related results from comprehensive geriatric assessments and disease profiles. The best combination of explanatory variables that provided the highest predictive power for distress among FCs in the multiple logistic regression (LR) model was determined according to statistical information criteria. The statistical robustness of the observed relationships and the discriminative power of the model were verified with the cross-validation method. The mean age of FCs was 57.2 (±10.6) years, whereas that of geriatric patients was 81.7 (±6.4) years. Despite the broad initial set of potential explanatory variables, only five predictors were jointly selected for the best statistical model. A higher level of distress was independently predicted by lower self-evaluation of health; worse self-appraisal of coping well as a caregiver; lower sense of general support; more hours of care per week; and the motor retardation of the cared-for person measured with the speed of the Timed Up and Go (TUG) test. Worse performance on the TUG test was only the patient-related predictor of distress among the variables examined as contributors to the higher NIoC. Enhancing the mobility of geriatric patients through suitably tailored kinesitherapeutic methods during their hospital stay may mitigate the burden endured by FCs.
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Gasqui, Patrick; Trommenschlager, Jean-Marie
2017-08-21
Milk production in dairy cow udders is a complex and dynamic physiological process that has resisted explanatory modelling thus far. The current standard model, Wood's model, is empirical in nature, represents yield in daily terms, and was published in 1967. Here, we have developed a dynamic and integrated explanatory model that describes milk yield at the scale of the milking session. Our approach allowed us to formally represent and mathematically relate biological features of known relevance while accounting for stochasticity and conditional elements in the form of explicit hypotheses, which could then be tested and validated using real-life data. Using an explanatory mathematical and biological model to explore a physiological process and pinpoint potential problems (i.e., "problem finding"), it is possible to filter out unimportant variables that can be ignored, retaining only those essential to generating the most realistic model possible. Such modelling efforts are multidisciplinary by necessity. It is also helpful downstream because model results can be compared with observed data, via parameter estimation using maximum likelihood and statistical testing using model residuals. The process in its entirety yields a coherent, robust, and thus repeatable, model.
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Sperandei, Sandro; Vieira, Marcelo C; Reis, Arianne C
2016-11-01
To evaluate the attrition rate of members of a fitness center in the city of Rio de Janeiro and the potential explanatory variables for the phenomenon. An exploratory, observational study using a retrospective longitudinal frame. The records of 5240 individuals, members of the fitness center between January-2005 and June-2014, were monitored for 12 months or until cancellation of membership, whichever occurred first. A Cox proportional hazard regression model was adjusted to identify variables associated to higher risk of 'abandonment' of activities. This study was approved by Southern Cross University's Human Research Ethics Committee (approval number: ECN-15-176). The general survival curve shows that 63% of new members will abandon activities before the third month, and less than 4% will remain for more than 12 months of continuous activity. The regression model showed that age, previous level of physical activity, initial body mass index and motivations related to weight loss, hypertrophy, health, and aesthetics are related to risk of abandonment. Combined, those variables represent an important difference in the probability to abandon the gym between individuals with the best and worse combination of variables. Even individuals presenting the best combination of variables still present a high risk of abandonment before completion of 12 months of fitness center membership. Findings can assist in the identification of high risk individuals and therefore help in the development of strategies to prevent abandonment of physical activity practice. Copyright © 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
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Ryberg, Karen R.
2006-01-01
This report presents the results of a study by the U.S. Geological Survey, done in cooperation with the Bureau of Reclamation, U.S. Department of the Interior, to estimate water-quality constituent concentrations in the Red River of the North at Fargo, North Dakota. Regression analysis of water-quality data collected in 2003-05 was used to estimate concentrations and loads for alkalinity, dissolved solids, sulfate, chloride, total nitrite plus nitrate, total nitrogen, total phosphorus, and suspended sediment. The explanatory variables examined for regression relation were continuously monitored physical properties of water-streamflow, specific conductance, pH, water temperature, turbidity, and dissolved oxygen. For the conditions observed in 2003-05, streamflow was a significant explanatory variable for all estimated constituents except dissolved solids. pH, water temperature, and dissolved oxygen were not statistically significant explanatory variables for any of the constituents in this study. Specific conductance was a significant explanatory variable for alkalinity, dissolved solids, sulfate, and chloride. Turbidity was a significant explanatory variable for total phosphorus and suspended sediment. For the nutrients, total nitrite plus nitrate, total nitrogen, and total phosphorus, cosine and sine functions of time also were used to explain the seasonality in constituent concentrations. The regression equations were evaluated using common measures of variability, including R2, or the proportion of variability in the estimated constituent explained by the regression equation. R2 values ranged from 0.703 for total nitrogen concentration to 0.990 for dissolved-solids concentration. The regression equations also were evaluated by calculating the median relative percentage difference (RPD) between measured constituent concentration and the constituent concentration estimated by the regression equations. Median RPDs ranged from 1.1 for dissolved solids to 35.2 for total nitrite plus nitrate. Regression equations also were used to estimate daily constituent loads. Load estimates can be used by water-quality managers for comparison of current water-quality conditions to water-quality standards expressed as total maximum daily loads (TMDLs). TMDLs are a measure of the maximum amount of chemical constituents that a water body can receive and still meet established water-quality standards. The peak loads generally occurred in June and July when streamflow also peaked.
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Factors affecting plant species composition of hedgerows: relative importance and hierarchy
NASA Astrophysics Data System (ADS)
Deckers, Bart; Hermy, Martin; Muys, Bart
2004-07-01
Although there has been a clear quantitative and qualitative decline in traditional hedgerow network landscapes during last century, hedgerows are crucial for the conservation of rural biodiversity, functioning as an important habitat, refuge and corridor for numerous species. To safeguard this conservation function, insight in the basic organizing principles of hedgerow plant communities is needed. The vegetation composition of 511 individual hedgerows situated within an ancient hedgerow network landscape in Flanders, Belgium was recorded, in combination with a wide range of explanatory variables, including a selection of spatial variables. Non-parametric statistics in combination with multivariate data analysis techniques were used to study the effect of individual explanatory variables. Next, variables were grouped in five distinct subsets and the relative importance of these variable groups was assessed by two related variation partitioning techniques, partial regression and partial canonical correspondence analysis, taking into account explicitly the existence of intercorrelations between variables of different factor groups. Most explanatory variables affected significantly hedgerow species richness and composition. Multivariate analysis showed that, besides adjacent land use, hedgerow management, soil conditions, hedgerow type and origin, the role of other factors such as hedge dimensions, intactness, etc., could certainly not be neglected. Furthermore, both methods revealed the same overall ranking of the five distinct factor groups. Besides a predominant impact of abiotic environmental conditions, it was found that management variables and structural aspects have a relatively larger influence on the distribution of plant species in hedgerows than their historical background or spatial configuration.
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Vacca, G M; Paschino, P; Dettori, M L; Bergamaschi, M; Cipolat-Gotet, C; Bittante, G; Pazzola, M
2016-09-01
Dairy goat farming is practiced worldwide, within a range of different farming systems. Here we investigated the effects of environmental factors and morphology on milk traits of the Sardinian goat population. Sardinian goats are currently reared in Sardinia (Italy) in a low-input context, similar to many goat farming systems, especially in developing countries. Milk and morphological traits from 1,050 Sardinian goats from 42 farms were recorded. We observed a high variability regarding morphological traits, such as coat color, ear length and direction, horn presence, and udder shape. Such variability derived partly from the unplanned repeated crossbreeding of the native Sardinian goats with exotic breeds, especially Maltese goats. The farms located in the mountains were characterized by the traditional farming system and the lowest percentage of crossbred goats. Explanatory factors analysis was used to summarize the interrelated measured milk variables. The explanatory factor related to fat, protein, and energy content of milk (the "Quality" latent variable) explained about 30% of the variance of the whole data set of measured milk traits followed by the "Hygiene" (19%), "Production" (19%), and "Acidity" (11%) factors. The "Quality" and "Hygiene" factors were not affected by any of the farm classification items, whereas "Production" and "Acidity" were affected only by altitude and size of herds, respectively, indicating the adaptation of the local goat population to different environmental conditions. The use of latent explanatory factor analysis allowed us to clearly explain the large variability of milk traits, revealing that the Sardinian goat population cannot be divided into subpopulations based on milk attitude The factors, properly integrated with genetic data, may be useful tools in future selection programs.
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Seasonally adjusted birth frequencies follow the Poisson distribution.
Barra, Mathias; Lindstrøm, Jonas C; Adams, Samantha S; Augestad, Liv A
2015-12-15
Variations in birth frequencies have an impact on activity planning in maternity wards. Previous studies of this phenomenon have commonly included elective births. A Danish study of spontaneous births found that birth frequencies were well modelled by a Poisson process. Somewhat unexpectedly, there were also weekly variations in the frequency of spontaneous births. Another study claimed that birth frequencies follow the Benford distribution. Our objective was to test these results. We analysed 50,017 spontaneous births at Akershus University Hospital in the period 1999-2014. To investigate the Poisson distribution of these births, we plotted their variance over a sliding average. We specified various Poisson regression models, with the number of births on a given day as the outcome variable. The explanatory variables included various combinations of years, months, days of the week and the digit sum of the date. The relationship between the variance and the average fits well with an underlying Poisson process. A Benford distribution was disproved by a goodness-of-fit test (p < 0.01). The fundamental model with year and month as explanatory variables is significantly improved (p < 0.001) by adding day of the week as an explanatory variable. Altogether 7.5% more children are born on Tuesdays than on Sundays. The digit sum of the date is non-significant as an explanatory variable (p = 0.23), nor does it increase the explained variance. INERPRETATION: Spontaneous births are well modelled by a time-dependent Poisson process when monthly and day-of-the-week variation is included. The frequency is highest in summer towards June and July, Friday and Tuesday stand out as particularly busy days, and the activity level is at its lowest during weekends.
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Body Fat Percentage Prediction Using Intelligent Hybrid Approaches
Shao, Yuehjen E.
2014-01-01
Excess of body fat often leads to obesity. Obesity is typically associated with serious medical diseases, such as cancer, heart disease, and diabetes. Accordingly, knowing the body fat is an extremely important issue since it affects everyone's health. Although there are several ways to measure the body fat percentage (BFP), the accurate methods are often associated with hassle and/or high costs. Traditional single-stage approaches may use certain body measurements or explanatory variables to predict the BFP. Diverging from existing approaches, this study proposes new intelligent hybrid approaches to obtain fewer explanatory variables, and the proposed forecasting models are able to effectively predict the BFP. The proposed hybrid models consist of multiple regression (MR), artificial neural network (ANN), multivariate adaptive regression splines (MARS), and support vector regression (SVR) techniques. The first stage of the modeling includes the use of MR and MARS to obtain fewer but more important sets of explanatory variables. In the second stage, the remaining important variables are served as inputs for the other forecasting methods. A real dataset was used to demonstrate the development of the proposed hybrid models. The prediction results revealed that the proposed hybrid schemes outperformed the typical, single-stage forecasting models. PMID:24723804
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A Study of Effects of MultiCollinearity in the Multivariable Analysis
Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; (Peter) He, Qinghua; Lillard, James W.
2015-01-01
A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables. PMID:25664257
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A Study of Effects of MultiCollinearity in the Multivariable Analysis.
Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; Peter He, Qinghua; Lillard, James W
2014-10-01
A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables.
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Pérez-Romero, Carmen; Ortega-Díaz, M Isabel; Ocaña-Riola, Ricardo; Martín-Martín, José Jesús
2018-05-11
To analyze technical efficiency by type of property and management of general hospitals in the Spanish National Health System (2010-2012) and identify hospital and regional explanatory variables. 230 hospitals were analyzed combining data envelopment analysis and fixed effects multilevel linear models. Data envelopment analysis measured overall, technical and scale efficiency, and the analysis of explanatory factors was performed using multilevel models. The average rate of overall technical efficiency of hospitals without legal personality is lower than hospitals with legal personality (0.691 and 0.876 in 2012). There is a significant variability in efficiency under variable returns (TE) by direct, indirect and mixed forms of management. The 29% of the variability in TE es attributable to the Region. Legal personality increased the TE of the hospitals by 11.14 points. On the other hand, most of the forms of management (different to those of the traditional hospitals) increased TE in varying percentages. At regional level, according to the model considered, insularity and average annual income per household are explanatory variables of TE. Having legal personality favours technical efficiency. The regulatory and management framework of hospitals, more than public or private ownership, seem to explain technical efficiency. Regional characteristics explain the variability in TE. Copyright © 2018 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.
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Serial killers with military experience: applying learning theory to serial murder.
Castle, Tammy; Hensley, Christopher
2002-08-01
Scholars have endeavored to study the motivation and causality behind serial murder by researching biological, psychological, and sociological variables. Some of these studies have provided support for the relationship between these variables and serial murder. However, the study of serial murder continues to be an exploratory rather than explanatory research topic. This article examines the possible link between serial killers and military service. Citing previous research using social learning theory for the study of murder, this article explores how potential serial killers learn to reinforce violence, aggression, and murder in military boot camps. As with other variables considered in serial killer research, military experience alone cannot account for all cases of serial murder. Future research should continue to examine this possible link.
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Bayesian Adaptive Lasso for Ordinal Regression with Latent Variables
ERIC Educational Resources Information Center
Feng, Xiang-Nan; Wu, Hao-Tian; Song, Xin-Yuan
2017-01-01
We consider an ordinal regression model with latent variables to investigate the effects of observable and latent explanatory variables on the ordinal responses of interest. Each latent variable is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a Bayesian adaptive lasso procedure to conduct…
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A non-linear data mining parameter selection algorithm for continuous variables
Razavi, Marianne; Brady, Sean
2017-01-01
In this article, we propose a new data mining algorithm, by which one can both capture the non-linearity in data and also find the best subset model. To produce an enhanced subset of the original variables, a preferred selection method should have the potential of adding a supplementary level of regression analysis that would capture complex relationships in the data via mathematical transformation of the predictors and exploration of synergistic effects of combined variables. The method that we present here has the potential to produce an optimal subset of variables, rendering the overall process of model selection more efficient. This algorithm introduces interpretable parameters by transforming the original inputs and also a faithful fit to the data. The core objective of this paper is to introduce a new estimation technique for the classical least square regression framework. This new automatic variable transformation and model selection method could offer an optimal and stable model that minimizes the mean square error and variability, while combining all possible subset selection methodology with the inclusion variable transformations and interactions. Moreover, this method controls multicollinearity, leading to an optimal set of explanatory variables. PMID:29131829
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Response of benthic algae to environmental gradients in an agriculturally dominated landscape
Munn, M.D.; Black, R.W.; Gruber, S.J.
2002-01-01
Benthic algal communities were assessed in an agriculturally dominated landscape in the Central Columbia Plateau, Washington, to determine which environmental variables best explained species distributions, and whether algae species optima models were useful in predicting specific water-quality parameters. Land uses in the study area included forest, range, urban, and agriculture. Most of the streams in this region can be characterized as open-channel systems influenced by intensive dryland (nonirrigated) and irrigated agriculture. Algal communities in forested streams were dominated by blue-green algae, with communities in urban and range streams dominated by diatoms. The predominance of either blue-greens or diatoms in agricultural streams varied greatly depending on the specific site. Canonical correspondence analysis (CCA) indicated a strong gradient effect of several key environmental variables on benthic algal community composition. Conductivity and % agriculture were the dominant explanatory variables when all sites (n = 24) were included in the CCA; water velocity replaced conductivity when the CCA included only agricultural and urban sites. Other significant explanatory variables included dissolved inorganic nitrogen (DIN), orthophosphate (OP), discharge, and precipitation. Regression and calibration models accurately predicted conductivity based on benthic algal communities, with OP having slightly lower predictability. The model for DIN was poor, and therefore may be less useful in this system. Thirty-four algal taxa were identified as potential indicators of conductivity and nutrient conditions, with most indicators being diatoms except for the blue-greens Anabaenasp. and Lyngbya sp.
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You, Ming P.; Rensing, Kelly; Renton, Michael; Barbetti, Martin J.
2017-01-01
Subterranean clover (Trifolium subterraneum) is a critical pasture legume in Mediterranean regions of southern Australia and elsewhere, including Mediterranean-type climatic regions in Africa, Asia, Australia, Europe, North America, and South America. Pythium damping-off and root disease caused by Pythium irregulare is a significant threat to subterranean clover in Australia and a study was conducted to define how environmental factors (viz. temperature, soil type, moisture and nutrition) as well as variety, influence the extent of damping-off and root disease as well as subterranean clover productivity under challenge by this pathogen. Relationships were statistically modeled using linear and generalized linear models and boosted regression trees. Modeling found complex relationships between explanatory variables and the extent of Pythium damping-off and root rot. Linear modeling identified high-level (4 or 5-way) significant interactions for each dependent variable (dry shoot and root weight, emergence, tap and lateral root disease index). Furthermore, all explanatory variables (temperature, soil, moisture, nutrition, variety) were found significant as part of some interaction within these models. A significant five-way interaction between all explanatory variables was found for both dry shoot and root dry weights, and a four way interaction between temperature, soil, moisture, and nutrition was found for both tap and lateral root disease index. A second approach to modeling using boosted regression trees provided support for and helped clarify the complex nature of the relationships found in linear models. All explanatory variables showed at least 5% relative influence on each of the five dependent variables. All models indicated differences due to soil type, with the sand-based soil having either higher weights, greater emergence, or lower disease indices; while lowest weights and less emergence, as well as higher disease indices, were found for loam soil and low temperature. There was more severe tap and lateral root rot disease in higher moisture situations. PMID:29184544
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Year-class formation of upper St. Lawrence River northern pike
Smith, B.M.; Farrell, J.M.; Underwood, H.B.; Smith, S.J.
2007-01-01
Variables associated with year-class formation in upper St. Lawrence River northern pike Esox lucius were examined to explore population trends. A partial least-squares (PLS) regression model (PLS 1) was used to relate a year-class strength index (YCSI; 1974-1997) to explanatory variables associated with spawning and nursery areas (seasonal water level and temperature and their variability, number of ice days, and last day of ice presence). A second model (PLS 2) incorporated four additional ecological variables: potential predators (abundance of double-crested cormorants Phalacrocorax auritus and yellow perch Perca flavescens), female northern pike biomass (as a measure of stock-recruitment effects), and total phosphorus (productivity). Trends in adult northern pike catch revealed a decline (1981-2005), and year-class strength was positively related to catch per unit effort (CPUE; R2 = 0.58). The YCSI exceeded the 23-year mean in only 2 of the last 10 years. Cyclic patterns in the YCSI time series (along with strong year-classes every 4-6 years) were apparent, as was a dampening effect of amplitude beginning around 1990. The PLS 1 model explained over 50% of variation in both explanatory variables and the dependent variable, YCSI first-order moving-average residuals. Variables retained (N = 10; Wold's statistic ??? 0.8) included negative YCSI associations with high summer water levels, high variability in spring and fall water levels, and variability in fall water temperature. The YCSI exhibited positive associations with high spring, summer, and fall water temperature, variability in spring temperature, and high winter and spring water level. The PLS 2 model led to positive YCSI associations with phosphorus and yellow perch CPUE and a negative correlation with double-crested cormorant abundance. Environmental variables (water level and temperature) are hypothesized to regulate northern pike YCSI cycles, and dampening in YCSI magnitude may be related to a combination of factors, including wetland habitat changes, reduced nutrient loading, and increased predation by double-crested cormorants. ?? Copyright by the American Fisheries Society 2007.
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The use of cognitive ability measures as explanatory variables in regression analysis.
Junker, Brian; Schofield, Lynne Steuerle; Taylor, Lowell J
2012-12-01
Cognitive ability measures are often taken as explanatory variables in regression analysis, e.g., as a factor affecting a market outcome such as an individual's wage, or a decision such as an individual's education acquisition. Cognitive ability is a latent construct; its true value is unobserved. Nonetheless, researchers often assume that a test score , constructed via standard psychometric practice from individuals' responses to test items, can be safely used in regression analysis. We examine problems that can arise, and suggest that an alternative approach, a "mixed effects structural equations" (MESE) model, may be more appropriate in many circumstances.
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Random parameter models for accident prediction on two-lane undivided highways in India.
Dinu, R R; Veeraragavan, A
2011-02-01
Generalized linear modeling (GLM), with the assumption of Poisson or negative binomial error structure, has been widely employed in road accident modeling. A number of explanatory variables related to traffic, road geometry, and environment that contribute to accident occurrence have been identified and accident prediction models have been proposed. The accident prediction models reported in literature largely employ the fixed parameter modeling approach, where the magnitude of influence of an explanatory variable is considered to be fixed for any observation in the population. Similar models have been proposed for Indian highways too, which include additional variables representing traffic composition. The mixed traffic on Indian highways comes with a lot of variability within, ranging from difference in vehicle types to variability in driver behavior. This could result in variability in the effect of explanatory variables on accidents across locations. Random parameter models, which can capture some of such variability, are expected to be more appropriate for the Indian situation. The present study is an attempt to employ random parameter modeling for accident prediction on two-lane undivided rural highways in India. Three years of accident history, from nearly 200 km of highway segments, is used to calibrate and validate the models. The results of the analysis suggest that the model coefficients for traffic volume, proportion of cars, motorized two-wheelers and trucks in traffic, and driveway density and horizontal and vertical curvatures are randomly distributed across locations. The paper is concluded with a discussion on modeling results and the limitations of the present study. Copyright © 2010 Elsevier Ltd. All rights reserved.
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Craig, Marlies H; Sharp, Brian L; Mabaso, Musawenkosi LH; Kleinschmidt, Immo
2007-01-01
Background Several malaria risk maps have been developed in recent years, many from the prevalence of infection data collated by the MARA (Mapping Malaria Risk in Africa) project, and using various environmental data sets as predictors. Variable selection is a major obstacle due to analytical problems caused by over-fitting, confounding and non-independence in the data. Testing and comparing every combination of explanatory variables in a Bayesian spatial framework remains unfeasible for most researchers. The aim of this study was to develop a malaria risk map using a systematic and practicable variable selection process for spatial analysis and mapping of historical malaria risk in Botswana. Results Of 50 potential explanatory variables from eight environmental data themes, 42 were significantly associated with malaria prevalence in univariate logistic regression and were ranked by the Akaike Information Criterion. Those correlated with higher-ranking relatives of the same environmental theme, were temporarily excluded. The remaining 14 candidates were ranked by selection frequency after running automated step-wise selection procedures on 1000 bootstrap samples drawn from the data. A non-spatial multiple-variable model was developed through step-wise inclusion in order of selection frequency. Previously excluded variables were then re-evaluated for inclusion, using further step-wise bootstrap procedures, resulting in the exclusion of another variable. Finally a Bayesian geo-statistical model using Markov Chain Monte Carlo simulation was fitted to the data, resulting in a final model of three predictor variables, namely summer rainfall, mean annual temperature and altitude. Each was independently and significantly associated with malaria prevalence after allowing for spatial correlation. This model was used to predict malaria prevalence at unobserved locations, producing a smooth risk map for the whole country. Conclusion We have produced a highly plausible and parsimonious model of historical malaria risk for Botswana from point-referenced data from a 1961/2 prevalence survey of malaria infection in 1–14 year old children. After starting with a list of 50 potential variables we ended with three highly plausible predictors, by applying a systematic and repeatable staged variable selection procedure that included a spatial analysis, which has application for other environmentally determined infectious diseases. All this was accomplished using general-purpose statistical software. PMID:17892584
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Ortega Cisneros, Kelly; Smit, Albertus J.; Laudien, Jürgen; Schoeman, David S.
2011-01-01
Sandy beach ecological theory states that physical features of the beach control macrobenthic community structure on all but the most dissipative beaches. However, few studies have simultaneously evaluated the relative importance of physical, chemical and biological factors as potential explanatory variables for meso-scale spatio-temporal patterns of intertidal community structure in these systems. Here, we investigate macroinfaunal community structure of a micro-tidal sandy beach that is located on an oligotrophic subtropical coast and is influenced by seasonal estuarine input. We repeatedly sampled biological and environmental variables at a series of beach transects arranged at increasing distances from the estuary mouth. Sampling took place over a period of five months, corresponding with the transition between the dry and wet season. This allowed assessment of biological-physical relationships across chemical and nutritional gradients associated with a range of estuarine inputs. Physical, chemical, and biological response variables, as well as measures of community structure, showed significant spatio-temporal patterns. In general, bivariate relationships between biological and environmental variables were rare and weak. However, multivariate correlation approaches identified a variety of environmental variables (i.e., sampling session, the C∶N ratio of particulate organic matter, dissolved inorganic nutrient concentrations, various size fractions of photopigment concentrations, salinity and, to a lesser extent, beach width and sediment kurtosis) that either alone or combined provided significant explanatory power for spatio-temporal patterns of macroinfaunal community structure. Overall, these results showed that the macrobenthic community on Mtunzini Beach was not structured primarily by physical factors, but instead by a complex and dynamic blend of nutritional, chemical and physical drivers. This emphasises the need to recognise ocean-exposed sandy beaches as functional ecosystems in their own right. PMID:21858213
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Ortega Cisneros, Kelly; Smit, Albertus J; Laudien, Jürgen; Schoeman, David S
2011-01-01
Sandy beach ecological theory states that physical features of the beach control macrobenthic community structure on all but the most dissipative beaches. However, few studies have simultaneously evaluated the relative importance of physical, chemical and biological factors as potential explanatory variables for meso-scale spatio-temporal patterns of intertidal community structure in these systems. Here, we investigate macroinfaunal community structure of a micro-tidal sandy beach that is located on an oligotrophic subtropical coast and is influenced by seasonal estuarine input. We repeatedly sampled biological and environmental variables at a series of beach transects arranged at increasing distances from the estuary mouth. Sampling took place over a period of five months, corresponding with the transition between the dry and wet season. This allowed assessment of biological-physical relationships across chemical and nutritional gradients associated with a range of estuarine inputs. Physical, chemical, and biological response variables, as well as measures of community structure, showed significant spatio-temporal patterns. In general, bivariate relationships between biological and environmental variables were rare and weak. However, multivariate correlation approaches identified a variety of environmental variables (i.e., sampling session, the C∶N ratio of particulate organic matter, dissolved inorganic nutrient concentrations, various size fractions of photopigment concentrations, salinity and, to a lesser extent, beach width and sediment kurtosis) that either alone or combined provided significant explanatory power for spatio-temporal patterns of macroinfaunal community structure. Overall, these results showed that the macrobenthic community on Mtunzini Beach was not structured primarily by physical factors, but instead by a complex and dynamic blend of nutritional, chemical and physical drivers. This emphasises the need to recognise ocean-exposed sandy beaches as functional ecosystems in their own right.
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Wiedermann, Wolfgang; Li, Xintong
2018-04-16
In nonexperimental data, at least three possible explanations exist for the association of two variables x and y: (1) x is the cause of y, (2) y is the cause of x, or (3) an unmeasured confounder is present. Statistical tests that identify which of the three explanatory models fits best would be a useful adjunct to the use of theory alone. The present article introduces one such statistical method, direction dependence analysis (DDA), which assesses the relative plausibility of the three explanatory models on the basis of higher-moment information about the variables (i.e., skewness and kurtosis). DDA involves the evaluation of three properties of the data: (1) the observed distributions of the variables, (2) the residual distributions of the competing models, and (3) the independence properties of the predictors and residuals of the competing models. When the observed variables are nonnormally distributed, we show that DDA components can be used to uniquely identify each explanatory model. Statistical inference methods for model selection are presented, and macros to implement DDA in SPSS are provided. An empirical example is given to illustrate the approach. Conceptual and empirical considerations are discussed for best-practice applications in psychological data, and sample size recommendations based on previous simulation studies are provided.
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Independent contrasts and PGLS regression estimators are equivalent.
Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary
2012-05-01
We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.
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Yahaya, Adamu; Nor, Norashidah Mohamed; Habibullah, Muzafar Shah; Ghani, Judhiana Abd; Noor, Zaleha Mohd
2016-01-01
Developing countries have witnessed economic growth as their GDP keeps increasing steadily over the years. The growth led to higher energy consumption which eventually leads to increase in air pollutions that pose a danger to human health. People's healthcare demand, in turn, increase due to the changes in the socioeconomic life and improvement in the health technology. This study is an attempt to investigate the impact of environmental quality on per capital health expenditure in 125 developing countries within a panel cointegration framework from 1995 to 2012. We found out that a long-run relationship exists between per capita health expenditure and all explanatory variables as they were panel cointegrated. The explanatory variables were found to be statistically significant in explaining the per capita health expenditure. The result further revealed that CO2 has the highest explanatory power on the per capita health expenditure. The impact of the explanatory power of the variables is greater in the long-run compared to the short-run. Based on this result, we conclude that environmental quality is a powerful determinant of health expenditure in developing countries. Therefore, developing countries should as a matter of health care policy give provision of healthy air a priority via effective policy implementation on environmental management and control measures to lessen the pressure on health care expenditure. Moreover more environmental proxies with alternative methods should be considered in the future research.
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NASA Astrophysics Data System (ADS)
Holburn, E. R.; Bledsoe, B. P.; Poff, N. L.; Cuhaciyan, C. O.
2005-05-01
Using over 300 R/EMAP sites in OR and WA, we examine the relative explanatory power of watershed, valley, and reach scale descriptors in modeling variation in benthic macroinvertebrate indices. Innovative metrics describing flow regime, geomorphic processes, and hydrologic-distance weighted watershed and valley characteristics are used in multiple regression and regression tree modeling to predict EPT richness, % EPT, EPT/C, and % Plecoptera. A nested design using seven ecoregions is employed to evaluate the influence of geographic scale and environmental heterogeneity on the explanatory power of individual and combined scales. Regression tree models are constructed to explain variability while identifying threshold responses and interactions. Cross-validated models demonstrate differences in the explanatory power associated with single-scale and multi-scale models as environmental heterogeneity is varied. Models explaining the greatest variability in biological indices result from multi-scale combinations of physical descriptors. Results also indicate that substantial variation in benthic macroinvertebrate response can be explained with process-based watershed and valley scale metrics derived exclusively from common geospatial data. This study outlines a general framework for identifying key processes driving macroinvertebrate assemblages across a range of scales and establishing the geographic extent at which various levels of physical description best explain biological variability. Such information can guide process-based stratification to avoid spurious comparison of dissimilar stream types in bioassessments and ensure that key environmental gradients are adequately represented in sampling designs.
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Real-time predictive seasonal influenza model in Catalonia, Spain
Basile, Luca; Oviedo de la Fuente, Manuel; Torner, Nuria; Martínez, Ana; Jané, Mireia
2018-01-01
Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010–2011 to 2013–2014) was created. A pilot test was conducted during the 2014–2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015–2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015–2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included. PMID:29513710
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Ledien, Julia; Sorn, Sopheak; Hem, Sopheak; Huy, Rekol; Buchy, Philippe; Tarantola, Arnaud; Cappelle, Julien
2017-01-01
Remote sensing can contribute to early warning for diseases with environmental drivers, such as flooding for leptospirosis. In this study we assessed whether and which remotely-sensed flooding indicator could be used in Cambodia to study any disease for which flooding has already been identified as an important driver, using leptospirosis as a case study. The performance of six potential flooding indicators was assessed by ground truthing. The Modified Normalized Difference Water Index (MNDWI) was used to estimate the Risk Ratio (RR) of being infected by leptospirosis when exposed to floods it detected, in particular during the rainy season. Chi-square tests were also calculated. Another variable-the time elapsed since the first flooding of the year-was created using MNDWI values and was also included as explanatory variable in a generalized linear model (GLM) and in a boosted regression tree model (BRT) of leptospirosis infections, along with other explanatory variables. Interestingly, MNDWI thresholds for both detecting water and predicting the risk of leptospirosis seroconversion were independently evaluated at -0.3. Value of MNDWI greater than -0.3 was significantly related to leptospirosis infection (RR = 1.61 [1.10-1.52]; χ2 = 5.64, p-value = 0.02, especially during the rainy season (RR = 2.03 [1.25-3.28]; χ2 = 8.15, p-value = 0.004). Time since the first flooding of the year was a significant risk factor in our GLM model (p-value = 0.042). These results suggest that MNDWI may be useful as a risk indicator in an early warning remote sensing tool for flood-driven diseases like leptospirosis in South East Asia.
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Apers, Silke; Kovacs, Adrienne H; Luyckx, Koen; Alday, Luis; Berghammer, Malin; Budts, Werner; Callus, Edward; Caruana, Maryanne; Chidambarathanu, Shanthi; Cook, Stephen C; Dellborg, Mikael; Enomoto, Junko; Eriksen, Katrine; Fernandes, Susan M; Jackson, Jamie L; Johansson, Bengt; Khairy, Paul; Kutty, Shelby; Menahem, Samuel; Rempel, Gwen; Sluman, Maayke A; Soufi, Alexandra; Thomet, Corina; Veldtman, Gruschen; Wang, Jou-Kou; White, Kamila; Moons, Philip
2015-01-20
Data on patient-reported outcomes (PROs) in adults with congenital heart disease (CHD) are inconsistent and vary across the world. Better understanding of PROs and their differences across cultural and geographic barriers can best be accomplished via international studies using uniform research methods. The APPROACH-IS consortium (Assessment of Patterns of Patient-Reported Outcomes in Adults with Congenital Heart disease - International Study) was created for this purpose and investigates PROs in adults with CHD worldwide. This paper outlines the project rationale, design, and methods. APPROACH-IS is a cross-sectional study. The goal is to recruit 3500-4000 adults with CHD from 15 countries in five major regions of the world (Asia, Australia, Europe, North and South America). Self-report questionnaires are administered to capture information on PRO domains: (i) perceived health status (12-item Short-form Health Survey & EuroQOL-5D); (ii) psychological functioning (Hospital Anxiety and Depression Scale); (iii) health behaviors (Health-Behavior Scale-Congenital Heart Disease); and (iv) quality of life (Linear Analog Scale & Satisfaction With Life Scale). Additionally, potential explanatory variables are assessed: (i) socio-demographic variables; (ii) medical history (chart review); (iii) sense of coherence (Orientation to Life Questionnaire); and (iv) illness perceptions (Brief Illness Perception Questionnaire). Descriptive analyses and multilevel models will examine differences in PROs and investigate potential explanatory variables. APPROACH-IS represents a global effort to increase research understanding and capacity in the field of CHD, and will have major implications for patient care. Results will generate valuable information for developing interventions to optimize patients' health and well-being. ClinicalTrials.gov: NCT02150603. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
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NASA Astrophysics Data System (ADS)
Stemler, Steven Edward
This study explored school effectiveness in mathematics and science at the fourth grade using data from IEA's Third International Mathematics and Science Study (TIMSS). Fourteen of the 26 countries participating in TIMSS at the fourth grade possessed sufficient between-school variability in mathematics achievement to justify the creation of explanatory models of school effectiveness while 13 countries possessed sufficient between-school variability in science achievement. Exploratory models were developed using variables drawn from student, teacher, and school questionnaires. The variables were chosen to represent the domains of student involvement, instructional methods, classroom organization, school climate, and school structure. Six explanatory models for each subject were analyzed using two-level hierarchical linear modeling (HLM) and were compared to models using only school mean SES as an explanatory variable. The amount of variability in student achievement in mathematics attributable to differences between schools ranged from 16% in Cyprus to 56% in Latvia, while the amount of between-school variance in science achievement ranged from 12% in Korea to 59% in Latvia. In general, about one-quarter of the variability in mathematics and science achievement was found to lie between schools. The research findings revealed that after adjusting for differences in student backgrounds across schools, the most effective schools in mathematics and science had students who reported seeing a positive relationship between hard work, belief in their own abilities, and achievement. In addition, more effective schools had students who reported less frequent use of computers and calculators in the classroom. These relationships were found to be stable across explanatory models, cultural contexts, and subject areas. This study has contributed a unique element to the literature by examining school effectiveness at the fourth grade across two subject areas and across 14 different countries. The results indicate that further exploration of the relationship between school effectiveness and student locus of control warrants serious consideration. Future research on school effectiveness is recommended, perhaps using trend data and looking at different grade levels.
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[A look at gender in research. A qualitative analysis].
López, Mercedes Eguiluz; Lerendegui, María Luisa Samitier; Simon, Teresa Yago; Aznar, Concepción Tomas; Martin, Dolores Ariño; Briz, Teresa Oliveros; Gavin, Gema Palacio; Botaya, Rosa Magallón
2011-10-01
To find out the views of a group of national women experts on gender and health on the key elements to consider in research with a gender perspective, and what are the resistance barriers when trying to include this perspective in the research. Meeting of a group of experts. Two types of analysis, discourse analysis, analysis of group outputs were used. Zaragoza. The group consists of six experts. An expert was defined as person accredited with specific training in the subject, and/or has presented her research at seminars, workshops, conferences on gender and health in recent years, or belongs to one of the networks of research on gender and heath. Qualitative analysis. Research with a gender perspective should meet the health needs and problems of both men and women, with those issues that contribute to determining the influence of gender on people's health being of special interest. The methodology should reflect this perspective throughout the research process and the variables should have gender explanatory potential. The main resistance barriers that prevent the inclusion of this perspective were related to the scientific institution, to feminism, and to a lack of training. A project cannot be considered to have a gender perspective if it does not include the analysis of variables with a gender explanatory potential and is not designed to help reduce inequalities between men and women. Knowing the resistance barriers that hinder this approach can guide future training. Copyright © 2010 Elsevier España, S.L. All rights reserved.
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Empirical spatial econometric modelling of small scale neighbourhood
NASA Astrophysics Data System (ADS)
Gerkman, Linda
2012-07-01
The aim of the paper is to model small scale neighbourhood in a house price model by implementing the newest methodology in spatial econometrics. A common problem when modelling house prices is that in practice it is seldom possible to obtain all the desired variables. Especially variables capturing the small scale neighbourhood conditions are hard to find. If there are important explanatory variables missing from the model, the omitted variables are spatially autocorrelated and they are correlated with the explanatory variables included in the model, it can be shown that a spatial Durbin model is motivated. In the empirical application on new house price data from Helsinki in Finland, we find the motivation for a spatial Durbin model, we estimate the model and interpret the estimates for the summary measures of impacts. By the analysis we show that the model structure makes it possible to model and find small scale neighbourhood effects, when we know that they exist, but we are lacking proper variables to measure them.
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Vocational Teacher Stress and the Educational System.
ERIC Educational Resources Information Center
Adams, Elaine; Heath-Camp, Betty; Camp, William G.
1999-01-01
A multiple regression analysis of data from 235 secondary vocational teachers in Virginia found that educational system-related variables explained most teacher stress. The most important explanatory variables were task stress and role overload. (SK)
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Explanatory Models and Medication Adherence in Patients with Depression in South India
Siddappa, Adarsh Lakkur; Raman, Rajesh; Hattur, Basavana Gowdappa
2017-01-01
Introduction Conceptualization of depression may have bearing on treatment seeking. It may affect adherence behaviour of the patients. Aim To find out the explanatory models and their relationship with socio-demographic variables and medication adherence in patients with depression. Materials and Methods Fifty-eight consecutive patients with depression in remission were recruited as per selection criteria. Socio-demographic details were collected. Patients were assessed using Mental Distress Explanatory Model Questionnaire (MDEMQ) and Morisky Medication Adherence Scale (MMAS). Results Significant scores were observed in all dimensions of explanatory models. In the Mann-Whitney U test the patient’s marital status (MU=113.500, p=0.05, sig≤0.05, 2-tailed), and family history of mental illness (MU=165.5, p=0.03, sig≤0.05, 2-tailed) had a statistically significant group difference in the score of MDEMQ. In linear regression analysis, four predictors (MDEMQ subscales Stress, Western physiology, Non-Western physiology and Supernatural) had significantly predicted the value of MMAS (R2=0.937, f=153.558, p<0.001). Conclusion Findings of this study suggested that patients with depression harbor multidimensional explanatory model. The levels of explanatory models are inversely associated with levels of medication adherence. PMID:28274025
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How Robust Is Linear Regression with Dummy Variables?
ERIC Educational Resources Information Center
Blankmeyer, Eric
2006-01-01
Researchers in education and the social sciences make extensive use of linear regression models in which the dependent variable is continuous-valued while the explanatory variables are a combination of continuous-valued regressors and dummy variables. The dummies partition the sample into groups, some of which may contain only a few observations.…
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The use of cognitive ability measures as explanatory variables in regression analysis
Junker, Brian; Schofield, Lynne Steuerle; Taylor, Lowell J
2015-01-01
Cognitive ability measures are often taken as explanatory variables in regression analysis, e.g., as a factor affecting a market outcome such as an individual’s wage, or a decision such as an individual’s education acquisition. Cognitive ability is a latent construct; its true value is unobserved. Nonetheless, researchers often assume that a test score, constructed via standard psychometric practice from individuals’ responses to test items, can be safely used in regression analysis. We examine problems that can arise, and suggest that an alternative approach, a “mixed effects structural equations” (MESE) model, may be more appropriate in many circumstances. PMID:26998417
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Bias, Confounding, and Interaction: Lions and Tigers, and Bears, Oh My!
Vetter, Thomas R; Mascha, Edward J
2017-09-01
Epidemiologists seek to make a valid inference about the causal effect between an exposure and a disease in a specific population, using representative sample data from a specific population. Clinical researchers likewise seek to make a valid inference about the association between an intervention and outcome(s) in a specific population, based upon their randomly collected, representative sample data. Both do so by using the available data about the sample variable to make a valid estimate about its corresponding or underlying, but unknown population parameter. Random error in an experiment can be due to the natural, periodic fluctuation or variation in the accuracy or precision of virtually any data sampling technique or health measurement tool or scale. In a clinical research study, random error can be due to not only innate human variability but also purely chance. Systematic error in an experiment arises from an innate flaw in the data sampling technique or measurement instrument. In the clinical research setting, systematic error is more commonly referred to as systematic bias. The most commonly encountered types of bias in anesthesia, perioperative, critical care, and pain medicine research include recall bias, observational bias (Hawthorne effect), attrition bias, misclassification or informational bias, and selection bias. A confounding variable is a factor associated with both the exposure of interest and the outcome of interest. A confounding variable (confounding factor or confounder) is a variable that correlates (positively or negatively) with both the exposure and outcome. Confounding is typically not an issue in a randomized trial because the randomized groups are sufficiently balanced on all potential confounding variables, both observed and nonobserved. However, confounding can be a major problem with any observational (nonrandomized) study. Ignoring confounding in an observational study will often result in a "distorted" or incorrect estimate of the association or treatment effect. Interaction among variables, also known as effect modification, exists when the effect of 1 explanatory variable on the outcome depends on the particular level or value of another explanatory variable. Bias and confounding are common potential explanations for statistically significant associations between exposure and outcome when the true relationship is noncausal. Understanding interactions is vital to proper interpretation of treatment effects. These complex concepts should be consistently and appropriately considered whenever one is not only designing but also analyzing and interpreting data from a randomized trial or observational study.
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A FORTRAN program for multivariate survival analysis on the personal computer.
Mulder, P G
1988-01-01
In this paper a FORTRAN program is presented for multivariate survival or life table regression analysis in a competing risks' situation. The relevant failure rate (for example, a particular disease or mortality rate) is modelled as a log-linear function of a vector of (possibly time-dependent) explanatory variables. The explanatory variables may also include the variable time itself, which is useful for parameterizing piecewise exponential time-to-failure distributions in a Gompertz-like or Weibull-like way as a more efficient alternative to Cox's proportional hazards model. Maximum likelihood estimates of the coefficients of the log-linear relationship are obtained from the iterative Newton-Raphson method. The program runs on a personal computer under DOS; running time is quite acceptable, even for large samples.
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Generic Feature Selection with Short Fat Data
Clarke, B.; Chu, J.-H.
2014-01-01
SUMMARY Consider a regression problem in which there are many more explanatory variables than data points, i.e., p ≫ n. Essentially, without reducing the number of variables inference is impossible. So, we group the p explanatory variables into blocks by clustering, evaluate statistics on the blocks and then regress the response on these statistics under a penalized error criterion to obtain estimates of the regression coefficients. We examine the performance of this approach for a variety of choices of n, p, classes of statistics, clustering algorithms, penalty terms, and data types. When n is not large, the discrimination over number of statistics is weak, but computations suggest regressing on approximately [n/K] statistics where K is the number of blocks formed by a clustering algorithm. Small deviations from this are observed when the blocks of variables are of very different sizes. Larger deviations are observed when the penalty term is an Lq norm with high enough q. PMID:25346546
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Prediction equations of forced oscillation technique: the insidious role of collinearity.
Narchi, Hassib; AlBlooshi, Afaf
2018-03-27
Many studies have reported reference data for forced oscillation technique (FOT) in healthy children. The prediction equation of FOT parameters were derived from a multivariable regression model examining the effect of age, gender, weight and height on each parameter. As many of these variables are likely to be correlated, collinearity might have affected the accuracy of the model, potentially resulting in misleading, erroneous or difficult to interpret conclusions.The aim of this work was: To review all FOT publications in children since 2005 to analyze whether collinearity was considered in the construction of the published prediction equations. Then to compare these prediction equations with our own study. And to analyse, in our study, how collinearity between the explanatory variables might affect the predicted equations if it was not considered in the model. The results showed that none of the ten reviewed studies had stated whether collinearity was checked for. Half of the reports had also included in their equations variables which are physiologically correlated, such as age, weight and height. The predicted resistance varied by up to 28% amongst these studies. And in our study, multicollinearity was identified between the explanatory variables initially considered for the regression model (age, weight and height). Ignoring it would have resulted in inaccuracies in the coefficients of the equation, their signs (positive or negative), their 95% confidence intervals, their significance level and the model goodness of fit. In Conclusion with inaccurately constructed and improperly reported models, understanding the results and reproducing the models for future research might be compromised.
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Case, Bradley S; Buckley, Hannah L
2015-01-01
Although treeline elevations are limited globally by growing season temperature, at regional scales treelines frequently deviate below their climatic limit. The cause of these deviations relate to a host of climatic, disturbance, and geomorphic factors that operate at multiple scales. The ability to disentangle the relative effects of these factors is currently hampered by the lack of reliable topoclimatic data, which describe how regional climatic characteristics are modified by topographic effects in mountain areas. In this study we present an analysis of the combined effects of local- and regional-scale factors on southern beech treeline elevation variability at 28 study areas across New Zealand. We apply a mesoscale atmospheric model to generate local-scale (200 m) meteorological data at these treelines and, from these data, we derive a set of topoclimatic indices that reflect possible detrimental and ameliorative influences on tree physiological functioning. Principal components analysis of meteorological data revealed geographic structure in how study areas were situated in multivariate space along gradients of topoclimate. Random forest and conditional inference tree modelling enabled us to tease apart the relative effects of 17 explanatory factors on local-scale treeline elevation variability. Overall, modelling explained about 50% of the variation in treeline elevation variability across the 28 study areas, with local landform and topoclimatic effects generally outweighing those from regional-scale factors across the 28 study areas. Further, the nature of the relationships between treeline elevation variability and the explanatory variables were complex, frequently non-linear, and consistent with the treeline literature. To our knowledge, this is the first study where model-generated meteorological data, and derived topoclimatic indices, have been developed and applied to explain treeline variation. Our results demonstrate the potential of such an approach for ecological research in mountainous environments.
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Buckley, Hannah L.
2015-01-01
Although treeline elevations are limited globally by growing season temperature, at regional scales treelines frequently deviate below their climatic limit. The cause of these deviations relate to a host of climatic, disturbance, and geomorphic factors that operate at multiple scales. The ability to disentangle the relative effects of these factors is currently hampered by the lack of reliable topoclimatic data, which describe how regional climatic characteristics are modified by topographic effects in mountain areas. In this study we present an analysis of the combined effects of local- and regional-scale factors on southern beech treeline elevation variability at 28 study areas across New Zealand. We apply a mesoscale atmospheric model to generate local-scale (200 m) meteorological data at these treelines and, from these data, we derive a set of topoclimatic indices that reflect possible detrimental and ameliorative influences on tree physiological functioning. Principal components analysis of meteorological data revealed geographic structure in how study areas were situated in multivariate space along gradients of topoclimate. Random forest and conditional inference tree modelling enabled us to tease apart the relative effects of 17 explanatory factors on local-scale treeline elevation variability. Overall, modelling explained about 50% of the variation in treeline elevation variability across the 28 study areas, with local landform and topoclimatic effects generally outweighing those from regional-scale factors across the 28 study areas. Further, the nature of the relationships between treeline elevation variability and the explanatory variables were complex, frequently non-linear, and consistent with the treeline literature. To our knowledge, this is the first study where model-generated meteorological data, and derived topoclimatic indices, have been developed and applied to explain treeline variation. Our results demonstrate the potential of such an approach for ecological research in mountainous environments. PMID:26528407
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NASA Astrophysics Data System (ADS)
Nanus, L.; Clow, D. W.; Sickman, J. O.
2016-12-01
High-elevation aquatic ecosystems in Yosemite (YOSE) and Sequoia and Kings Canyon (SEKI) National Parks are impacted by atmospheric nitrogen (N) deposition associated with local and regional air pollution. Documented effects include elevated surface water nitrate concentrations, increased algal productivity, and changes in diatom species assemblages. Annual wet inorganic N deposition maps, developed at 1-km resolution for YOSE and SEKI to quantify N deposition to sensitive high-elevation ecosystems, range from 1.0 to over 5.0 kg N ha-1 yr-1. Critical loads of N deposition for nutrient enrichment of aquatic ecosystems were quantified and mapped using a geostatistical approach, with N deposition, topography, vegetation, geology, and climate as potential explanatory variables. Multiple predictive models were created using various combinations of explanatory variables; this approach allowed us to better quantify uncertainty and more accurately identify the areas most sensitive to atmospherically deposited N. The lowest critical loads estimates and highest exceedances identified within YOSE and SEKI occurred in high-elevation basins with steep slopes, sparse vegetation, and areas of neoglacial till and talus. These results are consistent with previous analyses in the Rocky Mountains, and highlight the sensitivity of alpine ecosystems to atmospheric N deposition.
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Exhaustive Search for Sparse Variable Selection in Linear Regression
NASA Astrophysics Data System (ADS)
Igarashi, Yasuhiko; Takenaka, Hikaru; Nakanishi-Ohno, Yoshinori; Uemura, Makoto; Ikeda, Shiro; Okada, Masato
2018-04-01
We propose a K-sparse exhaustive search (ES-K) method and a K-sparse approximate exhaustive search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, we found the difficulty to determine K from the data. Using virtual measurement and analysis, we argue that this is caused by data shortage.
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Incomes, Attitudes, and Occurrences of Invasive Species: An Application to Signal Crayfish in Sweden
NASA Astrophysics Data System (ADS)
Gren, Ing-Marie; Campos, Monica; Edsman, Lennart; Bohman, Patrik
2009-02-01
This article analyzes and carries out an econometric test of the explanatory power of economic and attitude variables for occurrences of the nonnative signal crayfish in Swedish waters. Signal crayfish are a carrier of plague which threatens the native noble crayfish with extinction. Crayfish are associated with recreational and cultural traditions in Sweden, which may run against environmental preferences for preserving native species. Econometric analysis is carried out using panel data at the municipality level with economic factors and attitudes as explanatory variables, which are derived from a simple dynamic harvesting model. A log-normal model is used for the regression analysis, and the results indicate significant impacts on occurrences of waters with signal crayfish of changes in both economic and attitude variables. Variables reflecting environmental and recreational preferences have unexpected signs, where the former variable has a positive and the latter a negative impact on occurrences of waters with signal crayfish. These effects are, however, counteracted by their respective interaction effect with income.
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NASA Astrophysics Data System (ADS)
Smith, Tony E.; Lee, Ka Lok
2012-01-01
There is a common belief that the presence of residual spatial autocorrelation in ordinary least squares (OLS) regression leads to inflated significance levels in beta coefficients and, in particular, inflated levels relative to the more efficient spatial error model (SEM). However, our simulations show that this is not always the case. Hence, the purpose of this paper is to examine this question from a geometric viewpoint. The key idea is to characterize the OLS test statistic in terms of angle cosines and examine the geometric implications of this characterization. Our first result is to show that if the explanatory variables in the regression exhibit no spatial autocorrelation, then the distribution of test statistics for individual beta coefficients in OLS is independent of any spatial autocorrelation in the error term. Hence, inferences about betas exhibit all the optimality properties of the classic uncorrelated error case. However, a second more important series of results show that if spatial autocorrelation is present in both the dependent and explanatory variables, then the conventional wisdom is correct. In particular, even when an explanatory variable is statistically independent of the dependent variable, such joint spatial dependencies tend to produce "spurious correlation" that results in over-rejection of the null hypothesis. The underlying geometric nature of this problem is clarified by illustrative examples. The paper concludes with a brief discussion of some possible remedies for this problem.
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NASA Astrophysics Data System (ADS)
Carisi, Francesca; Domeneghetti, Alessio; Kreibich, Heidi; Schröter, Kai; Castellarin, Attilio
2017-04-01
Flood risk is function of flood hazard and vulnerability, therefore its accurate assessment depends on a reliable quantification of both factors. The scientific literature proposes a number of objective and reliable methods for assessing flood hazard, yet it highlights a limited understanding of the fundamental damage processes. Loss modelling is associated with large uncertainty which is, among other factors, due to a lack of standard procedures; for instance, flood losses are often estimated based on damage models derived in completely different contexts (i.e. different countries or geographical regions) without checking its applicability, or by considering only one explanatory variable (i.e. typically water depth). We consider the Secchia river flood event of January 2014, when a sudden levee-breach caused the inundation of nearly 200 km2 in Northern Italy. In the aftermath of this event, local authorities collected flood loss data, together with additional information on affected private households and industrial activities (e.g. buildings surface and economic value, number of company's employees and others). Based on these data we implemented and compared a quadratic-regression damage function, with water depth as the only explanatory variable, and a multi-variable model that combines multiple regression trees and considers several explanatory variables (i.e. bagging decision trees). Our results show the importance of data collection revealing that (1) a simple quadratic regression damage function based on empirical data from the study area can be significantly more accurate than literature damage-models derived for a different context and (2) multi-variable modelling may outperform the uni-variable approach, yet it is more difficult to develop and apply due to a much higher demand of detailed data.
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Explaining the sense of family coherence among husbands and wives: the Israeli case.
Kulik, Liat
2009-12-01
This study examined variables belonging to the family environment that explain the sense of family coherence among husbands (n = 133) and wives (n = 133) in Israel. Specifically, the explanatory variables tested were spousal power relations (as expressed in equality in the division of household labor and decision making), and perceived family conflict. In general, the sense of family coherence among spouses was found to be high. Perceived family conflict contributed to explaining the sense of family coherence for both husbands and wives. Equality in the division of household labor and in decision making had a greater impact on husbands than wives. Family coherence correlated negatively with age for husbands and positively with income for wives. The explanatory variables had a greater impact on the sense of family coherence among husbands than among wives.
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Avoiding and Correcting Bias in Score-Based Latent Variable Regression with Discrete Manifest Items
ERIC Educational Resources Information Center
Lu, Irene R. R.; Thomas, D. Roland
2008-01-01
This article considers models involving a single structural equation with latent explanatory and/or latent dependent variables where discrete items are used to measure the latent variables. Our primary focus is the use of scores as proxies for the latent variables and carrying out ordinary least squares (OLS) regression on such scores to estimate…
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The islands are different: human perceptions of game species in Hawaii.
Lohr, Cheryl A; Lepczyk, Christopher A; Johnson, Edwin D
2014-10-01
Hawaii's game animals are all non-native species, which provokes human-wildlife conflict among stakeholders. The management of human-wildlife conflict in Hawaii is further complicated by the discrete nature of island communities. Our goal was to understand the desires and perceived values or impacts of game held by residents of Hawaii regarding six game species [pigs (Sus scrofa), goats (Capra hircus), mouflon (Ovis musimon), axis deer (Axis axis), turkeys (Melagris gallopavo), and doves (Geopelia striata)]. We measured the desired abundance of game on the six main Hawaiian Islands using the potential for conflict index and identified explanatory variables for those desires via recursive partitioning. In 2011 we surveyed 5,407 residents (2,360 random residents and 3,047 pre-identified stakeholders). Overall 54.5 and 27.6 % of the emailed and mailed surveys were returned (n = 1,510). A non-respondent survey revealed that respondents and non-respondents had similar interest in wildlife, and a similar education level. The desired abundance of game differed significantly among stakeholders, species, and islands. The desired abundance scores were higher for axis deer, mouflon, and turkeys compared to pigs, goats or doves. Enjoyment at seeing game and the cultural value of game were widespread explanatory variables for desired abundance. Models for Lanai emphasized the economic value of game, whereas models for Maui identified the potential for game to contaminate soil and water. Models for Oahu and Kauai revealed concern for human health and safety. Given our findings we recommend managers design separate management plans for each island taking into consideration the values of residents.
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The Islands Are Different: Human Perceptions of Game Species in Hawaii
NASA Astrophysics Data System (ADS)
Lohr, Cheryl A.; Lepczyk, Christopher A.; Johnson, Edwin D.
2014-10-01
Hawaii's game animals are all non-native species, which provokes human-wildlife conflict among stakeholders. The management of human-wildlife conflict in Hawaii is further complicated by the discrete nature of island communities. Our goal was to understand the desires and perceived values or impacts of game held by residents of Hawaii regarding six game species [pigs ( Sus scrofa), goats ( Capra hircus), mouflon ( Ovis musimon), axis deer ( Axis axis), turkeys ( Melagris gallopavo), and doves ( Geopelia striata)]. We measured the desired abundance of game on the six main Hawaiian Islands using the potential for conflict index and identified explanatory variables for those desires via recursive partitioning. In 2011 we surveyed 5,407 residents (2,360 random residents and 3,047 pre-identified stakeholders). Overall 54.5 and 27.6 % of the emailed and mailed surveys were returned ( n = 1,510). A non-respondent survey revealed that respondents and non-respondents had similar interest in wildlife, and a similar education level. The desired abundance of game differed significantly among stakeholders, species, and islands. The desired abundance scores were higher for axis deer, mouflon, and turkeys compared to pigs, goats or doves. Enjoyment at seeing game and the cultural value of game were widespread explanatory variables for desired abundance. Models for Lanai emphasized the economic value of game, whereas models for Maui identified the potential for game to contaminate soil and water. Models for Oahu and Kauai revealed concern for human health and safety. Given our findings we recommend managers design separate management plans for each island taking into consideration the values of residents.
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'Food Sticking in My Throat': Videofluoroscopic Evaluation of a Common Symptom.
Madhavan, Aarthi; Carnaby, Giselle D; Crary, Michael A
2015-06-01
Prevalence of the symptom of food 'sticking' during swallowing has been reported to range from 5 to 50%, depending on the assessment setting. However, limited objective evidence has emerged to clarify factors that contribute to this symptom. Three hundred and fifteen patient records from an outpatient dysphagia clinic were reviewed to identify patients with symptoms of 'food sticking in the throat.' Corresponding videofluoroscopic swallowing studies for patients with this complaint were reviewed for the following variables: accuracy of symptom localization, identification and characteristics (anatomic, physiologic) of an explanatory cause for the symptom, and the specific swallowed material that identified the explanatory cause. One hundred and forty one patients (45%) were identified with a complaint of food 'sticking' in their throat during swallowing. Prevalence of explanatory findings on fluoroscopy was 76% (107/141). Eighty five percent (91/107) of explanatory causes were physiologic in nature, while 15% (16/107) were anatomic. The majority of explanatory causes were identified in the esophagus (71%). Symptom localization was more accurate when the explanatory cause was anatomic versus physiologic (75 vs. 18%). A non-masticated marshmallow presented with the highest diagnostic yield in identification of explanatory causes (71%). Patients complaining of 'food sticking in the throat' are likely to present with esophageal irregularities. Thus, imaging studies of swallowing function should include the esophagus. A range of materials, including a non-masticated marshmallow, is helpful in determining the location and characteristics of swallowing deficits contributing to this symptom.
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A multimodal dataset for various forms of distracted driving
Taamneh, Salah; Tsiamyrtzis, Panagiotis; Dcosta, Malcolm; Buddharaju, Pradeep; Khatri, Ashik; Manser, Michael; Ferris, Thomas; Wunderlich, Robert; Pavlidis, Ioannis
2017-01-01
We describe a multimodal dataset acquired in a controlled experiment on a driving simulator. The set includes data for n=68 volunteers that drove the same highway under four different conditions: No distraction, cognitive distraction, emotional distraction, and sensorimotor distraction. The experiment closed with a special driving session, where all subjects experienced a startle stimulus in the form of unintended acceleration—half of them under a mixed distraction, and the other half in the absence of a distraction. During the experimental drives key response variables and several explanatory variables were continuously recorded. The response variables included speed, acceleration, brake force, steering, and lane position signals, while the explanatory variables included perinasal electrodermal activity (EDA), palm EDA, heart rate, breathing rate, and facial expression signals; biographical and psychometric covariates as well as eye tracking data were also obtained. This dataset enables research into driving behaviors under neatly abstracted distracting stressors, which account for many car crashes. The set can also be used in physiological channel benchmarking and multispectral face recognition. PMID:28809848
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Computation of Standard Errors
Dowd, Bryan E; Greene, William H; Norton, Edward C
2014-01-01
Objectives We discuss the problem of computing the standard errors of functions involving estimated parameters and provide the relevant computer code for three different computational approaches using two popular computer packages. Study Design We show how to compute the standard errors of several functions of interest: the predicted value of the dependent variable for a particular subject, and the effect of a change in an explanatory variable on the predicted value of the dependent variable for an individual subject and average effect for a sample of subjects. Empirical Application Using a publicly available dataset, we explain three different methods of computing standard errors: the delta method, Krinsky–Robb, and bootstrapping. We provide computer code for Stata 12 and LIMDEP 10/NLOGIT 5. Conclusions In most applications, choice of the computational method for standard errors of functions of estimated parameters is a matter of convenience. However, when computing standard errors of the sample average of functions that involve both estimated parameters and nonstochastic explanatory variables, it is important to consider the sources of variation in the function's values. PMID:24800304
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A comparison of data-driven groundwater vulnerability assessment methods
Sorichetta, Alessandro; Ballabio, Cristiano; Masetti, Marco; Robinson, Gilpin R.; Sterlacchini, Simone
2013-01-01
Increasing availability of geo-environmental data has promoted the use of statistical methods to assess groundwater vulnerability. Nitrate is a widespread anthropogenic contaminant in groundwater and its occurrence can be used to identify aquifer settings vulnerable to contamination. In this study, multivariate Weights of Evidence (WofE) and Logistic Regression (LR) methods, where the response variable is binary, were used to evaluate the role and importance of a number of explanatory variables associated with nitrate sources and occurrence in groundwater in the Milan District (central part of the Po Plain, Italy). The results of these models have been used to map the spatial variation of groundwater vulnerability to nitrate in the region, and we compare the similarities and differences of their spatial patterns and associated explanatory variables. We modify the standard WofE method used in previous groundwater vulnerability studies to a form analogous to that used in LR; this provides a framework to compare the results of both models and reduces the effect of sampling bias on the results of the standard WofE model. In addition, a nonlinear Generalized Additive Model has been used to extend the LR analysis. Both approaches improved discrimination of the standard WofE and LR models, as measured by the c-statistic. Groundwater vulnerability probability outputs, based on rank-order classification of the respective model results, were similar in spatial patterns and identified similar strong explanatory variables associated with nitrate source (population density as a proxy for sewage systems and septic sources) and nitrate occurrence (groundwater depth).
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Regression Analysis of Stage Variability for West-Central Florida Lakes
Sacks, Laura A.; Ellison, Donald L.; Swancar, Amy
2008-01-01
The variability in a lake's stage depends upon many factors, including surface-water flows, meteorological conditions, and hydrogeologic characteristics near the lake. An understanding of the factors controlling lake-stage variability for a population of lakes may be helpful to water managers who set regulatory levels for lakes. The goal of this study is to determine whether lake-stage variability can be predicted using multiple linear regression and readily available lake and basin characteristics defined for each lake. Regressions were evaluated for a recent 10-year period (1996-2005) and for a historical 10-year period (1954-63). Ground-water pumping is considered to have affected stage at many of the 98 lakes included in the recent period analysis, and not to have affected stage at the 20 lakes included in the historical period analysis. For the recent period, regression models had coefficients of determination (R2) values ranging from 0.60 to 0.74, and up to five explanatory variables. Standard errors ranged from 21 to 37 percent of the average stage variability. Net leakage was the most important explanatory variable in regressions describing the full range and low range in stage variability for the recent period. The most important explanatory variable in the model predicting the high range in stage variability was the height over median lake stage at which surface-water outflow would occur. Other explanatory variables in final regression models for the recent period included the range in annual rainfall for the period and several variables related to local and regional hydrogeology: (1) ground-water pumping within 1 mile of each lake, (2) the amount of ground-water inflow (by category), (3) the head gradient between the lake and the Upper Floridan aquifer, and (4) the thickness of the intermediate confining unit. Many of the variables in final regression models are related to hydrogeologic characteristics, underscoring the importance of ground-water exchange in controlling the stage of karst lakes in Florida. Regression equations were used to predict lake-stage variability for the recent period for 12 additional lakes, and the median difference between predicted and observed values ranged from 11 to 23 percent. Coefficients of determination for the historical period were considerably lower (maximum R2 of 0.28) than for the recent period. Reasons for these low R2 values are probably related to the small number of lakes (20) with stage data for an equivalent time period that were unaffected by ground-water pumping, the similarity of many of the lake types (large surface-water drainage lakes), and the greater uncertainty in defining historical basin characteristics. The lack of lake-stage data unaffected by ground-water pumping and the poor regression results obtained for that group of lakes limit the ability to predict natural lake-stage variability using this method in west-central Florida.
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Relations among Functional Systems in Behavior Analysis
Thompson, Travis
2007-01-01
This paper proposes that an organism's integrated repertoire of operant behavior has the status of a biological system, similar to other biological systems, like the nervous, cardiovascular, or immune systems. Evidence from a number of sources indicates that the distinctions between biological and behavioral events is often misleading, engendering counterproductive explanatory controversy. A good deal of what is viewed as biological (often thought to be inaccessible or hypothetical) can become publicly measurable variables using currently available and developing technologies. Moreover, such endogenous variables can serve as establishing operations, discriminative stimuli, conjoint mediating events, and maintaining consequences within a functional analysis of behavior and need not lead to reductionistic explanation. I suggest that explanatory misunderstandings often arise from conflating different levels of analysis and that behavior analysis can extend its reach by identifying variables operating within a functional analysis that also serve functions in other biological systems. PMID:17575907
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Spatial generalised linear mixed models based on distances.
Melo, Oscar O; Mateu, Jorge; Melo, Carlos E
2016-10-01
Risk models derived from environmental data have been widely shown to be effective in delineating geographical areas of risk because they are intuitively easy to understand. We present a new method based on distances, which allows the modelling of continuous and non-continuous random variables through distance-based spatial generalised linear mixed models. The parameters are estimated using Markov chain Monte Carlo maximum likelihood, which is a feasible and a useful technique. The proposed method depends on a detrending step built from continuous or categorical explanatory variables, or a mixture among them, by using an appropriate Euclidean distance. The method is illustrated through the analysis of the variation in the prevalence of Loa loa among a sample of village residents in Cameroon, where the explanatory variables included elevation, together with maximum normalised-difference vegetation index and the standard deviation of normalised-difference vegetation index calculated from repeated satellite scans over time. © The Author(s) 2013.
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Introduction to the use of regression models in epidemiology.
Bender, Ralf
2009-01-01
Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.
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Morrison, John A; Glueck, Charles J; Daniels, Stephen; Wang, Ping; Stroop, Davis
2011-09-01
We hypothesized that adolescent oligomenorrhea (ages 14-19) would independently predict impaired fasting glucose (IFG; ≥110 to <126 mg/dL) plus type 2 diabetes mellitus (T2DM; ≥126 mg/dL), insulin and glucose levels, and insulin resistance (IR) in young adulthood (ages 19-25). A prospective 15-year follow-up of 370 schoolgirls starting at age 10 was performed. Age 14 waist circumference was the most important explanatory variable for IFG + T2DM during ages 19 to 24 (P = .002; odds ratio, 1.06; 95% confidence interval, 1.02-1.10), along with oligomenorrhea category from ages 14 to 19 (0, 1, 2, ≥3 reports over 6 years; P = .032; odds ratio, 1.82; 95% confidence interval, 1.05-3.14). Impaired fasting glucose + T2DM at ages 19 to 24 were more common in girls having 1 (6%), 2 (11%), and ≥3 (38%) oligomenorrhea reports from ages 14 to 19 than in girls without oligomenorrhea (3%; P = .0003). Positive explanatory variables (all Ps ≤ .05) for homeostasis model assessment of IR at ages 19 to 24 included age 14 waist (partial R(2) = 30.1%), oligomenorrhea with hyperandrogenism (polycystic ovary syndrome; partial R(2) = 4.1%), black race (3.8%), and oligomenorrhea frequency during ages 14 to 19 (0.8%); sex hormone binding globulin was a negative explanatory variable (0.7%). This is the first prospective study to report an independent association of adolescent oligomenorrhea with young adult IFG + T2DM, with insulin and glucose levels, and with IR. Age 14 waist circumference, oligomenorrhea with hyperandrogenism (polycystic ovary syndrome), black race, oligomenorrhea frequency at ages 14 to 19, and age 14 sex hormone binding globulin were independently associated with IR at ages 19 to 24, potentially facilitating primary prevention of IFG, T2DM, and hyperinsulinemia. Copyright © 2011 Elsevier Inc. All rights reserved.
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Insight in psychosis: Standards, science, ethics and value judgment.
Jacob, K S
2017-06-01
The clinical assessment of insight solely employs biomedical perspectives and criteria to the complete exclusion of context and culture and to the disregard of values and value judgments. The aim of this discussion article is to examine recent research from India on insight and explanatory models in psychosis and re-examine the framework of assessment, diagnosis and management of insight and explanatory models. Recent research from India on insight in psychosis and explanatory models is reviewed. Recent research, which has used longitudinal data and adjusted for pretreatment variables, suggests that insight and explanatory models of illness at baseline do not predict course, outcome and treatment response in schizophrenia, which seem to be dependent on the severity and quality of the psychosis. It supports the view that people with psychosis simultaneously hold multiple and contradictory explanatory models of illness, which change over time and with the trajectory of the illness. It suggests that insight, like all explanatory models, is a narrative of the person's reality and a coping strategy to handle with the varied impact of the illness. This article argues that the assessment of insight necessarily involves value entailments, commitments and consequences. It supports a need for a broad-based approach to assess awareness, attribution and action related to mental illness and to acknowledge the role of values and value judgment in the evaluation of insight in psychosis.
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Dong, Chunjiao; Xie, Kun; Zeng, Jin; Li, Xia
2018-04-01
Highway safety laws aim to influence driver behaviors so as to reduce the frequency and severity of crashes, and their outcomes. For one specific highway safety law, it would have different effects on the crashes across severities. Understanding such effects can help policy makers upgrade current laws and hence improve traffic safety. To investigate the effects of highway safety laws on crashes across severities, multivariate models are needed to account for the interdependency issues in crash counts across severities. Based on the characteristics of the dependent variables, multivariate dynamic Tobit (MVDT) models are proposed to analyze crash counts that are aggregated at the state level. Lagged observed dependent variables are incorporated into the MVDT models to account for potential temporal correlation issues in crash data. The state highway safety law related factors are used as the explanatory variables and socio-demographic and traffic factors are used as the control variables. Three models, a MVDT model with lagged observed dependent variables, a MVDT model with unobserved random variables, and a multivariate static Tobit (MVST) model are developed and compared. The results show that among the investigated models, the MVDT models with lagged observed dependent variables have the best goodness-of-fit. The findings indicate that, compared to the MVST, the MVDT models have better explanatory power and prediction accuracy. The MVDT model with lagged observed variables can better handle the stochasticity and dependency in the temporal evolution of the crash counts and the estimated values from the model are closer to the observed values. The results show that more lives could be saved if law enforcement agencies can make a sustained effort to educate the public about the importance of motorcyclists wearing helmets. Motor vehicle crash-related deaths, injuries, and property damages could be reduced if states enact laws for stricter text messaging rules, higher speeding fines, older licensing age, and stronger graduated licensing provisions. Injury and PDO crashes would be significantly reduced with stricter laws prohibiting the use of hand-held communication devices and higher fines for drunk driving. Copyright © 2018 Elsevier Ltd. All rights reserved.
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EXPLAINING THE GAP IN ANTENATAL CARE SERVICE UTILIZATION BETWEEN YOUNGER AND OLDER MOTHERS IN GHANA.
Boamah, Sheila A; Amoyaw, Jonathan; Luginaah, Isaac
2016-05-01
Over two-thirds of pregnant women (69%) have at least one antenatal care (ANC) coverage contact in sub-Saharan Africa. However, to achieve the full life-saving potential that ANC promises for women and babies, a nuanced understanding of age-specific gaps in utilization of ANC services is required. Using the 2008 Ghana Demographic and Health Survey of 1456 individuals, this study examined the disparities in the use of ANC services between younger and older mothers by applying four counterfactual decomposition techniques. The results show that cross-group differences in the explanatory variables largely account for the differentials in ANC service utilization between younger and older mothers. Birth order (parity) accounts for the largest share of the contribution to the overall explained gap in ANC utilization between the younger and older mothers, suggesting that ANC differentials between the two groups are probably due to biosocial factors. To a lesser extent, wealth status of the two groups also contributes to the overall explained gap in ANC service utilization. The policy implications of these findings are that in order to bridge the ANC service utilization gap between the two groups, policymakers must systematically address gaps in cross-group differences in the explanatory variables in order to increase the utilization of ANC to attain the minimum recommendation of four visits as per World Health Organization guidelines.
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Correlates of compliance with national comprehensive smoke-free laws.
Peruga, Armando; Hayes, Luminita S; Aguilera, Ximena; Prasad, Vinayak; Bettcher, Douglas W
2017-12-05
To explore correlates of high compliance with smoking bans in a cross-sectional data set from the 41 countries with national comprehensive smoke-free laws in 2014 and complete data on compliance and enforcement. Outcome variable: compliance with a national comprehensive smoke-free law in each country was obtained for 2014 from the WHO global report on the global tobacco epidemic. Explanatory variables: legal enforcement requirements, penalties, infrastructure and strategy were obtained through a separate survey of governments. Also, country socioeconomic and demographic characteristics including the level of corruption control were included. an initial bivariate analysis determined the significance of each potentially relevant explanatory variable of high compliance. Differences in compliance were tested using the exact logistic regression. High compliance with the national comprehensive smoke-free law was associated with the involvement of the local jurisdictions in providing training and/or guidance for inspections (OR=10.3, 95% CI 1.7 to 117.7) and a perception of high corruption control efforts in the country (OR=7.2, 95% CI 1.1 to 85.8). The results show the importance of the depth of the enforcement infrastructure and effort represented by the degree to which the local government is involved in enforcement. They also show the significance of fighting corruption in the enforcement process, including the attempts of the tobacco industry to undermine the process, to achieve high levels of compliance with the law. The results point out to the need to invest minimal but essential enforcement resources given that national comprehensive smoke-free laws are self-enforcing in many but not all countries and sectors.
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Deng, Yanhe; Yan, Mengge; Chen, Henry; Sun, Xin; Zhang, Peng; Zeng, Xianglong; Liu, Xiangping; Lye, Yue
2016-01-01
Highly optimistic explanatory style (HOES) and highly pessimistic explanatory style (HPES) are two maladaptive ways to explain the world and may have roots in attachment insecurity. The current study aims to explore the effects of security priming - activating supportive representations of attachment security - on ameliorating these maladaptive explanatory styles. 57 participants with HOES and 57 participants with HPES were randomized into security priming and control conditions. Their scores of overall optimistic attribution were measured before and after priming. Security priming had a moderating effect: the security primed HOES group exhibited lower optimistic attribution, while the security primed HPES group evinced higher scores of optimistic attribution. Furthermore, the security primed HOES group attributed positive outcomes more externally, while the security primed HPES group attributed successful results more internally. The results support the application of security priming interventions on maladaptive explanatory styles. Its potential mechanism and directions for future study are also discussed.
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Stackelberg, Paul E.; Barbash, Jack E.; Gilliom, Robert J.; Stone, Wesley W.; Wolock, David M.
2012-01-01
Tobit regression models were developed to predict the summed concentration of atrazine [6-chloro-N-ethyl-N'-(1-methylethyl)-1,3,5-triazine-2,4-diamine] and its degradate deethylatrazine [6-chloro-N-(1-methylethyl)-1,3,5,-triazine-2,4-diamine] (DEA) in shallow groundwater underlying agricultural settings across the conterminous United States. The models were developed from atrazine and DEA concentrations in samples from 1298 wells and explanatory variables that represent the source of atrazine and various aspects of the transport and fate of atrazine and DEA in the subsurface. One advantage of these newly developed models over previous national regression models is that they predict concentrations (rather than detection frequency), which can be compared with water quality benchmarks. Model results indicate that variability in the concentration of atrazine residues (atrazine plus DEA) in groundwater underlying agricultural areas is more strongly controlled by the history of atrazine use in relation to the timing of recharge (groundwater age) than by processes that control the dispersion, adsorption, or degradation of these compounds in the saturated zone. Current (1990s) atrazine use was found to be a weak explanatory variable, perhaps because it does not represent the use of atrazine at the time of recharge of the sampled groundwater and because the likelihood that these compounds will reach the water table is affected by other factors operating within the unsaturated zone, such as soil characteristics, artificial drainage, and water movement. Results show that only about 5% of agricultural areas have greater than a 10% probability of exceeding the USEPA maximum contaminant level of 3.0 μg L-1. These models are not developed for regulatory purposes but rather can be used to (i) identify areas of potential concern, (ii) provide conservative estimates of the concentrations of atrazine residues in deeper potential drinking water supplies, and (iii) set priorities among areas for future groundwater monitoring.
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Directional Dependence in Developmental Research
ERIC Educational Resources Information Center
von Eye, Alexander; DeShon, Richard P.
2012-01-01
In this article, we discuss and propose methods that may be of use to determine direction of dependence in non-normally distributed variables. First, it is shown that standard regression analysis is unable to distinguish between explanatory and response variables. Then, skewness and kurtosis are discussed as tools to assess deviation from…
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Pupil Control Ideology and the Salience of Teacher Characteristics
ERIC Educational Resources Information Center
Smyth, W. J.
1977-01-01
The explanatory power of the combined biographical variables of teacher age, experience, sex, organizational status, and academic qualifications for variances in pupil control ideology (PCI) is seriously questioned, since as little as 6 percent of PCI variance may be explained by reference to these particular variables. (Author)
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Determinants of Crime in Virginia: An Empirical Analysis
ERIC Educational Resources Information Center
Ali, Abdiweli M.; Peek, Willam
2009-01-01
This paper is an empirical analysis of the determinants of crime in Virginia. Over a dozen explanatory variables that current literature suggests as important determinants of crime are collected. The data is from 1970 to 2000. These include economic, fiscal, demographic, political, and social variables. The regression results indicate that crime…
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The use of generalised additive models (GAM) in dentistry.
Helfenstein, U; Steiner, M; Menghini, G
1997-12-01
Ordinary multiple regression and logistic multiple regression are widely applied statistical methods which allow a researcher to 'explain' or 'predict' a response variable from a set of explanatory variables or predictors. In these models it is usually assumed that quantitative predictors such as age enter linearly into the model. During recent years these methods have been further developed to allow more flexibility in the way explanatory variables 'act' on a response variable. The methods are called 'generalised additive models' (GAM). The rigid linear terms characterising the association between response and predictors are replaced in an optimal way by flexible curved functions of the predictors (the 'profiles'). Plotting the 'profiles' allows the researcher to visualise easily the shape by which predictors 'act' over the whole range of values. The method facilitates detection of particular shapes such as 'bumps', 'U-shapes', 'J-shapes, 'threshold values' etc. Information about the shape of the association is not revealed by traditional methods. The shapes of the profiles may be checked by performing a Monte Carlo simulation ('bootstrapping'). After the presentation of the GAM a relevant case study is presented in order to demonstrate application and use of the method. The dependence of caries in primary teeth on a set of explanatory variables is investigated. Since GAMs may not be easily accessible to dentists, this article presents them in an introductory condensed form. It was thought that a nonmathematical summary and a worked example might encourage readers to consider the methods described. GAMs may be of great value to dentists in allowing visualisation of the shape by which predictors 'act' and obtaining a better understanding of the complex relationships between predictors and response.
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Ledien, Julia; Sorn, Sopheak; Hem, Sopheak; Huy, Rekol; Buchy, Philippe
2017-01-01
Remote sensing can contribute to early warning for diseases with environmental drivers, such as flooding for leptospirosis. In this study we assessed whether and which remotely-sensed flooding indicator could be used in Cambodia to study any disease for which flooding has already been identified as an important driver, using leptospirosis as a case study. The performance of six potential flooding indicators was assessed by ground truthing. The Modified Normalized Difference Water Index (MNDWI) was used to estimate the Risk Ratio (RR) of being infected by leptospirosis when exposed to floods it detected, in particular during the rainy season. Chi-square tests were also calculated. Another variable—the time elapsed since the first flooding of the year—was created using MNDWI values and was also included as explanatory variable in a generalized linear model (GLM) and in a boosted regression tree model (BRT) of leptospirosis infections, along with other explanatory variables. Interestingly, MNDWI thresholds for both detecting water and predicting the risk of leptospirosis seroconversion were independently evaluated at -0.3. Value of MNDWI greater than -0.3 was significantly related to leptospirosis infection (RR = 1.61 [1.10–1.52]; χ2 = 5.64, p-value = 0.02, especially during the rainy season (RR = 2.03 [1.25–3.28]; χ2 = 8.15, p-value = 0.004). Time since the first flooding of the year was a significant risk factor in our GLM model (p-value = 0.042). These results suggest that MNDWI may be useful as a risk indicator in an early warning remote sensing tool for flood-driven diseases like leptospirosis in South East Asia. PMID:28704461
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de Jong, B; Meeder, A M; Koekkoek, K W A C; Schouten, M A; Westers, P; van Zanten, A R H
2018-07-01
Among patients admitted to European hospitals or intensive care units (ICUs), 5.7% and 19.5% will encounter healthcare-associated infections (HAIs), respectively, and antimicrobial resistance is emerging. As hospital surfaces are contaminated with potentially pathogenic bacteria, environmental cleanliness is an essential aspect to reduce HAIs. To address the efficacy of a titanium dioxide coating in reducing the microbial colonization of environmental surfaces in an ICU. A prospective, controlled, single-centre pilot study was conducted to examine the effect of a titanium dioxide coating on the microbial colonization of surfaces in an ICU. During the pre- and post-intervention periods, surfaces were cultured with agar contact plates (BBL RODAC plates). Factors that were potentially influencing the bacterial colonization of surfaces were recorded. A repeated measurements analysis within a hierarchic multi-level framework was used to analyse the effect of the intervention, controlling for the explanatory variables. The mean ratio for the total number of colony-forming units (cfus) in a room between the pre- and post-intervention periods was 0.86 (standard deviation 0.57). The optimal model included the following explanatory variables: intervention (P=0.065), week (P=0.002), culture surfaces (P<0.001), ICU room (P=0.039), and interaction between intervention and week (P=0.002) and between week and culture surfaces (P=0.031). The effect of the intervention on the number of cfus from all culture plates in Week 4 between the pre- and post-intervention periods was -0.47 (95% confidence interval -0.24 to - 0.70). This study found that a titanium dioxide coating had no effect on the microbial colonization of surfaces in an ICU. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.
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NASA Astrophysics Data System (ADS)
Mahmood, Ehab A.; Rana, Sohel; Hussin, Abdul Ghapor; Midi, Habshah
2016-06-01
The circular regression model may contain one or more data points which appear to be peculiar or inconsistent with the main part of the model. This may be occur due to recording errors, sudden short events, sampling under abnormal conditions etc. The existence of these data points "outliers" in the data set cause lot of problems in the research results and the conclusions. Therefore, we should identify them before applying statistical analysis. In this article, we aim to propose a statistic to identify outliers in the both of the response and explanatory variables of the simple circular regression model. Our proposed statistic is robust circular distance RCDxy and it is justified by the three robust measurements such as proportion of detection outliers, masking and swamping rates.
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Leboeuf-Yde, Charlotte; Kjaer, Per; Bendix, Tom; Manniche, Claus
2008-01-14
Recently, the MRI finding of "Modic changes" has been identified as pathologic spinal condition that probably reflects a vertebral inflammatory process (VIP), which coincides with spinal pain in most. We hypothesized that heavy smoking in combination with macro- or repeated microtrauma could lead to VIP. The objectives were to investigate if combinations of self-reported heavy smoking, hard physical work, and overweight would be more strongly linked with VIP than with other spinal conditions, such as degenerated discs and non-specific low back pain (LBP). Secondary analysis was made of a data base pertaining to a population-based cross-sectional study. A population-generated cohort of 412 40-yr old Danes provided questionnaire information on smoking, weight, height, type of work, and LBP. MRI was used to determine the presence/absence of disc degeneration and of VIP. Associations were tested between three explanatory variables (type of work, smoking, and body mass index) and four outcome variables (LBP in the past year, more persistent LBP in the past year, disc degeneration, and VIP). Associations with these four outcome variables were studied for each single explanatory variable and for combinations of two at a time, and, finally, in a multivariable analysis including all three explanatory variables. There were no significant associations between the single explanatory variables and the two pain variables or with disc degeneration. However, VIP was found in 15% of non-smokers vs. 26% of heavy smokers. Similarly, VIP was noted in 11% of those in sedentary jobs vs. 31% of those with hard physical work. Further, the prevalence of VIP in those, who neither smoked heavily nor had a hard physical job was 13%, 25% in those who either smoked heavily or had a hard physical job, and 41% in those who both smoked heavily and worked hard. The odds ratio was 4.9 (1.6-13.0) for those who were both heavy smokers and had a hard physical job as compared to those who were classified as "neither". Similar but weaker findings were noted for the combination of overweight and hard physical work but not for the combination of smoking and overweight. Hard physical work in combination with either heavy smoking or overweight is strongly associated with VIP. If this finding can be reproduced in other studies, it may have consequences in relation to both primary and secondary prevention of LBP, because blue collar workers, who are most likely to experience the consequences of LBP, also are those who are most likely to smoke.
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A Content Analysis of Acculturation Research in the Career Development Literature
ERIC Educational Resources Information Center
Miller, Matthew J.; Kerlow-Myers, Andrew E.
2009-01-01
The purpose of the present study was to highlight the importance of acculturation as an explanatory variable in career development and to provide an empirical review of acculturation research in the career development literature. Acculturation is a cultural variable that has been linked to a number of important career development outcomes for…
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Primary School Leadership Practice: How the Subject Matters
ERIC Educational Resources Information Center
Spillane, James P.
2005-01-01
Teaching is a critical consideration in investigations of primary school leadership and not just as an outcome variable. Factoring in instruction as an explanatory variable in scholarship on school leadership involves moving away from views of teaching as a monolithic or unitary practice. When it comes to leadership in primary schools, the subject…
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Examining Explanatory Biases in Young Children's Biological Reasoning
ERIC Educational Resources Information Center
Legare, Cristine H.; Gelman, Susan A.
2014-01-01
Despite the well-established literature on explanation in early childhood, little is known about what constrains children's explanations. State change and negative outcomes were examined as potential explanatory biases in the domain of naïve biology, extending upon previous work in the domain of naïve physics. In two studies, preschool children…
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Lee, K.G.; Hedgecock, T.S.
2008-01-01
The U.S. Geological Survey, in cooperation with the Alabama Department of Transportation, made observations of clear-water contraction scour at 25 bridge sites in the Black Prairie Belt of the Coastal Plain of Alabama. These bridge sites consisted of 54 hydraulic structures, of which 37 have measurable scour holes. Observed scour depths ranged from 1.4 to 10.4 feet. Theoretical clear-water contraction-scour depths were computed for each bridge and compared with observed scour. This comparison showed that theoretical scour depths, in general, exceeded the observed scour depths by about 475 percent. Variables determined to be important in developing scour in laboratory studies along with several other hydraulic variables were investigated to understand their influence within the Alabama field data. The strongest explanatory variables for clear-water contraction scour were channel-contraction ratio and velocity index. Envelope curves were developed relating both of these explanatory variables to observed scour. These envelope curves provide useful tools for assessing reasonable ranges of scour depth in the Black Prairie Belt of Alabama.
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Explanatory variables for adult patients' self-reported recovery after acute lateral ankle sprain.
van Rijn, Rogier M; Willemsen, Sten P; Verhagen, Arianne P; Koes, Bart W; Bierma-Zeinstra, Sita M A
2011-01-01
Longitudinal research on musculoskeletal disorders often makes use of a single measure of recovery, despite the large variation in reported recovery that exists. Patients with an acute ankle sprain often experience no pain or functional disability following treatment, yet report not being fully recovered, or vice versa. The purpose of this study was to find explanatory variables for reporting recovery by analyzing the extent to which different outcomes (eg, pain intensity) were associated with recovery and how baseline scores of different variables influence this association in adult patients after acute lateral ankle sprain. This was a cohort study based on data collected in a randomized controlled trial (RCT). This study was constructed within the framework of an RCT. One hundred two patients who incurred an acute ankle sprain were included. Recovery, pain intensity, giving way of the ankle, and Ankle Function Score (AFS) were assessed during the RCT at baseline and at 4 weeks, 8 weeks, 3 months, and 12 months postinjury. Mean differences were calculated between baseline and follow-up. Associations were calculated using linear mixed models, and the influence of baseline scores on these associations was determined using linear regression with interaction. Associations were found between recovery and the mean differences of pain during running on flat and rough surfaces (4 and 8 weeks, 3 months) and between recovery and the mean difference of giving way of the ankle during walking on a rough surface (8 weeks, 3 months). This study used data collected from an RCT. Therefore, the study was limited to the outcomes measured in that trial, and some explanatory factors easily could have been missed. This study is the first to identify explanatory variables for reporting recovery in adults after ankle sprain. Pain intensity and giving way of the ankle measured during high ankle load activities make it easier to measure and to generalize recovery in this population and should be the primary outcome measures of interest. This study indicates the huge need to reach consensus about primary outcome measures for research in patients sustaining ankle sprains.
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Waite, Ian R.; Brown, Larry R.; Kennen, Jonathan G.; May, Jason T.; Cuffney, Thomas F.; Orlando, James L.; Jones, Kimberly A.
2010-01-01
The successful use of macroinvertebrates as indicators of stream condition in bioassessments has led to heightened interest throughout the scientific community in the prediction of stream condition. For example, predictive models are increasingly being developed that use measures of watershed disturbance, including urban and agricultural land-use, as explanatory variables to predict various metrics of biological condition such as richness, tolerance, percent predators, index of biotic integrity, functional species traits, or even ordination axes scores. Our primary intent was to determine if effective models could be developed using watershed characteristics of disturbance to predict macroinvertebrate metrics among disparate and widely separated ecoregions. We aggregated macroinvertebrate data from universities and state and federal agencies in order to assemble stream data sets of high enough density appropriate for modeling in three distinct ecoregions in Oregon and California. Extensive review and quality assurance of macroinvertebrate sampling protocols, laboratory subsample counts and taxonomic resolution was completed to assure data comparability. We used widely available digital coverages of land-use and land-cover data summarized at the watershed and riparian scale as explanatory variables to predict macroinvertebrate metrics commonly used by state resource managers to assess stream condition. The “best” multiple linear regression models from each region required only two or three explanatory variables to model macroinvertebrate metrics and explained 41–74% of the variation. In each region the best model contained some measure of urban and/or agricultural land-use, yet often the model was improved by including a natural explanatory variable such as mean annual precipitation or mean watershed slope. Two macroinvertebrate metrics were common among all three regions, the metric that summarizes the richness of tolerant macroinvertebrates (RICHTOL) and some form of EPT (Ephemeroptera, Plecoptera, and Trichoptera) richness. Best models were developed for the same two invertebrate metrics even though the geographic regions reflect distinct differences in precipitation, geology, elevation, slope, population density, and land-use. With further development, models like these can be used to elicit better causal linkages to stream biological attributes or condition and can be used by researchers or managers to predict biological indicators of stream condition at unsampled sites.
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NASA Astrophysics Data System (ADS)
Nakatsugawa, M.; Kobayashi, Y.; Okazaki, R.; Taniguchi, Y.
2017-12-01
This research aims to improve accuracy of water level prediction calculations for more effective river management. In August 2016, Hokkaido was visited by four typhoons, whose heavy rainfall caused severe flooding. In the Tokoro river basin of Eastern Hokkaido, the water level (WL) at the Kamikawazoe gauging station, which is at the lower reaches exceeded the design high-water level and the water rose to the highest level on record. To predict such flood conditions and mitigate disaster damage, it is necessary to improve the accuracy of prediction as well as to prolong the lead time (LT) required for disaster mitigation measures such as flood-fighting activities and evacuation actions by residents. There is the need to predict the river water level around the peak stage earlier and more accurately. Previous research dealing with WL prediction had proposed a method in which the WL at the lower reaches is estimated by the correlation with the WL at the upper reaches (hereinafter: "the water level correlation method"). Additionally, a runoff model-based method has been generally used in which the discharge is estimated by giving rainfall prediction data to a runoff model such as a storage function model and then the WL is estimated from that discharge by using a WL discharge rating curve (H-Q curve). In this research, an attempt was made to predict WL by applying the Random Forest (RF) method, which is a machine learning method that can estimate the contribution of explanatory variables. Furthermore, from the practical point of view, we investigated the prediction of WL based on a multiple correlation (MC) method involving factors using explanatory variables with high contribution in the RF method, and we examined the proper selection of explanatory variables and the extension of LT. The following results were found: 1) Based on the RF method tuned up by learning from previous floods, the WL for the abnormal flood case of August 2016 was properly predicted with a lead time of 6 h. 2) Based on the contribution of explanatory variables, factors were selected for the MC method. In this way, plausible prediction results were obtained.
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Fall risk factors analysis based on sample entropy of plantar kinematic signal during stance phase.
Shengyun Liang; Huiyu Jia; Zilong Li; Huiqi Li; Xing Gao; Zuchang Ma; Yingnan Ma; Guoru Zhao
2016-08-01
Falls are a multi-causal phenomenon with a complex interaction. The aim of our research is to study the effect of multiple variables for potential risk of falls and construct an elderly fall risk assessment model based on demographics data and gait characteristics. A total of 101 subjects, whom belong to Malianwa Street, aged above 50 years old and participated in questionnaire survey. Participants were classified into three groups (high, medium and low risk group) according to the score of elderly fall risk assessment scale. In addition, the data of ground reaction force (GRF) and ground reaction moment (GRM) was record when they performed walking at comfortable state. The demographic variables, sample entropy of GRF and GRM, and impulse difference of bilateral foot were considered as potential explanatory variables of risk assessment model. Firstly, we investigated whether different groups could present difference in every variable. Statistical differences were found for the following variables: age (p=2.28e-05); impulse difference (p=0.02036); sample entropy of GRF in vertical direction (p=0.0144); sample entropy of GRM in anterior-posterior direction (p=0.0387). Finally, the multiple regression analysis results indicated that age, impulse difference and sample entropy of resultant GRM could identify individuals who had different levels of fall risk. Therefore, those results could potentially be useful in the fall risk assessment and monitor the state of physical function in elderly population.
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Ayala, George; Bingham, Trista; Kim, Junyeop; Wheeler, Darrell P; Millett, Gregorio A
2012-05-01
We examined the impact of social discrimination and financial hardship on unprotected anal intercourse with a male sex partner of serodiscordant or unknown HIV status in the past 3 months among 1081 Latino and 1154 Black men who have sex with men (MSM; n = 2235) residing in Los Angeles County, California; New York, New York; and Philadelphia, Pennsylvania. We administered HIV testing and a questionnaire assessing 6 explanatory variables. We combined traditional mediation analysis with the results of a path analysis to simultaneously examine the direct, indirect, and total effects of these variables on the outcome variable. Bivariate analysis showed that homophobia, racism, financial hardship, and lack of social support were associated with unprotected anal intercourse with a serodiscordant or sero-unknown partner. Path analysis determined that these relations were mediated by participation in risky sexual situations and lack of social support. However, paths between the explanatory variable and 2 mediating variables varied by participants' serostatus. Future prevention research and program designs should specifically address the differential impact of social discrimination and financial hardship on lack of social support and risky sexual situations among Latino and Black MSM.
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Spahr, Norman E.; Mueller, David K.; Wolock, David M.; Hitt, Kerie J.; Gronberg, JoAnn M.
2010-01-01
Data collected for the U.S. Geological Survey National Water-Quality Assessment program from 1992-2001 were used to investigate the relations between nutrient concentrations and nutrient sources, hydrology, and basin characteristics. Regression models were developed to estimate annual flow-weighted concentrations of total nitrogen and total phosphorus using explanatory variables derived from currently available national ancillary data. Different total-nitrogen regression models were used for agricultural (25 percent or more of basin area classified as agricultural land use) and nonagricultural basins. Atmospheric, fertilizer, and manure inputs of nitrogen, percent sand in soil, subsurface drainage, overland flow, mean annual precipitation, and percent undeveloped area were significant variables in the agricultural basin total nitrogen model. Significant explanatory variables in the nonagricultural total nitrogen model were total nonpoint-source nitrogen input (sum of nitrogen from manure, fertilizer, and atmospheric deposition), population density, mean annual runoff, and percent base flow. The concentrations of nutrients derived from regression (CONDOR) models were applied to drainage basins associated with the U.S. Environmental Protection Agency (USEPA) River Reach File (RF1) to predict flow-weighted mean annual total nitrogen concentrations for the conterminous United States. The majority of stream miles in the Nation have predicted concentrations less than 5 milligrams per liter. Concentrations greater than 5 milligrams per liter were predicted for a broad area extending from Ohio to eastern Nebraska, areas spatially associated with greater application of fertilizer and manure. Probabilities that mean annual total-nitrogen concentrations exceed the USEPA regional nutrient criteria were determined by incorporating model prediction uncertainty. In all nutrient regions where criteria have been established, there is at least a 50 percent probability of exceeding the criteria in more than half of the stream miles. Dividing calibration sites into agricultural and nonagricultural groups did not improve the explanatory capability for total phosphorus models. The group of explanatory variables that yielded the lowest model error for mean annual total phosphorus concentrations includes phosphorus input from manure, population density, amounts of range land and forest land, percent sand in soil, and percent base flow. However, the large unexplained variability and associated model error precluded the use of the total phosphorus model for nationwide extrapolations.
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Determinants of urban sprawl in European cities
Alvanides, Seraphim; Garrod, Guy
2015-01-01
This paper provides empirical evidence that helps to answer several key questions relating to the extent of urban sprawl in Europe. Building on the monocentric city model, this study uses existing data sources to derive a set of panel data for 282 European cities at three time points (1990, 2000 and 2006). Two indices of urban sprawl are calculated that, respectively, reflect changes in artificial area and the levels of urban fragmentation for each city. These are supplemented by a set of data on various economic and geographical variables that might explain the variation of the two indices. Using a Hausman-Taylor estimator and random regressors to control for the possible correlation between explanatory variables and unobservable city-level effects, we find that the fundamental conclusions of the standard monocentric model are valid in the European context for both indices. Although the variables generated by the monocentric model explain a large part of the variation of artificial area, their explanatory power for modelling the fragmentation index is relatively low. PMID:26321770
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Determinants of urban sprawl in European cities.
Oueslati, Walid; Alvanides, Seraphim; Garrod, Guy
2015-07-01
This paper provides empirical evidence that helps to answer several key questions relating to the extent of urban sprawl in Europe. Building on the monocentric city model, this study uses existing data sources to derive a set of panel data for 282 European cities at three time points (1990, 2000 and 2006). Two indices of urban sprawl are calculated that, respectively, reflect changes in artificial area and the levels of urban fragmentation for each city. These are supplemented by a set of data on various economic and geographical variables that might explain the variation of the two indices. Using a Hausman-Taylor estimator and random regressors to control for the possible correlation between explanatory variables and unobservable city-level effects, we find that the fundamental conclusions of the standard monocentric model are valid in the European context for both indices. Although the variables generated by the monocentric model explain a large part of the variation of artificial area, their explanatory power for modelling the fragmentation index is relatively low.
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Flood-frequency prediction methods for unregulated streams of Tennessee, 2000
Law, George S.; Tasker, Gary D.
2003-01-01
Up-to-date flood-frequency prediction methods for unregulated, ungaged rivers and streams of Tennessee have been developed. Prediction methods include the regional-regression method and the newer region-of-influence method. The prediction methods were developed using stream-gage records from unregulated streams draining basins having from 1 percent to about 30 percent total impervious area. These methods, however, should not be used in heavily developed or storm-sewered basins with impervious areas greater than 10 percent. The methods can be used to estimate 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence-interval floods of most unregulated rural streams in Tennessee. A computer application was developed that automates the calculation of flood frequency for unregulated, ungaged rivers and streams of Tennessee. Regional-regression equations were derived by using both single-variable and multivariable regional-regression analysis. Contributing drainage area is the explanatory variable used in the single-variable equations. Contributing drainage area, main-channel slope, and a climate factor are the explanatory variables used in the multivariable equations. Deleted-residual standard error for the single-variable equations ranged from 32 to 65 percent. Deleted-residual standard error for the multivariable equations ranged from 31 to 63 percent. These equations are included in the computer application to allow easy comparison of results produced by the different methods. The region-of-influence method calculates multivariable regression equations for each ungaged site and recurrence interval using basin characteristics from 60 similar sites selected from the study area. Explanatory variables that may be used in regression equations computed by the region-of-influence method include contributing drainage area, main-channel slope, a climate factor, and a physiographic-region factor. Deleted-residual standard error for the region-of-influence method tended to be only slightly smaller than those for the regional-regression method and ranged from 27 to 62 percent.
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Geographically weighted regression and multicollinearity: dispelling the myth
NASA Astrophysics Data System (ADS)
Fotheringham, A. Stewart; Oshan, Taylor M.
2016-10-01
Geographically weighted regression (GWR) extends the familiar regression framework by estimating a set of parameters for any number of locations within a study area, rather than producing a single parameter estimate for each relationship specified in the model. Recent literature has suggested that GWR is highly susceptible to the effects of multicollinearity between explanatory variables and has proposed a series of local measures of multicollinearity as an indicator of potential problems. In this paper, we employ a controlled simulation to demonstrate that GWR is in fact very robust to the effects of multicollinearity. Consequently, the contention that GWR is highly susceptible to multicollinearity issues needs rethinking.
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Developing deterioration models for Wyoming bridges.
DOT National Transportation Integrated Search
2016-05-01
Deterioration models for the Wyoming Bridge Inventory were developed using both stochastic and deterministic models. : The selection of explanatory variables is investigated and a new method using LASSO regression to eliminate human bias : in explana...
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Rationale for hedging initiatives: Empirical evidence from the energy industry
NASA Astrophysics Data System (ADS)
Dhanarajata, Srirajata
Theory offers different rationales for hedging including (i) financial distress and bankruptcy cost, (ii) capacity to capture attractive investment opportunities, (iii) information asymmetry, (iv) economy of scale, (v) substitution for hedging, (vi) managerial risk aversion, and (vii) convexity of tax schedule. The purpose of this dissertation is to empirically test the explanatory power of the first five theoretical rationales on hedging done by oil and gas exploration and production (E&P) companies. The level of hedging is measured by the percentage of production effectively hedged, calculated based on the concept of delta and delta-gamma hedging. I employ Tobit regression, principal components, and panel data analysis on dependent and raw independent variables. Tobit regression is applied due to the fact that the dependent variable used in the analysis is non-negative. Principal component analysis helps to reduce the dimension of explanatory variables while panel data analysis combines/pools the data that is a combination of time-series and cross-sectional. Based on the empirical results, leverage level is consistently found to be a significant factor on hedging activities, either due to an attempt to avoid financial distress by the firm, or an attempt to control agency cost by debtholders, or both. The effect of capital expenditures and discretionary cash flows are both indeterminable due possibly to a potential mismatch in timing of realized cash flow items and hedging decision. Firm size is found to be positively related to hedging supporting economy of scale hypothesis, which is introduced in past literature, as well as the argument that large firm usually are more sophisticated and should be more willing and more comfortable to use hedge instruments than smaller firms.
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Exploring socioeconomic disparities in self-reported oral health among adolescents in california.
Telford, Claire; Coulter, Ian; Murray, Liam
2011-01-01
Socioeconomic factors are associated with disparities in oral health among adolescents; however, the underlying reasons are not clear. The authors conducted a study to determine if known indicators of oral health can explain such disparities. The authors examined data from a 2007 California Health Interview Survey of adolescents. The outcome of interest was self-reported condition of the teeth; covariates were socioeconomic status (SES) (that is, family poverty level and parental education) and a range of other variables representing health-influencing behaviors, dental care and other social factors. The authors conducted analyses by using logistic regression to explain disparities in self-reported condition of the teeth associated with SES. The authors found that socioeconomic disparities decreased substantially after they added all potential explanatory variables to the model, leaving poverty level as the only variable associated with differences in the self-reported condition of the teeth. Adolescents living below the federal poverty guidelines were more likely to report that the condition of their teeth was fair or poor than were adolescents who were least poor (odds ratio = 1.58; 95 percent confidence interval, 1.04-2.41). In multivariate analyses, further oral health disparities existed in relation to behaviors that influence health, social environment and dental care. The results of this study showed that a number of factors decreased, but did not eliminate, the observed relationship between SES and oral health in Californian adolescents. Most of these explanatory factors are modifiable, indicating that socioeconomic differences associated with oral health among adolescents may be amenable to change. Practice Implications. By promoting a healthy lifestyle (including healthy diet, exercise and regular dental attendance) and conveying to patients in languages other than English how to maintain oral health, dentists may be able to ameliorate the effects of socioeconomic disparities in oral health.
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Cerda, Gamal; Pérez, Carlos; Navarro, José I; Aguilar, Manuel; Casas, José A; Aragón, Estíbaliz
2015-01-01
This study tested a structural model of cognitive-emotional explanatory variables to explain performance in mathematics. The predictor variables assessed were related to students' level of development of early mathematical competencies (EMCs), specifically, relational and numerical competencies, predisposition toward mathematics, and the level of logical intelligence in a population of primary school Chilean students (n = 634). This longitudinal study also included the academic performance of the students during a period of 4 years as a variable. The sampled students were initially assessed by means of an Early Numeracy Test, and, subsequently, they were administered a Likert-type scale to measure their predisposition toward mathematics (EPMAT) and a basic test of logical intelligence. The results of these tests were used to analyse the interaction of all the aforementioned variables by means of a structural equations model. This combined interaction model was able to predict 64.3% of the variability of observed performance. Preschool students' performance in EMCs was a strong predictor for achievement in mathematics for students between 8 and 11 years of age. Therefore, this paper highlights the importance of EMCs and the modulating role of predisposition toward mathematics. Also, this paper discusses the educational role of these findings, as well as possible ways to improve negative predispositions toward mathematical tasks in the school domain.
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Guglielminotti, Jean; Dechartres, Agnès; Mentré, France; Montravers, Philippe; Longrois, Dan; Laouénan, Cedric
2015-10-01
Prognostic research studies in anesthesiology aim to identify risk factors for an outcome (explanatory studies) or calculate the risk of this outcome on the basis of patients' risk factors (predictive studies). Multivariable models express the relationship between predictors and an outcome and are used in both explanatory and predictive studies. Model development demands a strict methodology and a clear reporting to assess its reliability. In this methodological descriptive review, we critically assessed the reporting and methodology of multivariable analysis used in observational prognostic studies published in anesthesiology journals. A systematic search was conducted on Medline through Web of Knowledge, PubMed, and journal websites to identify observational prognostic studies with multivariable analysis published in Anesthesiology, Anesthesia & Analgesia, British Journal of Anaesthesia, and Anaesthesia in 2010 and 2011. Data were extracted by 2 independent readers. First, studies were analyzed with respect to reporting of outcomes, design, size, methods of analysis, model performance (discrimination and calibration), model validation, clinical usefulness, and STROBE (i.e., Strengthening the Reporting of Observational Studies in Epidemiology) checklist. A reporting rate was calculated on the basis of 21 items of the aforementioned points. Second, they were analyzed with respect to some predefined methodological points. Eighty-six studies were included: 87.2% were explanatory and 80.2% investigated a postoperative event. The reporting was fairly good, with a median reporting rate of 79% (75% in explanatory studies and 100% in predictive studies). Six items had a reporting rate <36% (i.e., the 25th percentile), with some of them not identified in the STROBE checklist: blinded evaluation of the outcome (11.9%), reason for sample size (15.1%), handling of missing data (36.0%), assessment of colinearity (17.4%), assessment of interactions (13.9%), and calibration (34.9%). When reported, a few methodological shortcomings were observed, both in explanatory and predictive studies, such as an insufficient number of events of the outcome (44.6%), exclusion of cases with missing data (93.6%), or categorization of continuous variables (65.1%.). The reporting of multivariable analysis was fairly good and could be further improved by checking reporting guidelines and EQUATOR Network website. Limiting the number of candidate variables, including cases with missing data, and not arbitrarily categorizing continuous variables should be encouraged.
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Thogmartin, W.E.; Sauer, J.R.; Knutson, M.G.
2007-01-01
We used an over-dispersed Poisson regression with fixed and random effects, fitted by Markov chain Monte Carlo methods, to model population spatial patterns of relative abundance of American woodcock (Scolopax minor) across its breeding range in the United States. We predicted North American woodcock Singing Ground Survey counts with a log-linear function of explanatory variables describing habitat, year effects, and observer effects. The model also included a conditional autoregressive term representing potential correlation between adjacent route counts. Categories of explanatory habitat variables in the model included land-cover composition, climate, terrain heterogeneity, and human influence. Woodcock counts were higher in landscapes with more forest, especially aspen (Populus tremuloides) and birch (Betula spp.) forest, and in locations with a high degree of interspersion among forest, shrubs, and grasslands. Woodcock counts were lower in landscapes with a high degree of human development. The most noteworthy practical application of this spatial modeling approach was the ability to map predicted relative abundance. Based on a map of predicted relative abundance derived from the posterior parameter estimates, we identified major concentrations of woodcock abundance in east-central Minnesota, USA, the intersection of Vermont, USA, New York, USA, and Ontario, Canada, the upper peninsula of Michigan, USA, and St. Lawrence County, New York. The functional relations we elucidated for the American woodcock provide a basis for the development of management programs and the model and map may serve to focus management and monitoring on areas and habitat features important to American woodcock.
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Dropouts in Two-Year Colleges: Better Prediction with the Use of Moderator Subgroups.
ERIC Educational Resources Information Center
Capoor, Madan; Eagle, Norman
Failure to identify and account for the effect of moderator variables is an important reason for the low explanatory power of much educational research. Pre-existing subgroups such as sex, ethnicity, and curriculum offer an easily identifiable and theoretically meaningful source of moderator variables. Tests for intercept and slope differences in…
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Timpka, Toomas; Jacobsson, Jenny; Dahlström, Örjan; Kowalski, Jan; Bargoria, Victor; Ekberg, Joakim; Nilsson, Sverker; Renström, Per
2015-11-01
Athletes' psychological characteristics are important for understanding sports injury mechanisms. We examined the relevance of psychological factors in an integrated model of overuse injury risk in athletics/track and field. Swedish track and field athletes (n=278) entering a 12-month injury surveillance in March 2009 were also invited to complete a psychological survey. Simple Cox proportional hazards models were compiled for single explanatory variables. We also tested multiple models for 3 explanatory variable groupings: an epidemiological model without psychological variables, a psychological model excluding epidemiological variables and an integrated (combined) model. The integrated multiple model included the maladaptive coping behaviour self-blame (p=0.007; HR 1.32; 95% CI 1.08 to 1.61), and an interaction between athlete category and injury history (p<0.001). Youth female (p=0.034; HR 0.51; 95% CI 0.27 to 0.95) and youth male (p=0.047; HR 0.49; 95% CI 0.24 to 0.99) athletes with no severe injury the previous year were at half the risk of sustaining a new injury compared with the reference group. A training load index entered the epidemiological multiple model, but not the integrated model. The coping behaviour self-blame replaced training load in an integrated explanatory model of overuse injury risk in athletes. What seemed to be more strongly related to the likelihood of overuse injury was not the athletics load per se, but, rather, the load applied in situations when the athlete's body was in need of rest. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
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Corruption as a Propensity: Personality and Motivational Determinants Among Nigerians.
Agbo, Aaron Adibe; Iwundu, Emmanuel Ifeanyi
2016-01-01
Psychologically oriented studies on corruption are lacking despite the fact that psychology has contributed enormously to the understanding of criminal behaviors over the past decades. Part of this problem relates to the lack of psychological tone in the definition and measurement of corruption. Taking a clue from the extensive psychological discourse on crime, which provides strong evidence for the existence of criminal tendencies, we conceptualized corruption here in terms of propensity. Possible personality and motivation determinants of this propensity were then explored. Results revealed that extraversion and conscientiousness positively and inversely predicted corruption tendencies, respectively. Results further showed that extrinsic motivation positively potentiates corruption tendencies, whereas intrinsic motivation was inversely related to corruption tendencies. Analysis of demographic variables revealed that males were more likely to be corrupt than their female counterparts. In general, findings here are consistent with the criminal behavior literature, which strongly suggests that the study of corruption is indeed amenable to psychological theories and methods and that individual differences variables constitute important explanatory variables in this regard.
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Repeat migration and disappointment.
Grant, E K; Vanderkamp, J
1986-01-01
This article investigates the determinants of repeat migration among the 44 regions of Canada, using information from a large micro-database which spans the period 1968 to 1971. The explanation of repeat migration probabilities is a difficult task, and this attempt is only partly successful. May of the explanatory variables are not significant, and the overall explanatory power of the equations is not high. In the area of personal characteristics, the variables related to age, sex, and marital status are generally significant and with expected signs. The distance variable has a strongly positive effect on onward move probabilities. Variables related to prior migration experience have an important impact that differs between return and onward probabilities. In particular, the occurrence of prior moves has a striking effect on the probability of onward migration. The variable representing disappointment, or relative success of the initial move, plays a significant role in explaining repeat migration probabilities. The disappointment variable represents the ratio of actural versus expected wage income in the year after the initial move, and its effect on both repeat migration probabilities is always negative and almost always highly significant. The repeat probabilities diminish after a year's stay in the destination region, but disappointment in the most recent year still has a bearing on the delayed repeat probabilities. While the quantitative impact of the disappointment variable is not large, it is difficult to draw comparisons since similar estimates are not available elsewhere.
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Self-rated health and health-strengthening factors in community-living frail older people.
Ebrahimi, Zahra; Dahlin-Ivanoff, Synneve; Eklund, Kajsa; Jakobsson, Annika; Wilhelmson, Katarina
2015-04-01
The aim of this study was to analyse the explanatory power of variables measuring health-strengthening factors for self-rated health among community-living frail older people. Frailty is commonly constructed as a multi-dimensional geriatric syndrome ascribed to the multi-system deterioration of the reserve capacity in older age. Frailty in older people is associated with decreased physical and psychological well-being. However, knowledge about the experiences of health in frail older people is still limited. The design of the study was cross-sectional. The data were collected between October 2008 and November 2010 through face-to-face structured interviews with older people aged 65-96 years (N = 161). Binary logistic regression was used to analyse whether a set of explanatory relevant variables is associated with self-rated health. The results from the final model showed that satisfaction with one's ability to take care of oneself, having 10 or fewer symptoms and not feeling lonely had the best explanatory power for community-living frail older peoples' experiences of good health. The results indicate that a multi-disciplinary approach is desirable, where the focus should not only be on medical problems but also on providing supportive services to older people to maintain their independence and experiences of health despite frailty. © 2014 John Wiley & Sons Ltd.
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Gender interactions and success.
Wiggins, Carla; Peterson, Teri
2004-01-01
Does gender by itself, or does gender's interaction with career variables, better explain the difference between women and men's careers in healthcare management? US healthcare managers were surveyed regarding career and personal experiences. Gender was statistically interacted with explanatory variables. Multiple regression with backwards selection systematically removed non-significant variables. All gender interaction variables were non-significant. Much of the literature proposes that work and career factors impact working women differently than working men. We find that while gender alone is a significant predictor of income, it does not significantly interact with other career variables.
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Belief models in first episode schizophrenia in South India.
Saravanan, Balasubramanian; Jacob, K S; Johnson, Shanthi; Prince, Martin; Bhugra, Dinesh; David, Anthony S
2007-06-01
Existing evidence indicates that dissonance between patients' and professionals' explanatory models affects engagement of patients with psychiatric services in Western and non-Western countries. To assess qualitatively the explanatory models (EMs) of psychosis and their association with clinical variables in a representative sample of first episode patients with schizophrenia in South India. One hundred and thirty one patients with schizophrenia presenting consecutively were assessed. Measures included the patient's explanatory models, and clinician ratings of insight, symptoms of psychosis, and functioning on standard scales. The majority of patients (70%) considered spiritual and mystical factors as the cause of their predicament; 22% held multiple models of illness. Patients who held a biomedical concept of disease had significantly higher scores on the insight scale compared to those who held non-medical beliefs. Multivariate analyses identified three factors associated with holding of spiritual/mystical models (female sex, low education and visits to traditional healers); and a single factor (high level of insight) for the endorsement of biological model. Patients with schizophrenia in this region of India hold a variety of non-medical belief models, which influence patterns of health seeking. Those holding non-medical explanatory models are likey to be rated as having less insight.
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Classification and regression trees
G. G. Moisen
2008-01-01
Frequently, ecologists are interested in exploring ecological relationships, describing patterns and processes, or making spatial or temporal predictions. These purposes often can be addressed by modeling the relationship between some outcome or response and a set of features or explanatory variables.
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A. C. Gellis; NO-VALUE
2013-01-01
The significant characteristics controlling the variability in storm-generated suspended-sediment loads and concentrations were analyzed for four basins of differing land use (forest, pasture, cropland, and urbanizing) in humid-tropical Puerto Rico. Statistical analysis involved stepwise regression on factor scores. The explanatory variables were attributes of flow,...
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K. R. Sherrill; M. A. Lefsky; J. B. Bradford; M. G. Ryan
2008-01-01
This study evaluates the relative ability of simple light detection and ranging (lidar) indices (i.e., mean and maximum heights) and statistically derived canonical correlation analysis (CCA) variables attained from discrete-return lidar to estimate forest structure and forest biomass variables for three temperate subalpine forest sites. Both lidar and CCA explanatory...
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K.R. Sherrill; M.A. Lefsky; J.B. Bradford; M.G. Ryan
2008-01-01
This study evaluates the relative ability of simple light detection and ranging (lidar) indices (i.e., mean and maximum heights) and statistically derived canonical correlation analysis (CCA) variables attained from discrete-return lidar to estimate forest structure and forest biomass variables for three temperate subalpine forest sites. Both lidar and CCA explanatory...
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The Impact of Household Heads' Education Levels on the Poverty Risk: The Evidence from Turkey
ERIC Educational Resources Information Center
Bilenkisi, Fikret; Gungor, Mahmut Sami; Tapsin, Gulcin
2015-01-01
This study aims to analyze the relationship between the education levels of household heads and the poverty risk of households in Turkey. The logistic regression models have been estimated with the poverty risk of a household as a dependent variable and a set of educational levels as explanatory variables for all households. There are subgroups of…
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ERIC Educational Resources Information Center
Luna, Andrew L.
2007-01-01
This study used two multiple regression analyses to develop an explanatory model to determine which model might best explain faculty salaries. The central purpose of the study was to determine if using a single market ratio variable was a stronger predictor for faculty salaries than the use of dummy variables representing various disciplines.…
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Exploring individual differences in preschoolers' causal stance.
Alvarez, Aubry; Booth, Amy E
2016-03-01
Preschoolers, as a group, are highly attuned to causality, and this attunement is known to facilitate memory, learning, and problem solving. However, recent work reveals substantial individual variability in the strength of children's "causal stance," as demonstrated by their curiosity about and preference for new causal information. In this study, we explored the coherence and short-term stability of individual differences in children's causal stance. We also began to investigate the origins of this variability, focusing particularly on the potential role of mothers' explanatory talk in shaping the causal stance of their children. Two measures of causal stance correlated with each other, as well as themselves across time. Both also revealed internal consistency of response. The strength of children's causal stance also correlated with mother's responses on the same tasks and the frequency with which mothers emphasized causality during naturalistic joint activities with their children. Implications for theory and practice are discussed. (c) 2016 APA, all rights reserved).
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Bingham, Trista; Kim, Junyeop; Wheeler, Darrell P.; Millett, Gregorio A.
2012-01-01
Objectives. We examined the impact of social discrimination and financial hardship on unprotected anal intercourse with a male sex partner of serodiscordant or unknown HIV status in the past 3 months among 1081 Latino and 1154 Black men who have sex with men (MSM; n = 2235) residing in Los Angeles County, California; New York, New York; and Philadelphia, Pennsylvania. Methods. We administered HIV testing and a questionnaire assessing 6 explanatory variables. We combined traditional mediation analysis with the results of a path analysis to simultaneously examine the direct, indirect, and total effects of these variables on the outcome variable. Results. Bivariate analysis showed that homophobia, racism, financial hardship, and lack of social support were associated with unprotected anal intercourse with a serodiscordant or sero-unknown partner. Path analysis determined that these relations were mediated by participation in risky sexual situations and lack of social support. However, paths between the explanatory variable and 2 mediating variables varied by participants’ serostatus. Conclusions. Future prevention research and program designs should specifically address the differential impact of social discrimination and financial hardship on lack of social support and risky sexual situations among Latino and Black MSM. PMID:22401516
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Johnson, Shanthi; Sathyaseelan, Manoranjitham; Charles, Helen; Jeyaseelan, Visalakshi; Jacob, Kuruthukulangara Sebastian
2012-09-27
The sole focus of models of insight on bio-medical perspectives to the complete exclusion of local, non-medical and cultural constructs mandates review. This study attempted to investigate the impact of insight, psychopathology, explanatory models of illness on outcome of first episode schizophrenia. Patients diagnosed to have DSM IV schizophrenia (n = 131) were assessed prospectively for insight, psychopathology, explanatory models of illness at baseline, 6, 12 and 60 months using standard instruments. Multiple linear and logistic regression and generalized estimating equations (GEE) were employed to assess predictors of outcome. We could follow up 95 (72.5%) patients. Sixty-five of these patients (68.4%) achieved remission. There was a negative relationship between psychosis rating and insight scores. Urban residence, fluctuating course of the initial illness, and improvement in global functioning at 6 months and lower psychosis rating at 12 months were significantly related to remission at 5 years. Insight scores, number of non-medical explanatory models and individual explanatory models held during the later course of the illness were significantly associated with outcome. Analysis of longitudinal data using GEE showed that women, rural residence, insight scores and number of non-medical explanatory models of illness held were significantly associated with BPRS scores during the study period. Insight, the disease model and the number of non-medical model positively correlated with improvement in psychosis arguing for a complex interaction between the culture, context and illness variables. These finding argue that insight and explanatory models are secondary to psychopathology, course and outcome of the illness. The awareness of mental illness is a narrative act in which people make personal sense of the many challenges they face. The course and outcome of the illness, cultural context, acceptable cultural explanations and the prevalent social stigma interact to produce a complex and multifaceted understanding of the issues. This complexity calls for a nuanced framing of insight.
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Huttunen, K-L; Mykrä, H; Oksanen, J; Astorga, A; Paavola, R; Muotka, T
2017-05-03
One of the key challenges to understanding patterns of β diversity is to disentangle deterministic patterns from stochastic ones. Stochastic processes may mask the influence of deterministic factors on community dynamics, hindering identification of the mechanisms causing variation in community composition. We studied temporal β diversity (among-year dissimilarity) of macroinvertebrate communities in near-pristine boreal streams across 14 years. To assess whether the observed β diversity deviates from that expected by chance, and to identify processes (deterministic vs. stochastic) through which different explanatory factors affect community variability, we used a null model approach. We observed that at the majority of sites temporal β diversity was low indicating high community stability. When stochastic variation was unaccounted for, connectivity was the only variable explaining temporal β diversity, with weakly connected sites exhibiting higher community variability through time. After accounting for stochastic effects, connectivity lost importance, suggesting that it was related to temporal β diversity via random colonization processes. Instead, β diversity was best explained by in-stream vegetation, community variability decreasing with increasing bryophyte cover. These results highlight the potential of stochastic factors to dampen the influence of deterministic processes, affecting our ability to understand and predict changes in biological communities through time.
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Harapan, Harapan; Anwar, Samsul; Setiawan, Abdul Malik; Sasmono, R Tedjo
2016-07-12
The first dengue vaccine (DV) has been licensed in some countries, but an assessment of the public's acceptance of DV is widely lacking. This study aimed to explore and understand DV acceptance and its associated explanatory variables among healthy inhabitants of Aceh, Indonesia. A community-based cross-sectional survey was conducted from November 2014 to March 2015 in nine regencies of Aceh that were selected randomly. A set of validated questionnaires covering a range of explanatory variables and DV acceptance was used to conduct the interviews. A multi-step logistic regression analysis and Spearman's rank correlation were employed to assess the role of explanatory variables in DV acceptance. We included 652 community members in the final analysis and found that 77.3% of them were willing to accept the DV. Gender, monthly income, socioeconomic status (SES), attitude toward dengue fever (DF) and attitude toward vaccination practice were associated with DV acceptance in bivariate analyses (P<0.05). A correlation analysis confirmed that attitude toward vaccination practice and attitude toward DF were strongly correlated with DV acceptance, rs=0.41 and rs=0.39, respectively (P<0.001). The multivariate analysis revealed that a high monthly income, high SES, and a good attitude toward vaccination practice and toward DF were independent predictors of DV acceptance. The acceptance rate of the DV among inhabitants of Aceh, Indonesia was relatively high, and the strongest associated factors of higher support for the DV were a good attitude toward vaccination practices and a good attitude toward DF. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Schefers, J M; Weigel, K A; Rawson, C L; Zwald, N R; Cook, N B
2010-04-01
Data from lactating Holstein cows in herds that participate in a commercial progeny testing program were analyzed to explain management factors associated with herd-average conception and service rates on large commercial dairies. On-farm herd management software was used as the source of data related to production, reproduction, culling, and milk quality for 108 herds. Also, a survey regarding management, facilities, nutrition, and labor was completed on 86 farms. A total of 41 explanatory variables related to management factors and conditions that could affect conception and service rate were considered in this study. Models explaining conception and service rates were developed using a machine learning algorithm for constructing model trees. The most important explanatory variables associated with conception rate were the percentage of repeated inseminations between 4 and 17 d post-artificial insemination, stocking density in the breeding pen, length of the voluntary waiting period, days at pregnancy examination, and somatic cell score. The most important explanatory variables associated with service rate were the number of lactating cows per breeding technician, use of a resynchronization program, utilization of soakers in the holding area during the summer, and bunk space per cow in the breeding pen. The aforementioned models explained 35% and 40% of the observed variation in conception rate and service rate, respectively, and underline the association of herd-level management factors not strictly related to reproduction with herd reproductive performance. Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
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Fox, Jesse; Warber, Katie M
2014-01-01
Social networking sites serve as both a source of information and a source of tension between romantic partners. Previous studies have investigated the use of Facebook for monitoring former and current romantic partners, but why certain individuals engage in this behavior has not been fully explained. College students (N=328) participated in an online survey that examined two potential explanatory variables for interpersonal electronic surveillance (IES) of romantic partners: attachment style and relational uncertainty. Attachment style predicted both uncertainty and IES, with preoccupieds and fearfuls reporting the highest levels. Uncertainty did not predict IES, however. Future directions for research on romantic relationships and online surveillance are explored.
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Lee, Michael T.; Asquith, William H.; Oden, Timothy D.
2012-01-01
In December 2005, the U.S. Geological Survey (USGS), in cooperation with the City of Houston, Texas, began collecting discrete water-quality samples for nutrients, total organic carbon, bacteria (Escherichia coli and total coliform), atrazine, and suspended sediment at two USGS streamflow-gaging stations that represent watersheds contributing to Lake Houston (08068500 Spring Creek near Spring, Tex., and 08070200 East Fork San Jacinto River near New Caney, Tex.). Data from the discrete water-quality samples collected during 2005–9, in conjunction with continuously monitored real-time data that included streamflow and other physical water-quality properties (specific conductance, pH, water temperature, turbidity, and dissolved oxygen), were used to develop regression models for the estimation of concentrations of water-quality constituents of substantial source watersheds to Lake Houston. The potential explanatory variables included discharge (streamflow), specific conductance, pH, water temperature, turbidity, dissolved oxygen, and time (to account for seasonal variations inherent in some water-quality data). The response variables (the selected constituents) at each site were nitrite plus nitrate nitrogen, total phosphorus, total organic carbon, E. coli, atrazine, and suspended sediment. The explanatory variables provide easily measured quantities to serve as potential surrogate variables to estimate concentrations of the selected constituents through statistical regression. Statistical regression also facilitates accompanying estimates of uncertainty in the form of prediction intervals. Each regression model potentially can be used to estimate concentrations of a given constituent in real time. Among other regression diagnostics, the diagnostics used as indicators of general model reliability and reported herein include the adjusted R-squared, the residual standard error, residual plots, and p-values. Adjusted R-squared values for the Spring Creek models ranged from .582–.922 (dimensionless). The residual standard errors ranged from .073–.447 (base-10 logarithm). Adjusted R-squared values for the East Fork San Jacinto River models ranged from .253–.853 (dimensionless). The residual standard errors ranged from .076–.388 (base-10 logarithm). In conjunction with estimated concentrations, constituent loads can be estimated by multiplying the estimated concentration by the corresponding streamflow and by applying the appropriate conversion factor. The regression models presented in this report are site specific, that is, they are specific to the Spring Creek and East Fork San Jacinto River streamflow-gaging stations; however, the general methods that were developed and documented could be applied to most perennial streams for the purpose of estimating real-time water quality data.
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Jones, Christina L.; Jensen, Jakob D.; Scherr, Courtney L.; Brown, Natasha R.; Christy, Katheryn; Weaver, Jeremy
2015-01-01
The Health Belief Model (HBM) posits that messages will achieve optimal behavior change if they successfully target perceived barriers, benefits, self-efficacy, and threat. While the model seems to be an ideal explanatory framework for communication research, theoretical limitations have limited its use in the field. Notably, variable ordering is currently undefined in the HBM. Thus, it is unclear whether constructs mediate relationships comparably (parallel mediation), in sequence (serial mediation), or in tandem with a moderator (moderated mediation). To investigate variable ordering, adults (N = 1,377) completed a survey in the aftermath of an 8-month flu vaccine campaign grounded in the HBM. Exposure to the campaign was positively related to vaccination behavior. Statistical evaluation supported a model where the indirect effect of exposure on behavior through perceived barriers and threat was moderated by self-efficacy (moderated mediation). Perceived barriers and benefits also formed a serial mediation chain. The results indicate that variable ordering in the Health Belief Model may be complex, may help to explain conflicting results of the past, and may be a good focus for future research. PMID:25010519
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Cerda, Gamal; Pérez, Carlos; Navarro, José I.; Aguilar, Manuel; Casas, José A.; Aragón, Estíbaliz
2015-01-01
This study tested a structural model of cognitive-emotional explanatory variables to explain performance in mathematics. The predictor variables assessed were related to students’ level of development of early mathematical competencies (EMCs), specifically, relational and numerical competencies, predisposition toward mathematics, and the level of logical intelligence in a population of primary school Chilean students (n = 634). This longitudinal study also included the academic performance of the students during a period of 4 years as a variable. The sampled students were initially assessed by means of an Early Numeracy Test, and, subsequently, they were administered a Likert-type scale to measure their predisposition toward mathematics (EPMAT) and a basic test of logical intelligence. The results of these tests were used to analyse the interaction of all the aforementioned variables by means of a structural equations model. This combined interaction model was able to predict 64.3% of the variability of observed performance. Preschool students’ performance in EMCs was a strong predictor for achievement in mathematics for students between 8 and 11 years of age. Therefore, this paper highlights the importance of EMCs and the modulating role of predisposition toward mathematics. Also, this paper discusses the educational role of these findings, as well as possible ways to improve negative predispositions toward mathematical tasks in the school domain. PMID:26441739
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A Predictive Model of Domestic Violence in Multicultural Families Focusing on Perpetrator.
Choi, Eun Young; Hyun, Hye Jin
2016-09-01
This study was conducted to assess predictor variables of husbands in multicultural families and examine the relationship among variables after setting up a hypothetical model including influencing factors, so as to provide a framework necessary for developing nursing interventions of domestic violence. The participants were 260 husbands in multicultural families in four cities in Korea. Data were analyzed using SPSS 22.0 and AMOS 20.0. Self-control, social support, family of origin violence experience and stress on cultural adaptation directly affected to dysfunctional communication, and the explanatory power of the variables was 64.7%. Family of origin violence experience in domestic stress on cultural adaptation, and dysfunctional communication were directly related to domestic violence in multicultural families, and the explanatory power of the variables was 64.6%. We found out that all variables in the model had mediation effects to domestic violence through dysfunctional communication. In other words, self-control and social support had complete mediation effects, and family of origin violence experience in domestic violence and stress on cultural adaptation had partial mediation effects. The variables explained in this study should be considered as predictive factors of domestic violence in multicultural families, and used to provide preventive nursing intervention. Our resutls can be taken into account for developing and implementing programs on alleviating dysfunctional communication in multicultural families in Korea. Copyright © 2016. Published by Elsevier B.V.
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Ordinal probability effect measures for group comparisons in multinomial cumulative link models.
Agresti, Alan; Kateri, Maria
2017-03-01
We consider simple ordinal model-based probability effect measures for comparing distributions of two groups, adjusted for explanatory variables. An "ordinal superiority" measure summarizes the probability that an observation from one distribution falls above an independent observation from the other distribution, adjusted for explanatory variables in a model. The measure applies directly to normal linear models and to a normal latent variable model for ordinal response variables. It equals Φ(β/2) for the corresponding ordinal model that applies a probit link function to cumulative multinomial probabilities, for standard normal cdf Φ and effect β that is the coefficient of the group indicator variable. For the more general latent variable model for ordinal responses that corresponds to a linear model with other possible error distributions and corresponding link functions for cumulative multinomial probabilities, the ordinal superiority measure equals exp(β)/[1+exp(β)] with the log-log link and equals approximately exp(β/2)/[1+exp(β/2)] with the logit link, where β is the group effect. Another ordinal superiority measure generalizes the difference of proportions from binary to ordinal responses. We also present related measures directly for ordinal models for the observed response that need not assume corresponding latent response models. We present confidence intervals for the measures and illustrate with an example. © 2016, The International Biometric Society.
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A hierarchical spatial model of avian abundance with application to Cerulean Warblers
Thogmartin, Wayne E.; Sauer, John R.; Knutson, Melinda G.
2004-01-01
Surveys collecting count data are the primary means by which abundance is indexed for birds. These counts are confounded, however, by nuisance effects including observer effects and spatial correlation between counts. Current methods poorly accommodate both observer and spatial effects because modeling these spatially autocorrelated counts within a hierarchical framework is not practical using standard statistical approaches. We propose a Bayesian approach to this problem and provide as an example of its implementation a spatial model of predicted abundance for the Cerulean Warbler (Dendroica cerulea) in the Prairie-Hardwood Transition of the upper midwestern United States. We used an overdispersed Poisson regression with fixed and random effects, fitted by Markov chain Monte Carlo methods. We used 21 years of North American Breeding Bird Survey counts as the response in a loglinear function of explanatory variables describing habitat, spatial relatedness, year effects, and observer effects. The model included a conditional autoregressive term representing potential correlation between adjacent route counts. Categories of explanatory habitat variables in the model included land cover composition and configuration, climate, terrain heterogeneity, and human influence. The inherent hierarchy in the model was from counts occurring, in part, as a function of observers within survey routes within years. We found that the percentage of forested wetlands, an index of wetness potential, and an interaction between mean annual precipitation and deciduous forest patch size best described Cerulean Warbler abundance. Based on a map of relative abundance derived from the posterior parameter estimates, we estimated that only 15% of the species' population occurred on federal land, necessitating active engagement of public landowners and state agencies in the conservation of the breeding habitat for this species. Models of this type can be applied to any data in which the response is counts, such as animal counts, activity (e.g.,nest) counts, or species richness. The most noteworthy practical application of this spatial modeling approach is the ability to map relative species abundance. The functional relationships that we elucidated for the Cerulean Warbler provide a basis for the development of management programs and may serve to focus management and monitoring on areas and habitat variables important to Cerulean Warblers.
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ERIC Educational Resources Information Center
Tymms, Peter
2001-01-01
The feelings (self-concepts and attitudes) of 21,000 British 7-year-olds toward math, reading, and school were investigated using multivariate multilevel models. The most important explanatory variables were the teacher and pupils' academic level. Other variables (age, sex, and first language) were weakly connected to attitude measures. (Contains…
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ERIC Educational Resources Information Center
Wolfe, Adam
2016-01-01
This correlational, explanatory, longitudinal study sought to determine the combination of community and family-level demographic variables found in the 2010 U.S. Census data that most accurately predicted a New Jersey school district's percentage of students scoring proficient or above on the 2010, 2011, and 2012 NJ ASK 7 in Language Arts and…
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An evaluation of nitrogen and phosphorus responses to rain events in a forested watershed
NASA Astrophysics Data System (ADS)
Steadman, C.; Argerich, A.; Bladon, K. D.; Johnson, S. L.
2017-12-01
Nitrogen (N) and phosphorus (P) exhibit differential responses to storm events which reflect complex, hydrologically-driven biogeochemical activity in a watershed. However, the magnitude of the responses change throughout the year indicating that they may be strongly influenced by antecedent precipitation conditions. To evaluate N and P responses to storms, we collected storm samples from four subwatersheds in a small forested watershed over a 12-month period as well as climate and hydrologic data. We quantified dissolved nitrate (NO3-), ammonium (NH4+), total dissolved nitrogen (TDN), soluble reactive phosphorus (SRP), and total dissolved phosphorus (TDP) concentrations and exports in 300 samples and examined responses across subwatersheds and storms. To assess the influence of potential drivers, we generated a series of models with discharge, instantaneous rain, and cumulative rain as explanatory variables for analyte concentrations. We also constructed models with cumulative rain as the explanatory variable for analyte exports. There was strong evidence (p < .05) that cumulative rain or the cumulative rain-subwatershed interaction were important for all analyte exports and concentrations. In contrast, evidence was weak for the significance of instantaneous rain for any analyte concentrations while discharge or the discharge-subwatershed interaction was significant for NO3- and NH4+, respectively. Of all factors, cumulative rain was the most relevant to explain analyte concentrations (i.e., showed the highest pseudo-R2), except for NH4+, for which discharge was more relevant. There was significant spatial and temporal variability for all analyte concentrations with the exception of NH4+, which showed little variability storm-to-storm. Maximum NO3- concentration occurred at the onset of the wet season while SRP had the lowest concentration during the same time period. Differential responses of analytes evidence distinct influences of hydrologically-driven biogeochemical activity on individual analytes. However, strong correlations with cumulative rain suggest that insight may be gained through consideration of coarser factors such as antecedent precipitation conditions which may serve to integrate complexities of the hillslope, improving understanding of N and P variability.
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Assessing groundwater vulnerability to agrichemical contamination in the Midwest US
Burkart, M.R.; Kolpin, D.W.; James, D.E.
1999-01-01
Agrichemicals (herbicides and nitrate) are significant sources of diffuse pollution to groundwater. Indirect methods are needed to assess the potential for groundwater contamination by diffuse sources because groundwater monitoring is too costly to adequately define the geographic extent of contamination at a regional or national scale. This paper presents examples of the application of statistical, overlay and index, and process-based modeling methods for groundwater vulnerability assessments to a variety of data from the Midwest U.S. The principles for vulnerability assessment include both intrinsic (pedologic, climatologic, and hydrogeologic factors) and specific (contaminant and other anthropogenic factors) vulnerability of a location. Statistical methods use the frequency of contaminant occurrence, contaminant concentration, or contamination probability as a response variable. Statistical assessments are useful for defining the relations among explanatory and response variables whether they define intrinsic or specific vulnerability. Multivariate statistical analyses are useful for ranking variables critical to estimating water quality responses of interest. Overlay and index methods involve intersecting maps of intrinsic and specific vulnerability properties and indexing the variables by applying appropriate weights. Deterministic models use process-based equations to simulate contaminant transport and are distinguished from the other methods in their potential to predict contaminant transport in both space and time. An example of a one-dimensional leaching model linked to a geographic information system (GIS) to define a regional metamodel for contamination in the Midwest is included.
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Delay discounting rates: a strong prognostic indicator of smoking relapse.
Sheffer, Christine E; Christensen, Darren R; Landes, Reid; Carter, Larry P; Jackson, Lisa; Bickel, Warren K
2014-11-01
Recent evidence suggests that several dimensions of impulsivity and locus of control are likely to be significant prognostic indicators of relapse. One-hundred and thirty-one treatment seeking smokers were enrolled in six weeks of multi-component cognitive-behavioral therapy with eight weeks of nicotine replacement therapy. Cox proportional hazard regressions were used to model days to relapse with each of the following: delay discounting of $100, delay discounting of $1000, six subscales of the Barratt Impulsiveness Scale (BIS), Rotter's Locus of Control (RLOC), Fagerstrom's Test for Nicotine Dependence (FTND), and the Perceived Stress Scale (PSS). Hazard ratios for a one standard deviation increase were estimated with 95% confidence intervals for each explanatory variable. Likelihood ratios were used to examine the level of association with days to relapse for different combinations of the explanatory variables while accounting for nicotine dependence and stress level. These analyses found that the $100 delay discounting rate had the strongest association with days to relapse. Further, when discounting rates were combined with the FTND and PSS, the associations remained significant. When the other measures were combined with the FTND and PSS, their associations with relapse non-significant. These findings indicate that delay discounting is independently associated with relapse and adds to what is already accounted for by nicotine dependence and stress level. They also signify that delay discounting is a productive new target for enhancing treatment for tobacco dependence. Consequently, adding an intervention designed to decrease discounting rates to a comprehensive treatment for tobacco dependence has the potential to decrease relapse rates. Copyright © 2014 Elsevier Ltd. All rights reserved.
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Kennen, J.G.
1999-01-01
The level of macroinvertebrate community impairment was statistically related to selected basin and water-quality characteristics in New Jersey streams. More than 700 ambient biomonitoring stations were chosen to evaluate potential and known anthropogenic effects. Macroinvertebrate communities were assessed with a modified rapid-bioassessment approach using three impairment ratings (nonimpaired, moderately impaired, and severely impaired). Maximum-likelihood multiple logistic-regression analysis was used to develop equations defining the probability of community impairment above predetermined impairment levels. Seven of the original 140 explanatory variables were highly related to the level of community impairment. Explanatory variables found to be most useful for predicting severe macroinvertebrate community impairment were the amount of urban land and total flow of municipal effluent. Area underlain by the Reading Prong physiographic region and amount of forested land were inversely related to severe impairment. Nonparametric analysis of variance on rank-transformed bioassessment scores was used to evaluate differences in level of impairment among physiographic regions and major drainage areas simultaneously. Rejection of the null hypothesis indicated that the levels of impairment among all six physiographic regions and five major drainage areas were not equal. Physiographic regions located in the less urbanized northwest portion of New Jersey were not significantly different from each other and had the lowest occurrence of severely impaired macroinvertebrate communities. Physiographic regions containing urban centers had a higher probability of exhibiting a severely impaired macroinvertebrate community. Analysis of major drainage areas indicates that levels of impairment in the Atlantic Coastal Rivers drainage area differed significantly from those in the Lower Delaware River drainage area.
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Black-white preterm birth disparity: a marker of inequality
Purpose. The racial disparity in preterrn birth (PTB) is a persistent feature of perinatal epidemiology, inconsistently modeled in the literature. Rather than include race as an explanatory variable, or employ race-stratified models, we sought to directly model the PTB disparity ...
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ASSESSING ACCURACY OF NET CHANGE DERIVED FROM LAND COVER MAPS
Net change derived from land-cover maps provides important descriptive information for environmental monitoring and is often used as an input or explanatory variable in environmental models. The sampling design and analysis for assessing net change accuracy differ from traditio...
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Multilevel Modeling with Correlated Effects
ERIC Educational Resources Information Center
Kim, Jee-Seon; Frees, Edward W.
2007-01-01
When there exist omitted effects, measurement error, and/or simultaneity in multilevel models, explanatory variables may be correlated with random components, and standard estimation methods do not provide consistent estimates of model parameters. This paper introduces estimators that are consistent under such conditions. By employing generalized…
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Land Use, Land Conservation, and Wind Energy Development Outcomes in New England
NASA Astrophysics Data System (ADS)
Weimar, William Cameron
This dissertation provides three independent research inquiries. The first examines how inter-governmental policy, site-specific, and social factors lead to the success, prolonged delay, or failure of inland wind power projects in New England. The three case studies examined include the 48 megawatt Glebe Mountain Wind Farm proposal in southern Vermont, the 30 megawatt Hoosac Wind Farm in western Massachusetts, and the 24 megawatt Lempster Wind Farm in southern New Hampshire. To ascertain why the project outcomes varied, 45 semi-structured interviews were conducted with a range of stakeholders, including wind development firms, utility companies, state regulatory agencies, regional planning commissions, town officials, land conservation organizations, and opposition groups. The second study establishes a comprehensive set of thirty-seven explanatory variables to determine the amount of suitable land and the corresponding electricity generation potential within the prime wind resource areas of Western Massachusetts. The explanatory variables are incorporated into Boolean GIS suitability models which represent the two divergent positions towards wind power development in Massachusetts, and a third, balanced model. The third study determines that exurban residential development is not the only land use factor that reduces wind power development potential in Western Massachusetts. A set of Boolean GIS models for 1985 and 2009 find the onset of conservation easements on private lands having the largest impact. During this 25 year period a combination of land use conversion and land conservation has reduced the access to prime wind resource areas by 18% (11,601 hectares), an equivalent loss of 5,800--8,700 GWh/year of zero carbon electricity generation. The six main findings from this research are: (1) Visual aesthetics remain the main factor of opposition to specific projects; (2) The Not-in-my Backyard debate for wind power remains unsettled; (3) Widespread support exists for regional land use energy plans; (4) The wind resources of Western Massachusetts can significantly contribute to the state's current renewable portfolio standard while balancing conservation and renewable energy development objectives; However, (5) a combination of exurban residential development and conservation easements significantly reduces wind power development potential over time; and (6) a need exists to legally define wind as a publicly beneficial resource.
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Zscheischler, Jakob; Fatichi, Simone; Wolf, Sebastian; ...
2016-08-08
Ecosystem models often perform poorly in reproducing interannual variability in carbon and water fluxes, resulting in considerable uncertainty when estimating the land-carbon sink. While many aggregated variables (growing season length, seasonal precipitation, or temperature) have been suggested as predictors for interannual variability in carbon fluxes, their explanatory power is limited and uncertainties remain as to their relative contributions. Recent results show that the annual count of hours where evapotranspiration (ET) is larger than its 95th percentile is strongly correlated with the annual variability of ET and gross primary production (GPP) in an ecosystem model. This suggests that the occurrence ofmore » favorable conditions has a strong influence on the annual carbon budget. Here we analyzed data from eight forest sites of the AmeriFlux network with at least 7 years of continuous measurements. We show that for ET and the carbon fluxes GPP, ecosystem respiration (RE), and net ecosystem production, counting the “most active hours/days” (i.e., hours/days when the flux exceeds a high percentile) correlates well with the respective annual sums, with correlation coefficients generally larger than 0.8. Phenological transitions have much weaker explanatory power. By exploiting the relationship between most active hours and interannual variability, we classify hours as most active or less active and largely explain interannual variability in ecosystem fluxes, particularly for GPP and RE. Our results suggest that a better understanding and modeling of the occurrence of large values in high-frequency ecosystem fluxes will result in a better understanding of interannual variability of these fluxes.« less
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2010-01-01
The Regional Short-Term Energy Model (RSTEM) uses macroeconomic variables such as income, employment, industrial production and consumer prices at both the national and regional1 levels as explanatory variables in the generation of the Short-Term Energy Outlook (STEO). This documentation explains how national macroeconomic forecasts are used to update regional macroeconomic forecasts through the RSTEM Macro Bridge procedure.
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Army College Fund Cost-Effectiveness Study
1990-11-01
Section A.2 presents a theory of enlistment supply to provide a basis for specifying the regression model , The model Is specified in Section A.3, which...Supplementary materials are included in the final four sections. Section A.6 provides annual trends in the regression model variables. Estimates of the model ...millions, A.S. ESTIMATION OF A YOUTH EARNINGS FORECASTING MODEL Civilian pay is an important explanatory variable in the regression model . Previous
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Aguado, Alba; López, Flora; Miravet, Sonia; Oriol, Pilar; Fuentes, M Isabel; Henares, Belén; Badia, Teresa; Esteve, Lluis; Peligro, Javier
2009-05-08
Information on hypertension in the very elderly is sparse. Until recently evidence of benefits from pharmacological treatment was inconclusive. We estimated the prevalence of hypertension in subjects aged 80 or more, the proportion of awareness, treatment and control. Explanatory variables associated with good control were also studied. Cross sectional, population-based study, conducted in Martorell, an urban Spanish municipality, in 2005. By simple random sampling from the census, 323 subjects aged 80 or more were included. Patients were visited at home or in the geriatric institution and after giving informed consent, the study variables were collected. These included: supine and standing blood pressure and information about diagnosis and treatment of hypertension. The estimation and 95% confidence interval were obtained and a logistic regression model was used to study explanatory variables associated with blood pressure below 140/90 mm Hg. The prevalence of hypertension was 72.8% (95%CI: 69.5-76.6%) and 93% of the patients were aware of this condition, of whom 96.3% (95%CI: 93.65-97.9%) had been prescribed pharmacological treatment and 30.7% (95%CI: 25.8 - 36.1%) had blood pressure below 140/90 mm Hg. Some of the patients (43%) had one antihypertensive drug and 39.5% had two in combination. Explanatory variables associated with blood pressure below 140/90 mm Hg included prescription of a diuretic, OR: 0.31 (95%CI: 0.14-0.66), and history of ischemic heart disease, OR: 0.21 (95%CI: 0.1-0.47). The prevalence of hypertension in population aged 80 or more was over 70%. Most patients were aware of this condition and they had antihypertensive medication prescribed. Approximately one third of treated patients had blood pressure below 140/90 mm Hg. Patients with heart disease and with diuretics had more frequently blood pressure below this value.
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Aguado, Alba; López, Flora; Miravet, Sonia; Oriol, Pilar; Fuentes, M Isabel; Henares, Belén; Badia, Teresa; Esteve, Lluis; Peligro, Javier
2009-01-01
Background Information on hypertension in the very elderly is sparse. Until recently evidence of benefits from pharmacological treatment was inconclusive. We estimated the prevalence of hypertension in subjects aged 80 or more, the proportion of awareness, treatment and control. Explanatory variables associated with good control were also studied. Methods Cross sectional, population-based study, conducted in Martorell, an urban Spanish municipality, in 2005. By simple random sampling from the census, 323 subjects aged 80 or more were included. Patients were visited at home or in the geriatric institution and after giving informed consent, the study variables were collected. These included: supine and standing blood pressure and information about diagnosis and treatment of hypertension. The estimation and 95% confidence interval were obtained and a logistic regression model was used to study explanatory variables associated with blood pressure below 140/90 mm Hg. Results The prevalence of hypertension was 72.8% (95%CI: 69.5 – 76.6%) and 93% of the patients were aware of this condition, of whom 96.3% (95%CI: 93.65 – 97.9%) had been prescribed pharmacological treatment and 30.7% (95%CI: 25.8 – 36.1%) had blood pressure below 140/90 mm Hg. Some of the patients (43%) had one antihypertensive drug and 39.5% had two in combination. Explanatory variables associated with blood pressure below 140/90 mm Hg included prescription of a diuretic, OR: 0.31 (95%CI: 0.14 – 0.66), and history of ischemic heart disease, OR: 0.21 (95%CI: 0.1 – 0.47). Conclusion The prevalence of hypertension in population aged 80 or more was over 70%. Most patients were aware of this condition and they had antihypertensive medication prescribed. Approximately one third of treated patients had blood pressure below 140/90 mm Hg. Patients with heart disease and with diuretics had more frequently blood pressure below this value. PMID:19426484
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Shahinfar, Saleh; Page, David; Guenther, Jerry; Cabrera, Victor; Fricke, Paul; Weigel, Kent
2014-02-01
When making the decision about whether or not to breed a given cow, knowledge about the expected outcome would have an economic impact on profitability of the breeding program and net income of the farm. The outcome of each breeding can be affected by many management and physiological features that vary between farms and interact with each other. Hence, the ability of machine learning algorithms to accommodate complex relationships in the data and missing values for explanatory variables makes these algorithms well suited for investigation of reproduction performance in dairy cattle. The objective of this study was to develop a user-friendly and intuitive on-farm tool to help farmers make reproduction management decisions. Several different machine learning algorithms were applied to predict the insemination outcomes of individual cows based on phenotypic and genotypic data. Data from 26 dairy farms in the Alta Genetics (Watertown, WI) Advantage Progeny Testing Program were used, representing a 10-yr period from 2000 to 2010. Health, reproduction, and production data were extracted from on-farm dairy management software, and estimated breeding values were downloaded from the US Department of Agriculture Agricultural Research Service Animal Improvement Programs Laboratory (Beltsville, MD) database. The edited data set consisted of 129,245 breeding records from primiparous Holstein cows and 195,128 breeding records from multiparous Holstein cows. Each data point in the final data set included 23 and 25 explanatory variables and 1 binary outcome for of 0.756 ± 0.005 and 0.736 ± 0.005 for primiparous and multiparous cows, respectively. The naïve Bayes algorithm, Bayesian network, and decision tree algorithms showed somewhat poorer classification performance. An information-based variable selection procedure identified herd average conception rate, incidence of ketosis, number of previous (failed) inseminations, days in milk at breeding, and mastitis as the most effective explanatory variables in predicting pregnancy outcome. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
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Pineault, Raynald; Borgès Da Silva, Roxane; Prud'homme, Alexandre; Fournier, Michel; Couture, Audrey; Provost, Sylvie; Levesque, Jean-Frédéric
2014-05-21
Healthcare reforms initiated in the early 2000s in Québec involved the implementation of new modes of primary healthcare (PHC) delivery and the creation of Health and Social Services Centers (HSSCs) to support it. The objective of this article is to assess and explain the degree of PHC organizational change achieved following these reforms. We conducted two surveys of PHC organizations, in 2005 and 2010, in two regions of the province of Québec, Canada. From the responses to these surveys, we derived a measure of organizational change based on an index of conformity to an ideal type (ICIT). One set of explanatory variables was contextual, related to coercive, normative and mimetic influences; the other consisted of organizational variables that measured receptivity towards new PHC models. Multilevel analyses were performed to examine the relationships between ICIT change in the post-reform period and the explanatory variables. Positive results were attained, as expressed by increase in the ICIT score in the post-reform period, mainly due to implementation of new types of PHC organizations (Family Medicine Groups and Network Clinics). Organizational receptivity was the main explanatory variable mediating the effect of coercive and mimetic influences. Normative influence was not a significant factor in explaining changes. Changes were modest at the system level but important with regard to new forms of PHC organizations. The top-down decreed reform was a determining factor in initiating change whereas local coercive and normative influences did not play a major role. The exemplar role played by certain PHC organizations through mimetic influence was more important. Receptivity of individual organizations was both a necessary condition and a mediating factor in influencing change. This supports the view that a combination of top-down and bottom-up strategy is best suited for achieving substantial changes in PHC local organization.
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2014-01-01
Background Healthcare reforms initiated in the early 2000s in Québec involved the implementation of new modes of primary healthcare (PHC) delivery and the creation of Health and Social Services Centers (HSSCs) to support it. The objective of this article is to assess and explain the degree of PHC organizational change achieved following these reforms. Methods We conducted two surveys of PHC organizations, in 2005 and 2010, in two regions of the province of Québec, Canada. From the responses to these surveys, we derived a measure of organizational change based on an index of conformity to an ideal type (ICIT). One set of explanatory variables was contextual, related to coercive, normative and mimetic influences; the other consisted of organizational variables that measured receptivity towards new PHC models. Multilevel analyses were performed to examine the relationships between ICIT change in the post-reform period and the explanatory variables. Results Positive results were attained, as expressed by increase in the ICIT score in the post-reform period, mainly due to implementation of new types of PHC organizations (Family Medicine Groups and Network Clinics). Organizational receptivity was the main explanatory variable mediating the effect of coercive and mimetic influences. Normative influence was not a significant factor in explaining changes. Conclusion Changes were modest at the system level but important with regard to new forms of PHC organizations. The top-down decreed reform was a determining factor in initiating change whereas local coercive and normative influences did not play a major role. The exemplar role played by certain PHC organizations through mimetic influence was more important. Receptivity of individual organizations was both a necessary condition and a mediating factor in influencing change. This supports the view that a combination of top-down and bottom-up strategy is best suited for achieving substantial changes in PHC local organization. PMID:24886490
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Burke, Morgen W V; Shahabi, Mojtaba; Xu, Yeqian; Zheng, Haochi; Zhang, Xiaodong; VanLooy, Jeffrey
2018-05-22
Studies have shown that the agricultural expansion and land use changes in the Midwest of the U.S. are major drivers for increased nonpoint source pollution throughout the regional river systems. In this study, we empirically examined the relationship of planted area and production of three dominant crops with nitrate flux in the Republican River, Kansas, a sub-watershed of Mississippi River Basin. Our results show that land use in the region could not explain the observed changes in nitrate flux in the river. Instead, after including explanatory variables such as precipitation, growing degree days, and well water irrigation in the regression model we found that irrigation and spring precipitation could explain >85% of the variability in nitrate flux from 2000 to 2014. This suggests that changes in crop acreage and production alone cannot explain variability in nitrate flux. Future agricultural policy for the region should focus on controlling both the timing and amount of fertilizer applied to the field to reduce the potential leaching of excess fertilizer through spring time runoff and/or over-irrigation into nearby river systems.
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Consumer-operated service program members' explanatory models of mental illness and recovery.
Hoy, Janet M
2014-10-01
Incorporating individuals' understandings and explanations of mental illness into service delivery offers benefits relating to increased service relevance and meaning. Existing research delineates explanatory models of mental illness held by individuals in home, outpatient, and hospital-based contexts; research on models held by those in peer-support contexts is notably absent. In this article, I describe themes identified within and across explanatory models of mental illness and recovery held by mental health consumers (N = 24) at one peer center, referred to as a consumer-operated service center (COSP). Participants held explanatory models inclusive of both developmental stressors and biomedical causes, consistent with a stress-diathesis model (although no participant explicitly referenced such). Explicit incorporation of stress-diathesis constructs into programming at this COSP offers the potential of increasing service meaning and relevance. Identifying and incorporating shared meanings across individuals' understandings of mental illness likewise can increase relevance and meaning for particular subgroups of service users. © The Author(s) 2014.
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Perkins, Jessica M; Lee, Hwa-Young; James, K S; Oh, Juhwan; Krishna, Aditi; Heo, Jongho; Lee, Jong-Koo; Subramanian, S V
2016-09-30
Previous research has demonstrated health benefits of marriage and the potential for worse outcomes during widowhood in some populations. However, few studies have assessed the relevance of widowhood and widowhood duration to a variety of health-related outcomes and chronic diseases among older adults in India, and even fewer have examined these relationships stratified by gender. Using a cross-sectional representative sample of 9,615 adults aged 60 years or older from 7 states in diverse regions of India, we examine the relationship between widowhood and self-rated health, psychological distress, cognitive ability, and four chronic diseases before and after adjusting for demographic characteristics, socioeconomic status, living with children, and rural-urban location for men and women, separately. We then assess these associations when widowhood accounts for duration. Being widowed as opposed to married was associated with worse health outcomes for women after adjusting for other explanatory factors. Widowhood in general was not associated with any outcomes for men except for cognitive ability, though men who were widowed within 0-4 years were at greater risk for diabetes compared to married men. Moreover, recently widowed women and women who were widowed long-term were more likely to experience psychological distress, worse self-rated health, and hypertension, even after adjusting for other explanatory variables, whereas women widowed 5-9 years were not, compared to married women. Gender, the duration of widowhood, and type of outcome are each relevant pieces of information when assessing the potential for widowhood to negatively impact health. Future research should explore how the mechanisms linking widowhood to health vary over the course of widowhood. Incorporating information about marital relationships into the design of intervention programs may help better target potential beneficiaries among older adults in India.
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Temporal self-regulation theory: a neurobiologically informed model for physical activity behavior
Hall, Peter A.; Fong, Geoffrey T.
2015-01-01
Dominant explanatory models for physical activity behavior are limited by the exclusion of several important components, including temporal dynamics, ecological forces, and neurobiological factors. The latter may be a critical omission, given the relevance of several aspects of cognitive function for the self-regulatory processes that are likely required for consistent implementation of physical activity behavior in everyday life. This narrative review introduces temporal self-regulation theory (TST; Hall and Fong, 2007, 2013) as a new explanatory model for physical activity behavior. Important features of the model include consideration of the default status of the physical activity behavior, as well as the disproportionate influence of temporally proximal behavioral contingencies. Most importantly, the TST model proposes positive feedback loops linking executive function (EF) and the performance of physical activity behavior. Specifically, those with relatively stronger executive control (and optimized brain structures supporting it, such as the dorsolateral prefrontal cortex (PFC)) are able to implement physical activity with more consistency than others, which in turn serves to strengthen the executive control network itself. The TST model has the potential to explain everyday variants of incidental physical activity, sport-related excellence via capacity for deliberate practice, and variability in the propensity to schedule and implement exercise routines. PMID:25859196
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Social determinants of childhood asthma symptoms: an ecological study in urban Latin America.
Fattore, Gisel L; Santos, Carlos A T; Barreto, Mauricio L
2014-04-01
Asthma is an important public health problem in urban Latin America. This study aimed to analyze the role of socioeconomic and environmental factors as potential determinants of asthma symptoms prevalence in children from Latin American (LA) urban centers. We selected 31 LA urban centers with complete data, and an ecological analysis was performed. According to our theoretical framework, the explanatory variables were classified in three levels: distal, intermediate, and proximate. The association between variables in the three levels and prevalence of asthma symptoms was examined by bivariate and multivariate linear regression analysis weighed by sample size. In a second stage, we fitted several linear regression models introducing sequentially the variables according to the predefined hierarchy. In the final hierarchical model Gini Index, crowding, sanitation, variation in infant mortality rates and homicide rates, explained great part of the variance in asthma prevalence between centers (R(2) = 75.0 %). We found a strong association between socioeconomic and environmental variables and prevalence of asthma symptoms in LA urban children, and according to our hierarchical framework and the results found we suggest that social inequalities (measured by the Gini Index) is a central determinant to explain high prevalence of asthma in LA.
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[Analysis of the technical efficiency of hospitals in the Spanish National Health Service].
Pérez-Romero, Carmen; Ortega-Díaz, M Isabel; Ocaña-Riola, Ricardo; Martín-Martín, José Jesús
To analyse the technical efficiency and productivity of general hospitals in the Spanish National Health Service (NHS) (2010-2012) and identify explanatory hospital and regional variables. 230 NHS hospitals were analysed by data envelopment analysis for overall, technical and scale efficiency, and Malmquist index. The robustness of the analysis is contrasted with alternative input-output models. A fixed effects multilevel cross-sectional linear model was used to analyse the explanatory efficiency variables. The average rate of overall technical efficiency (OTE) was 0.736 in 2012; there was considerable variability by region. Malmquist index (2010-2012) is 1.013. A 23% variability in OTE is attributable to the region in question. Statistically significant exogenous variables (residents per 100 physicians, aging index, average annual income per household, essential public service expenditure and public health expenditure per capita) explain 42% of the OTE variability between hospitals and 64% between regions. The number of residents showed a statistically significant relationship. As regards regions, there is a statistically significant direct linear association between OTE and annual income per capita and essential public service expenditure, and an indirect association with the aging index and annual public health expenditure per capita. The significant room for improvement in the efficiency of hospitals is conditioned by region-specific characteristics, specifically aging, wealth and the public expenditure policies of each one. Copyright © 2016 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.
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Lequy, Emeline; Saby, Nicolas P A; Ilyin, Ilia; Bourin, Aude; Sauvage, Stéphane; Leblond, Sébastien
2017-07-15
Air pollution in trace elements (TE) remains a concern for public health in Europe. For this reasons, networks of air pollution concentrations or exposure are deployed, including a moss bio-monitoring programme in Europe. Spatial determinants of TE concentrations in mosses remain unclear. In this study, the French dataset of TE in mosses is analyzed by spatial autoregressive model to account for spatial structure of the data and several variables proven or suspected to affect TE concentrations in mosses. Such variables include source (atmospheric deposition and soil concentrations), protocol (sampling month, collector, and moss species), and environment (forest type and canopy density, distance to the coast or the highway, and elevation). Modeled atmospheric deposition was only available for Cd and Pb and was one of the main explanatory variables of the concentrations in mosses. Predicted soil content was also an important explanatory variable except for Cr, Ni, and Zn. However, the moss species was the main factor for all the studied TE. The other environmental variables affected differently the TE. In particular, the forest type and canopy density were important in most cases. These results stress the need for further research on the effect of the moss species on the capture and retention of TE, as well as for accounting for several variables and the spatial structure of the data in statistical analyses. Copyright © 2017 Elsevier B.V. All rights reserved.
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Fernández-Antelo, Inmaculada; Cuadrado-Gordillo, Isabel
2018-04-01
The controversies that exist regarding the delimitation of the cyberbullying construct demonstrate the need for further research focused on determining the criteria that shape the structure of the perceptions that adolescents have of this phenomenon and on seeking explanations of this behavior. The objectives of this study were to (a) construct possible explanatory models of the perception of cyberbullying from identifying and relating the criteria that form this construct and (b) analyze the influence of previous cyber victimization and cyber aggression experiences in the construction of explanatory models of the perception of cyberbullying. The sample consisted of 2,148 adolescents (49.1% girls; SD = 0.5) aged from 12 to 16 years ( M = 13.9 years; SD = 1.2). The results have shown that previous cyber victimization and cyber aggression experiences lead to major differences in the explanatory models to interpret cyber-abusive behavior as cyberbullying episodes, or as social relationship mechanisms, or as a revenge reaction. We note that the aggressors' explanatory model is based primarily on a strong reciprocal relationship between the imbalance of power and intentionality, that it functions as a link promoting indirect causal relationships of the anonymity and repetition factors with the cyberbullying construct. The victims' perceptual structure is based on three criteria-imbalance of power, intentionality, and publicity-where the key factor in this structure is the intention to harm. These results allow to design more effective measures of prevention and intervention closely tailored to addressing directly the factors that are considered to be predictors of risk.
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The Determinants of College Student Retention
ERIC Educational Resources Information Center
Guerrero, Adam A.
2010-01-01
This study attempts to add to the college student dropout literature by examining persistence decisions at private, non-selective university using previously unstudied explanatory variables and advanced econometric methods. Three main contributions are provided. First, proprietary data obtained from a type of university that is underrepresented in…
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Optical Properties of Three Beach Waters: Implications for Predictive Modeling of Enterococci
Sunlight plays an important role in the inactivation of fecal indicator bacteria in recreational waters. Solar radiation can explain temporal trends in bacterial counts and is commonly used as an explanatory variable in predictive models. Broadband surface radiation provides a ba...
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Predictive Modeling of a Fecal Indicator at a Subtropical Marine Beach
The Virtual Beach Model Builder (VBMB) is a software tool that can be used to develop predictive models at beaches based on microbial data and observations (explanatory variables) that describe hydrometeorological and biogeochemical conditions. During the summer of 2008, a study...
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Andersen, Claus E; Raaschou-Nielsen, Ole; Andersen, Helle Primdal; Lind, Morten; Gravesen, Peter; Thomsen, Birthe L; Ulbak, Kaare
2007-01-01
A linear regression model has been developed for the prediction of indoor (222)Rn in Danish houses. The model provides proxy radon concentrations for about 21,000 houses in a Danish case-control study on the possible association between residential radon and childhood cancer (primarily leukaemia). The model was calibrated against radon measurements in 3116 houses. An independent dataset with 788 house measurements was used for model performance assessment. The model includes nine explanatory variables, of which the most important ones are house type and geology. All explanatory variables are available from central databases. The model was fitted to log-transformed radon concentrations and it has an R(2) of 40%. The uncertainty associated with individual predictions of (untransformed) radon concentrations is about a factor of 2.0 (one standard deviation). The comparison with the independent test data shows that the model makes sound predictions and that errors of radon predictions are only weakly correlated with the estimates themselves (R(2) = 10%).
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NASA Astrophysics Data System (ADS)
Räsänen, Aleksi; Juutinen, Sari; Aurela, Mika; Virtanen, Tarmo
2017-04-01
Biomass is one of the central bio-geophysical variables in Earth observation for tracking plant productivity, and flow of carbon, nutrients, and water. Most of the satellite based biomass mapping exercises in Arctic environments have been performed by using rather coarse spatial resolution data, e.g. Landsat and AVHRR which have spatial resolutions of 30 m and >1 km, respectively. While the coarse resolution images have high temporal resolution, they are incapable of capturing the fragmented nature of tundra environment and fine-scale changes in vegetation and carbon exchange patterns. Very high spatial resolution (VHSR, spatial resolution 0.5-2 m) satellite images have the potential to detect environmental variables with an ecologically sound spatial resolution. The usage of VHSR images has, nevertheless, been modest so far in biomass modeling in the Arctic. Our objectives were to use VHSR for predicting above ground biomass in tundra landscapes, evaluate whether a common predictive model can be applied across circum-Arctic tundra and peatland sites having different types of vegetation, and produce knowledge on distribution of plant functional types (PFT) in these sites. Such model development is dependent on ground-based surveys of vegetation with the same spatial resolution and extent with the VHSR images. In this study, we conducted ground-based surveys of vegetation composition and biomass in four different arctic tundra or peatland areas located in Russia, Canada, and Finland. First, we sorted species into PFTs and developed PFT-specific models to predict biomass on the basis of non-destructive measurements (cover, height). Second, we predicted overall biomass on landscape scale by combinations of single bands and vegetation indices of very high resolution satellite images (QuickBird or WorldView-2 images of the eight sites). We compared area-specific empirical regression models and common models that were applied across all sites. We found that NDVI was usually the highest scoring spectral indices in explaining biomass distribution with good explanatory power. Furthermore, models which had more than one explanatory variable had higher explanatory power than models with a single index. The dissimilarity between common and site-specific model estimates was, however, high and data indicates that variation in vegetation properties and its impact on spectral reflectance needs to be acknowledged. Our work produced knowledge on above-ground biomass distribution and contribution of PFTs across circum-Arctic low-growth landscapes and will contribute to developing space-borne vegetation monitoring schemes utilizing VHSR satellite images.
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Relevance of the Paraná River hydrology on the fluvial water quality of the Delta Biosphere Reserve.
Puig, Alba; Olguín Salinas, Héctor F; Borús, Juan A
2016-06-01
The increasing frequency of extreme events in large rivers may affect not only their flow, but also their water quality. In the present study, spatial and temporal changes in fluvial physico-chemical variables were analyzed in a mega-river delta during two extreme hydrological years (La Niña-El Niño) and related to potential explanatory factors. Basic water variables were evaluated in situ at 13 points (distant 2-35 km from each other) in watercourses of the Delta Biosphere Reserve (890 km(2)) in the Lower Paraná River (Argentina) in nine surveys (October 2008-July 2010) without meteorological tides. Samples for laboratory analyses were collected from each main river. Multivariate tests by permutations were applied. The period studied was influenced by a drought, within a long period dominated by low flows combined with dry weather and wildfires, and a large (10 years of recurrence) and prolonged (7 months) flood. The hydrological phase, followed by the season and the hydrological year (according to the ENSO event) were the principal explanatory factors of the main water quality changes, whereas the drainage sub-basin and the fluvial environment (river or stream) were secondary explanatory factors. During the drought period, conductivity, turbidity, and associated variables (e.g., major ions, silicon, and iron concentrations) were maximal, whereas real color was minimal. In the overbanking flood phase, pH and dissolved oxygen concentration were minimal, whereas real color was maximal. Dissolved oxygen saturation was also low in the receding flood phase and total major ion load doubled after the arrival of the overbanking stage. The water quality of these watercourses may be affected by the combination of several influences, such as the Paraná River flow, the pulses with sediments and solutes from the Bermejo River, the export of the Delta floodplain properties mainly by the flood, the season, and the saline tributaries to the Lower Paraná River. The high influence of the hydrology of this large river on the Delta fluvial water quality emphasizes the relevance of changes in its flow regime in recent decades, such as the seasonality attenuation. Considering that the effects of extreme events differ among and within fluvial systems, specific ecohydrological evaluations and powerful appropriate statistics are key tools to gain knowledge on these systems and to provide bases for suitable management measures in a scenario of climate change and increasing human alterations and demands.
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NASA Astrophysics Data System (ADS)
Felkner, John Sames
The scale and extent of global land use change is massive, and has potentially powerful effects on the global climate and global atmospheric composition (Turner & Meyer, 1994). Because of this tremendous change and impact, there is an urgent need for quantitative, empirical models of land use change, especially predictive models with an ability to capture the trajectories of change (Agarwal, Green, Grove, Evans, & Schweik, 2000; Lambin et al., 1999). For this research, a spatial statistical predictive model of land use change was created and run in two provinces of Thailand. The model utilized an extensive spatial database, and used a classification tree approach for explanatory model creation and future land use (Breiman, Friedman, Olshen, & Stone, 1984). Eight input variables were used, and the trees were run on a dependent variable of land use change measured from 1979 to 1989 using classified satellite imagery. The derived tree models were used to create probability of change surfaces, and these were then used to create predicted land cover maps for 1999. These predicted 1999 maps were compared with actual 1999 landcover derived from 1999 Landsat 7 imagery. The primary research hypothesis was that an explanatory model using both economic and environmental input variables would better predict future land use change than would either a model using only economic variables or a model using only environmental. Thus, the eight input variables included four economic and four environmental variables. The results indicated a very slight superiority of the full models to predict future agricultural change and future deforestation, but a slight superiority of the economic models to predict future built change. However, the margins of superiority were too small to be statistically significant. The resulting tree structures were used, however, to derive a series of principles or "rules" governing land use change in both provinces. The model was able to predict future land use, given a series of assumptions, with 90 percent overall accuracies. The model can be used in other developing or developed country locations for future land use prediction, determination of future threatened areas, or to derive "rules" or principles driving land use change.
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Understanding logistic regression analysis.
Sperandei, Sandro
2014-01-01
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.
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A geospatial model of ambient sound pressure levels in the contiguous United States.
Mennitt, Daniel; Sherrill, Kirk; Fristrup, Kurt
2014-05-01
This paper presents a model that predicts measured sound pressure levels using geospatial features such as topography, climate, hydrology, and anthropogenic activity. The model utilizes random forest, a tree-based machine learning algorithm, which does not incorporate a priori knowledge of source characteristics or propagation mechanics. The response data encompasses 270 000 h of acoustical measurements from 190 sites located in National Parks across the contiguous United States. The explanatory variables were derived from national geospatial data layers and cross validation procedures were used to evaluate model performance and identify variables with predictive power. Using the model, the effects of individual explanatory variables on sound pressure level were isolated and quantified to reveal systematic trends across environmental gradients. Model performance varies by the acoustical metric of interest; the seasonal L50 can be predicted with a median absolute deviation of approximately 3 dB. The primary application for this model is to generalize point measurements to maps expressing spatial variation in ambient sound levels. An example of this mapping capability is presented for Zion National Park and Cedar Breaks National Monument in southwestern Utah.
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Modification of the Integrated Sasang Constitutional Diagnostic Model
Nam, Jiho
2017-01-01
In 2012, the Korea Institute of Oriental Medicine proposed an objective and comprehensive physical diagnostic model to address quantification problems in the existing Sasang constitutional diagnostic method. However, certain issues have been raised regarding a revision of the proposed diagnostic model. In this paper, we propose various methodological approaches to address the problems of the previous diagnostic model. Firstly, more useful variables are selected in each component. Secondly, the least absolute shrinkage and selection operator is used to reduce multicollinearity without the modification of explanatory variables. Thirdly, proportions of SC types and age are considered to construct individual diagnostic models and classify the training set and the test set for reflecting the characteristics of the entire dataset. Finally, an integrated model is constructed with explanatory variables of individual diagnosis models. The proposed integrated diagnostic model significantly improves the sensitivities for both the male SY type (36.4% → 62.0%) and the female SE type (43.7% → 64.5%), which were areas of limitation of the previous integrated diagnostic model. The ideas of these new algorithms are expected to contribute not only to the scientific development of Sasang constitutional medicine in Korea but also to that of other diagnostic methods for traditional medicine. PMID:29317897
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Huo, Hong; Feng, Qi; Su, Yong-hong
2014-01-01
Understanding the factors that influence the distribution of understory vegetation is important for biological conservation and forest management. We compared understory species composition by multi-response permutation procedure and indicator species analysis between plots dominated by Qinghai spruce (Picea crassifolia Kom.) and Qilian juniper (Sabina przewalskii Kom.) in coniferous forests of the Qilian Mountains, northwestern China. Understory species composition differed markedly between the forest types. Many heliophilous species were significantly associated with juniper forest, while only one species was indicative of spruce forest. Using constrained ordination and the variation partitioning model, we quantitatively assessed the relative effects of two sets of explanatory variables on understory species composition. The results showed that topographic variables had higher explanatory power than did site conditions for understory plant distributions. However, a large amount of the variation in understory species composition remained unexplained. Forward selection revealed that understory species distributions were primarily affected by elevation and aspect. Juniper forest had higher species richness and α-diversity and lower β-diversity in the herb layer of the understory plant community than spruce forest, suggesting that the former may be more important in maintaining understory biodiversity and community stability in alpine coniferous forest ecosystems.
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NASA Technical Reports Server (NTRS)
Chylack, Leo T.; Peterson, Leif E.; Feiveson, Alan H.; Wear, Mary; Manuel, F. Keith
2007-01-01
The NASA Study of Cataract in Astronauts (NASCA) is a five-year, multi-centered, investigation of lens opacification in populations of U.S. astronauts, military pilots, and ground-based (nonaviator) comparison participants. For astronauts, the explanatory variable of most interest is radiation exposure during space flight, however to properly evaluate its effect, the secondary effects of age, nutrition, general health, solar ocular exposure, and other confounding variables encountered in non-space flight must also be considered. NASCA contains an initial baseline, cross-sectional objective assessment of the severity of cortical (C), nuclear (N), and posterior subcapsular (PSC) lens opacification, and annual follow-on assessments of severity and progression of these opacities in the population of astronauts and in participants sampled from populations of military pilots and ground-based exposure controls. From these data, NASCA will estimate the degree to which space radiation affects lens opacification for astronauts and how the overall risks of each cataract type for astronauts compared with those of the other exposure control groups after adjusting for differences in age and other explanatory variables.
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An outline of graphical Markov models in dentistry.
Helfenstein, U; Steiner, M; Menghini, G
1999-12-01
In the usual multiple regression model there is one response variable and one block of several explanatory variables. In contrast, in reality there may be a block of several possibly interacting response variables one would like to explain. In addition, the explanatory variables may split into a sequence of several blocks, each block containing several interacting variables. The variables in the second block are explained by those in the first block; the variables in the third block by those in the first and the second block etc. During recent years methods have been developed allowing analysis of problems where the data set has the above complex structure. The models involved are called graphical models or graphical Markov models. The main result of an analysis is a picture, a conditional independence graph with precise statistical meaning, consisting of circles representing variables and lines or arrows representing significant conditional associations. The absence of a line between two circles signifies that the corresponding two variables are independent conditional on the presence of other variables in the model. An example from epidemiology is presented in order to demonstrate application and use of the models. The data set in the example has a complex structure consisting of successive blocks: the variable in the first block is year of investigation; the variables in the second block are age and gender; the variables in the third block are indices of calculus, gingivitis and mutans streptococci and the final response variables in the fourth block are different indices of caries. Since the statistical methods may not be easily accessible to dentists, this article presents them in an introductory form. Graphical models may be of great value to dentists in allowing analysis and visualisation of complex structured multivariate data sets consisting of a sequence of blocks of interacting variables and, in particular, several possibly interacting responses in the final block.
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Key factors affecting urban runoff pollution under cold climatic conditions
NASA Astrophysics Data System (ADS)
Valtanen, Marjo; Sillanpää, Nora; Setälä, Heikki
2015-10-01
Urban runoff contains various pollutants and has the potential of deteriorating the quality of aquatic ecosystems. In this study our objective is to shed light on the factors that control the runoff water quality in urbanized catchments. The effects of runoff event characteristics, land use type and catchment imperviousness on event mass loads (EML) and event mean concentrations (EMC) were studied during warm and cold periods in three study catchments (6.1, 6.5 and 12.6 ha in size) in the city of Lahti, Finland. Runoff and rainfall were measured continuously for two years at each catchment. Runoff samples were taken for total nutrients (tot-P and tot-N), total suspended solids (TSS), heavy metals (Zn, Cr, Al, Co, Ni, Cu, Pb, Mn) and total organic carbon (TOC). Stepwise multiple linear regression analysis (SMLR) was used to identify general relationships between the following variables: event water quality, runoff event characteristics and catchment characteristics. In general, the studied variables explained 50-90% of the EMLs but only 30-60% of the EMCs, with runoff duration having an important role in most of the SMLR models. Mean runoff intensity or peak flow was also often included in the runoff quality models. Yet, the importance (being the first, second or third best) and role (negative or positive impact) of the explanatory variables varied between the cold and warm period. Land use type often explained cold period concentrations, but imperviousness alone explained EMCs weakly. As for EMLs, the influence of imperviousness and/or land use was season and pollutant dependent. The study suggests that pollutant loads can be - throughout the year - adequately predicted by runoff characteristics given that seasonal differences are taken into account. Although pollutant concentrations were sensitive to variation in seasonal and catchment conditions as well, the accurate estimation of EMCs would require a more complete set of explanatory factors than used in this study.
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Predicting cerulean warbler habitat use in the Cumberland Mountains of Tennessee
DOE Office of Scientific and Technical Information (OSTI.GOV)
Buehler, D.A.; Welton, M.J.; Beachy, T.A.
2006-12-15
We developed a habitat model to predict cerulean warbler (Dendroica cerulea) habitat availability in the Cumberland Mountains of eastern Tennessee. We used 7 remotely sensed vegetation and topographic landform explanatory variables and known locations of territorial male cerulean warblers mapped in 2003 as the response variable to develop a Mahalanobis distance statistic model of potential habitat. We evaluated the accuracy of the model based on field surveys for ceruleans during the 2004 breeding season. The model performed well with an 80% correct classification of cerulean presence based on the validation data, although prediction of absence was only 54% correct. Wemore » extrapolated from potential habitat to cerulean abundance based on density estimates from territory mapping on 8 20-ha plots in 2005. Over the 200,000-ha study area, we estimated there were 80,584 ha of potential habitat, capable of supporting about 36,500 breeding pairs. We applied the model to the 21,609-ha state-owned Royal Blue Wildlife Management Area to evaluate the potential effects of coal surface mining as one example of a potential conflict between land use and cerulean warbler conservation. Our models suggest coal surface mining could remove 2,954 ha of cerulean habitat on Royal Blue Wildlife Management Area and could displace 2,540 breeding pairs (23% of the Royal Blue population). A comprehensive conservation strategy is needed to address potential and realized habitat loss and degradation on the breeding grounds, during migration, and on the wintering grounds.« less
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Márquez, Ana L.; Real, Raimundo; Kin, Marta S.; Guerrero, José Carlos; Galván, Betina; Barbosa, A. Márcia; Olivero, Jesús; Palomo, L. Javier; Vargas, J. Mario; Justo, Enrique
2012-01-01
We analysed the main geographical trends of terrestrial mammal species richness (SR) in Argentina, assessing how broad-scale environmental variation (defined by climatic and topographic variables) and the spatial form of the country (defined by spatial filters based on spatial eigenvector mapping (SEVM)) influence the kinds and the numbers of mammal species along these geographical trends. We also evaluated if there are pure geographical trends not accounted for by the environmental or spatial factors. The environmental variables and spatial filters that simultaneously correlated with the geographical variables and SR were considered potential causes of the geographic trends. We performed partial correlations between SR and the geographical variables, maintaining the selected explanatory variables statistically constant, to determine if SR was fully explained by them or if a significant residual geographic pattern remained. All groups and subgroups presented a latitudinal gradient not attributable to the spatial form of the country. Most of these trends were not explained by climate. We used a variation partitioning procedure to quantify the pure geographic trend (PGT) that remained unaccounted for. The PGT was larger for latitudinal than for longitudinal gradients. This suggests that historical or purely geographical causes may also be relevant drivers of these geographical gradients in mammal diversity. PMID:23028254
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Public Views on the Gendering of Mathematics and Related Careers: International Comparisons
ERIC Educational Resources Information Center
Forgasz, Helen; Leder, Gilah; Tan, Hazel
2014-01-01
Mathematics continues to be an enabling discipline for Science, Technology, Engineering, and Mathematics (STEM)-based university studies and related careers. Explanatory models for females' underrepresentation in higher level mathematics and STEM-based courses comprise learner-related and environmental variables--including societal beliefs. Using…
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Explanation and Prediction: Building a Unified Theory of Librarianship, Concept and Review.
ERIC Educational Resources Information Center
McGrath, William E.
2002-01-01
Develops a comprehensive, unified, explanatory theory of librarianship by first making an analogy to the unification of the fundamental forces of nature. Topics include dependent and independent variables; publishing; acquisitions; classification and organization of knowledge; storage, preservation, and collection management; collections; and…
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Adolescent Self-Reported and Peer-Reported Self-Esteem.
ERIC Educational Resources Information Center
O'Donnell, William James
1979-01-01
The study is an examination of the relationship between adolescents' self-reported and peer-reported self-esteem and how this relationship is affected by sex, race, and age variables. Significant sex and race variations interacted with age. Explanatory hypotheses for these findings are given. (Author/KC)
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Student Effort, Consistency, and Online Performance
ERIC Educational Resources Information Center
Patron, Hilde; Lopez, Salvador
2011-01-01
This paper examines how student effort, consistency, motivation, and marginal learning, influence student grades in an online course. We use data from eleven Microeconomics courses taught online for a total of 212 students. Our findings show that consistency, or less time variation, is a statistically significant explanatory variable, whereas…
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Predicting daily use of urban forest recreation sites
John F. Dwyer
1988-01-01
A multiple linear regression model explains 90% of the variance in daily use of an urban recreation site. Explanatory variables include season, day of the week, and weather. The results offer guides for recreation site planning and management as well as suggestions for improving the model.
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Drought tolerance in cacao is mediated by root phenotypic plasticity
USDA-ARS?s Scientific Manuscript database
This study aimed to evaluate phenotypic relationships and their direct and indirect effects through path analysis, and evaluate the use of the phenotypic plasticity index as criteria for the estimation of the basic and explanatory variables used to analysis several cacao progenies subjected to soil ...
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García-Campayo, Javier; Del Hoyo, Yolanda L; Barceló-Soler, Alberto; Navarro-Gil, Mayte; Borao, Luis; Giarin, Veronica; Tovar-Garcia, R Raziel; Montero-Marin, Jesus
2018-01-01
Introduction: Personal wisdom has demonstrated important implications for the health of individuals. The aim of the present study was to validate a Spanish version of the Three-Dimensional Wisdom Scale (3D-WS), exploring the structure of a possible general factor, and assessing its explanatory power on psychological health-related variables. Methods: A cross-sectional study design was used, with a total sample of 624 Spanish participants recruited on the Internet and randomly split into two halves. The following instruments were applied: 3D-WS, Purpose in Life (PIL), Multidimensional State Boredom Scale (MSBS), Positive and Negative Affect Scale (PANAS), and Difficulties in Emotion Regulation Scale (DERS). Factorial structures were analyzed through exploratory and confirmatory factor analysis (EFA and CFA), and the general factor was characterized by using bifactor models. The explanatory power of the 3D-WS was established by multiple regression. Results: The original long and short versions of the 3D-WS were not replicated in the first subsample using EFA, and there was a high rate of cross-loadings. Thus, a new short 3D-WS was proposed by ordering the original items according to factorial weights. This three-correlated-factor (reflective, cognitive, and affective) proposal was tested by means of CFA in the second subsample, with adequate psychometrics and invariance, and a good fit (χ 2 /df = 1.98; CFI = 0.946; RMSEA = 0.056; 90% CI = 0.040-0.072). A bifactor structure, in which the reflective trait of wisdom was integrated into a general factor (G-Reflective) improved the model fit (χ 2 /df = 1.85; CFI = 0.959; RMSEA = 0.052; 90% CI = 0.035-0.070). The explained common variance of G-Reflective was 0.53; therefore, the new short 3D-WS should not be considered essentially unidimensional. The new short 3D-WS showed positive relationships with the PIL and PANAS-positive, and negative associations with the MSBS, PANAS-negative and DERS, contributing to explain all the referred variables. These results were consistent across subsamples. Conclusion: The new short 3D-WS appears to be a reliable instrument for measuring wisdom in the Spanish general population. The reflective facet might influence the cognitive and affective wisdom components through the G-Reflective general factor. There seems to be a high explanatory power of the 3D-WS on psychological health-related variables. This study will facilitate the development of future research and psychological knowledge regarding wisdom.
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2012-01-01
Background The classic determination of burnout is by means of the dimensions exhaustion, cynicism and inefficacy. A new definition of the syndrome is based on clinical subtypes, consisting of “frenetic” (involved, ambitious, overloaded), “underchallenged” (indifferent, bored, with lack of personal development) and “worn-out” (neglectful, unacknowledged, with little control). The dimensions of overload, lack of development and neglect form a shortened version of this perspective. The aims of this study were to estimate and to compare the explanatory power of both typological models, short and long, with the standard measurement. Methods This was a cross-sectional survey with a randomly sample of university employees (n=409). Multivariate linear regression models were constructed between the “Maslach Burnout Inventory General Survey” (MBI-GS) dimensions, as dependent variables, and the “Burnout Clinical Subtype Questionnaire” (BCSQ-36 and BCSQ-12) dimensions, as independent variables. Results The BCSQ-36 subscales together explained 53% of ‘exhaustion’ (p<0.001), 59% of ‘cynicism’ (p<0.001) and 37% of ‘efficacy’ (p<0.001), while BCSQ-12 subscales explained 44% of ‘exhaustion’ (p<0.001), 44% of ‘cynicism’ (p<0.001), and 30% of ‘efficacy’ (p<0.001). The difference in the explanatory power of both models was significant for ‘exhaustion’ (p<0.001), and for ‘cynicism’ (p<0.001) and ‘efficacy (p<0.001). Conclusions Both BCSQ-36 and BCSQ-12 demonstrate great explanatory power over the standard MBI-GS, while offering a useful characterization of the syndrome for the evaluation and design of interventions tailored to the characteristics of each individual. The BCSQ-36 may be very useful in mental health services, given that it provides a good deal of information, while the BCSQ-12 could be used as a screening measure in primary care consultations owing to its simplicity and functional nature. PMID:23110723
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Binder, Harald; Sauerbrei, Willi; Royston, Patrick
2013-06-15
In observational studies, many continuous or categorical covariates may be related to an outcome. Various spline-based procedures or the multivariable fractional polynomial (MFP) procedure can be used to identify important variables and functional forms for continuous covariates. This is the main aim of an explanatory model, as opposed to a model only for prediction. The type of analysis often guides the complexity of the final model. Spline-based procedures and MFP have tuning parameters for choosing the required complexity. To compare model selection approaches, we perform a simulation study in the linear regression context based on a data structure intended to reflect realistic biomedical data. We vary the sample size, variance explained and complexity parameters for model selection. We consider 15 variables. A sample size of 200 (1000) and R(2) = 0.2 (0.8) is the scenario with the smallest (largest) amount of information. For assessing performance, we consider prediction error, correct and incorrect inclusion of covariates, qualitative measures for judging selected functional forms and further novel criteria. From limited information, a suitable explanatory model cannot be obtained. Prediction performance from all types of models is similar. With a medium amount of information, MFP performs better than splines on several criteria. MFP better recovers simpler functions, whereas splines better recover more complex functions. For a large amount of information and no local structure, MFP and the spline procedures often select similar explanatory models. Copyright © 2012 John Wiley & Sons, Ltd.
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Fattore, Gisel Lorena; Santos, Carlos Antonio de Souza Teles; Barreto, Mauricio Lima
2015-11-01
The prevalence of asthma is high in urban areas of many Latin-American countries where societies show high levels of inequality and different levels of development. This study aimed to examine the relationship between asthma symptoms prevalence in adolescents living in Latin American urban centers and socioeconomic and environmental determinants measured at the ecological level. Asthma prevalence symptoms were obtained from the International Study of Asthma and Allergies in Childhood (ISAAC) phase III. A hierarchical conceptual framework was defined and the explanatory variables were organized in three levels: distal, intermediate, proximal. Linear regression models weighed by sample size were undertaken between asthma prevalence and the selected variables. Asthma prevalence was positively associated with Gini index, water supply and homicide rate, and inversely associated with the Human Development Index, crowding and adequate sanitation. This study provides evidence of the potential influence of poverty and social inequalities on current wheezing in adolescents in a complex social context like Latin America.
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Typical intellectual engagement and cognition in old age.
Dellenbach, Myriam; Zimprich, Daniel
2008-03-01
Typical Intellectual Engagement (TIE) comprises the preference to engage in cognitively demanding activities and has been proposed as a potential explanatory variable of individual differences in cognitive abilities. Little is known, however, about the factorial structure of TIE, its relations to socio-demographic variables, and its influence on intellectual functioning in old age. In the present study, data of 364 adults (65-81 years) from the Zurich Longitudinal Study on Cognitive Aging (ZULU) were used to investigate the factorial structure of TIE and to examine the hypothesis that TIE is associated more strongly with crystallized intelligence than with fluid intelligence in old age. A measurement model of a second order factor based on a structure of four correlated first order factors (Reading, Problem Solving, Abstract Thinking, and Intellectual Curiosity) evinced an excellent fit. After controlling for age, sex, and formal education, TIE was more strongly associated with crystallized intelligence than with fluid intelligence, comparable to results in younger persons. More detailed analyses showed that this association is mostly defined via Reading and Intellectual Curiosity.
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Velasquez-Melendez, Gustavo; Molina, Maria Del Carmen B; Benseñor, Isabela M; Cardoso, Leticia O; Fonseca, Maria de Jesus M; Moreira, Alexandra D; Pereira, Taísa Sabrina S; Barreto, Sandhi M
2017-02-01
To estimate the association between regular consumption of sweetened soft drinks, natural fruit juice, and coconut water with metabolic syndrome (MetS). This was a cross-sectional study including men and women aged 35-74 years from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) Study, excluding patients with type 2 diabetes. The main explanatory variables were beverage consumption and the outcome variable was metabolic syndrome (Adult Treatment Panel III). After adjustments, a daily intake of 250 ml of soft drink increased the chance of metabolic syndrome (odds ratio [OR] = 1.95; 95% confidence interval [CI], 1.60-2.38). There was no association between coconut water and MetS. Moderate consumption of fruit juices has low odds of MetS compared to no consumption. Our results add evidence to potential negative effects of sweetened soft drinks on cluster metabolic abnormalities in middle-income countries.
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Felicitas, Jamie Q; Tanenbaum, Hilary C; Li, Yawen; Chou, Chih-Ping; Palmer, Paula H; Spruijt-Metz, Donna; Reynolds, Kim D; Johnson, C Anderson; Xie, Bin
This paper explores the longitudinal effects of socioeconomic factors (i.e., parent education and family income level), foreign media, and attitude toward appearance on general and central adiposity among Chinese adolescents. A longitudinal analysis was performed using data from the China Seven Cities Study, a health promotion and smoking prevention study conducted in seven cities across Mainland China between 2002 and 2005. Participants included 5,020 middle and high school students and their parents. Explanatory variables included foreign media exposure, attitude toward appearance, parent education, and family income. Three-level, random-effect models were used to predict general adiposity (i.e., body mass index) and central adiposity (i.e., waist circumference). The Generalized Estimating Equation approach was utilized to determine the effect of explanatory variables on overweight status. Among girls, foreign media exposure was significantly negatively associated with general adiposity over time (β=-0.06, p=0.01 for middle school girls; β=-0.06, p=0.03 for high school girls). Attitude toward appearance was associated with lesser odds of being overweight, particularly among high school girls (OR=0.86, p<0.01). Among boys, parental education was significantly positively associated with general adiposity (β=0.62, p<0.01 for middle school boys; β=0.37, p=0.02 for high school boys) and associated with greater odds of being overweight (OR=1.55, p<0.01 for middle school boys; OR=1.26, p=0.04 for high school boys). Across all gender and grade levels, family income was significantly negatively associated with central adiposity over time. Interventions addressing Chinese adolescent overweight/obesity should consider these factors as potential focus areas.
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Duration of Physical Activity and Serum 25-hydroxyvitamin D Status of Postmenopausal Women
Kluczynski, Melissa A.; LaMonte, Michael J.; Mares, Julie A.; Wactawski-Wende, Jean; Smith, Ashley Wilder; Engelman, Corinne D.; Andrews, Christopher A.; Snetselaar, Linda G.; Sarto, Gloria E.; Millen, Amy E.
2011-01-01
Purpose To investigate whether the association between physical activity and serum 25-hydroxyvitamin D (25(OH)D) concentrations is independent of sun exposure, body size, and other potential explanatory variables. Methods Using data from a sample of 1,343 postmenopausal women, from the Women’s Health Initiative, linear regression was used to examine the associations of duration (minutes/week) of recreational activity and of yard work with 25(OH)D concentrations (nmol/L). Results In age-adjusted analyses, positive associations were observed between 25(OH)D concentrations and both duration of recreational physical activity (β=0.71, SE(0.09), P<0.001) and yard work (β=0.36, SE(0.10), P=0.004). After further adjustment for vitamin D intake, self-reported sunlight exposure, waist circumference, and season of blood draw, 25(OH)D was significantly associated with recreational activity (β=0.21, SE(0.09), P=0.014) but not with yard work (β=0.18, SE(0.09), P=0.061). Interactions were observed between season and both recreational activity (Pinteraction=0.082) and yard work (Pinteraction=0.038) such that these activity-25(OH)D associations were greater during summer/fall compared to winter/spring. Self-reported sunlight exposure and measures of body size did not modify the associations. Conclusion The observed age-adjusted activity-25(OH)D associations were attenuated after adjusting for explanatory variables and were modified by season of blood draw. Adopting a lifestyle that incorporates outdoor physical activity during summer/fall, consuming recommended amounts of vitamin D, and maintaining a healthy weight may improve or maintain vitamin D status in postmenopausal women. PMID:21414803
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Surrogate analysis and index developer (SAID) tool and real-time data dissemination utilities
Domanski, Marian M.; Straub, Timothy D.; Wood, Molly S.; Landers, Mark N.; Wall, Gary R.; Brady, Steven J.
2015-01-01
The use of acoustic and other parameters as surrogates for suspended-sediment concentrations (SSC) in rivers has been successful in multiple applications across the Nation. Critical to advancing the operational use of surrogates are tools to process and evaluate the data along with the subsequent development of regression models from which real-time sediment concentrations can be made available to the public. Recent developments in both areas are having an immediate impact on surrogate research, and on surrogate monitoring sites currently in operation. The Surrogate Analysis and Index Developer (SAID) standalone tool, under development by the U.S. Geological Survey (USGS), assists in the creation of regression models that relate response and explanatory variables by providing visual and quantitative diagnostics to the user. SAID also processes acoustic parameters to be used as explanatory variables for suspended-sediment concentrations. The sediment acoustic method utilizes acoustic parameters from fixed-mount stationary equipment. The background theory and method used by the tool have been described in recent publications, and the tool also serves to support sediment-acoustic-index methods being drafted by the multi-agency Sediment Acoustic Leadership Team (SALT), and other surrogate guidelines like USGS Techniques and Methods 3-C4 for turbidity and SSC. The regression models in SAID can be used in utilities that have been developed to work with the USGS National Water Information System (NWIS) and for the USGS National Real-Time Water Quality (NRTWQ) Web site. The real-time dissemination of predicted SSC and prediction intervals for each time step has substantial potential to improve understanding of sediment-related water-quality and associated engineering and ecological management decisions.
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An Empirical Examination of the Anomie Theory of Drug Use.
ERIC Educational Resources Information Center
Dull, R. Thomas
1983-01-01
Investigated the relationship between anomie theory, as measured by Srole's Anomie Scale, and self-admitted drug use in an adult population (N=1,449). Bivariate cross-comparison correlations indicated anomie was significantly correlated with several drug variables, but these associations were extremely weak and of little explanatory value.…
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Improving Audience Learning from Television News through Between-Channel Redundancy.
ERIC Educational Resources Information Center
Reese, Stephen D.
A study tested the effects of between-channel redundancy on television news learning. Redundancy, defined as shared information, was proposed as an explanatory variable that considers the relationship between information in three channels: the audio, the nonverbal pictorial, and visual-verbal print channel. It was hypothesized that pictures would…
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Eating Disorders: Explanatory Variables in Caucasian and Hispanic College Women
ERIC Educational Resources Information Center
Aviña, Vanessa; Day, Susan X.
2016-01-01
The authors explored Hispanic and Caucasian college women's (N = 264) behavioral and attitudinal symptoms of eating disorders after controlling for body mass index and internalization of the thinness ideal, as well as the roles of ethnicity and ethnic identity in symptomatology. Correlational analysis, multivariate analysis of variance, and…
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ERIC Educational Resources Information Center
Brown, F. William; Bielinska-Kwapisz, Agnieszka
2015-01-01
The authors examine the dimensions and determinants of critical thinking skills, as measured by the California Critical Thinking Skills Test, among graduating senior students enrolled in an Association to Advance Collegiate Schools of Business-accredited undergraduate business program. Utilizing explanatory variables, a methodology for predicting…
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Social Inequality and Labor Force Participation.
ERIC Educational Resources Information Center
King, Jonathan
The labor force participation rates of whites, blacks, and Spanish-Americans, grouped by sex, are explained in a linear regression model fitted with 1970 U. S. Census data on Standard Metropolitan Statistical Area (SMSA). The explanatory variables are: average age, average years of education, vocational training rate, disabled rate, unemployment…
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Cognitive Style Predictors of Affect Change in Older Adults
ERIC Educational Resources Information Center
Isaacowitz, Derek M.; Seligman, Martin E. P.
2002-01-01
Cognitive styles are the lenses through which individuals habitually process information from their environment. In this study, we evaluated whether different cognitive style individual difference variables, such as explanatory style and dispositional optimism, could predict changes in affective state over time in community-dwelling older adults.…
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A Theory of School Achievement: A Quantum View
ERIC Educational Resources Information Center
Phelps, James L.
2012-01-01
In most school achievement research, the relationships between achievement and explanatory variables follow the Newton and Einstein concept/principle and the viewpoint of the macro-observer: Deterministic measures based on the mean value of a sufficiently large number of schools. What if the relationships between achievement and explanatory…
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Marital Conflict and Children's Emotional Security in the Context of Parental Depression
ERIC Educational Resources Information Center
Kouros, Chrystyna D.; Merrilees, Christine E.; Cummings, E. Mark
2008-01-01
Evidence has emerged for emotional security as an explanatory variable linking marital conflict to children's adjustment. Further evidence suggests parental psychopathology is a key factor in child development. To advance understanding of the pathways by which these family risk factors impact children's development, the mediational role of…
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A Note on the Heterogeneous Choice Model
ERIC Educational Resources Information Center
Rohwer, Goetz
2015-01-01
The heterogeneous choice model (HCM) has been proposed as an extension of the standard logit and probit models, which allows taking into account different error variances of explanatory variables. In this note, I show that in an important special case, this model is just another way to specify an interaction effect.
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Lexicography and Mathematics Learning: A Case Study of "Variable."
ERIC Educational Resources Information Center
Frawley, William
1992-01-01
Lexicography is shown to offer some useful new tools to researchers in mathematics education. The paper examines the relationship between the sublanguage of mathematics and the acquisition of mathematical knowledge, and also the use of definitions in research and curriculum design. An Explanatory Combinatorial Dictionary is advocated for improving…
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Toxic Familial Effects of Parental Hostility.
ERIC Educational Resources Information Center
Buri, John R.; And Others
In research efforts to account for the variance in parent-child interactions, two variables have been cited repeatedly for their explanatory cogency--nurturance and authority. This study was conducted to examine the relation of parents' Hostility (Ho) scores from the Minnesota Multiphasic Personality Inventory-based Cook and Medley Hostility Scale…
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Performance Measurement: Does Education Impact Productivity?
ERIC Educational Resources Information Center
Larbi-Apau, Josephine A.; Sarpong, Daniel Bruce
2010-01-01
This study investigated the impact of managers' educational levels on productivity in the commercial poultry industry in Ghana. The level of education of 33 production managers of the poultry farms were factored into a Cobb-Douglas production function with other explanatory variables. The computed percentage change in productivity due to higher…
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Defining conservation priorities using fragmentation forecasts
David Wear; John Pye; Kurt H. Riitters
2004-01-01
Methods are developed for forecasting the effects of population and economic growth on the distribution of interior forest habitat. An application to the southeastern United States shows that models provide significant explanatory power with regard to the observed distribution of interior forest. Estimates for economic and biophysical variables are significant and...
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Dynamical Systems Approaches to Emotional Development
ERIC Educational Resources Information Center
Camras, Linda A.; Witherington, David C.
2005-01-01
Within the last 20 years, transitions in the conceptualization of emotion and its development have given rise to calls for an explanatory framework that captures emotional development in all its organizational complexity and variability. Recent attempts have been made to couch emotional development in terms of a dynamical systems approach through…
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NASA Astrophysics Data System (ADS)
Creaco, E.; Berardi, L.; Sun, Siao; Giustolisi, O.; Savic, D.
2016-04-01
The growing availability of field data, from information and communication technologies (ICTs) in "smart" urban infrastructures, allows data modeling to understand complex phenomena and to support management decisions. Among the analyzed phenomena, those related to storm water quality modeling have recently been gaining interest in the scientific literature. Nonetheless, the large amount of available data poses the problem of selecting relevant variables to describe a phenomenon and enable robust data modeling. This paper presents a procedure for the selection of relevant input variables using the multiobjective evolutionary polynomial regression (EPR-MOGA) paradigm. The procedure is based on scrutinizing the explanatory variables that appear inside the set of EPR-MOGA symbolic model expressions of increasing complexity and goodness of fit to target output. The strategy also enables the selection to be validated by engineering judgement. In such context, the multiple case study extension of EPR-MOGA, called MCS-EPR-MOGA, is adopted. The application of the proposed procedure to modeling storm water quality parameters in two French catchments shows that it was able to significantly reduce the number of explanatory variables for successive analyses. Finally, the EPR-MOGA models obtained after the input selection are compared with those obtained by using the same technique without benefitting from input selection and with those obtained in previous works where other data-modeling techniques were used on the same data. The comparison highlights the effectiveness of both EPR-MOGA and the input selection procedure.
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NASA Astrophysics Data System (ADS)
Petropavlovskikh, I. V.; Disterhoft, P.; Johnson, B. J.; Rieder, H. E.; Manney, G. L.; Daffer, W.
2012-12-01
This work attributes tropospheric ozone variability derived from the ground-based Dobson and Brewer Umkehr measurements and from ozone sonde data to local sources and transport. It assesses capability and limitations in both types of measurements that are often used to analyze long- and short-term variability in tropospheric ozone time series. We will address the natural and instrument-related contribution to the variability found in both Umkehr and sonde data. Validation of Umkehr methods is often done by intercomparisons against independent ozone measuring techniques such as ozone sounding. We will use ozone-sounding in its original and AK-smoothed vertical profiles for assessment of ozone inter-annual variability over Boulder, CO. We will discuss possible reasons for differences between different ozone measuring techniques and its effects on the derived ozone trends. Next to standard evaluation techniques we utilize a STL-decomposition method to address temporal variability and trends in the Boulder Umkehr data. Further, we apply a statistical modeling approach to the ozone data set to attribute ozone variability to individual driving forces associated with natural and anthropogenic causes. To this aim we follow earlier work applying a backward selection method (i.e., a stepwise elimination procedure out of a set of total 44 explanatory variables) to determine those explanatory variables which contribute most significantly to the observed variability. We will present also some results associated with completeness (sampling rate) of the existing data sets. We will also use MERRA (Modern-Era Retrospective analysis for Research and Applications) re-analysis results selected for Boulder location as a transfer function in understanding of the effects that the temporal sampling and vertical resolution bring into trend and ozone variability analysis. Analyzing intra-annual variability in ozone measurements over Boulder, CO, in relation to the upper tropospheric subtropical and polar jets, we will address the stratospheric and tropospheric intrusions in the middle latitude troposphere ozone field.
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An Explanatory Model of Self-Service on the Internet
NASA Astrophysics Data System (ADS)
Oliver, Dave; Livermore, Celia Romm; Farag, Neveen Awad
This chapter describes research that identifies and classifies the dimensions of self-service activity enabled through the Internet. Self-service is effected by organizations providing ways and means whereby customers perform tasks related to the procurement of goods and services. We describe how an instrument used to measure Internet-based self-service was developed, validated and applied. The results from applying the instrument to a large number of Web sites, covering a range of industries, countries and cultures, are analyzed and discussed. The study presents a model in which type of industry, level of technological development, income and cultural factors are proposed as explanatory variables for Web-based self-service. We conclude with an assessment of this program of research’s achievements so far.
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Anderson, Chauncey W.; Rounds, Stewart A.
2010-01-01
Management of water quality in streams of the United States is becoming increasingly complex as regulators seek to control aquatic pollution and ecological problems through Total Maximum Daily Load programs that target reductions in the concentrations of certain constituents. Sediment, nutrients, and bacteria, for example, are constituents that regulators target for reduction nationally and in the Tualatin River basin, Oregon. These constituents require laboratory analysis of discrete samples for definitive determinations of concentrations in streams. Recent technological advances in the nearly continuous, in situ monitoring of related water-quality parameters has fostered the use of these parameters as surrogates for the labor intensive, laboratory-analyzed constituents. Although these correlative techniques have been successful in large rivers, it was unclear whether they could be applied successfully in tributaries of the Tualatin River, primarily because these streams tend to be small, have rapid hydrologic response to rainfall and high streamflow variability, and may contain unique sources of sediment, nutrients, and bacteria. This report evaluates the feasibility of developing correlative regression models for predicting dependent variables (concentrations of total suspended solids, total phosphorus, and Escherichia coli bacteria) in two Tualatin River basin streams: one draining highly urbanized land (Fanno Creek near Durham, Oregon) and one draining rural agricultural land (Dairy Creek at Highway 8 near Hillsboro, Oregon), during 2002-04. An important difference between these two streams is their response to storm runoff; Fanno Creek has a relatively rapid response due to extensive upstream impervious areas and Dairy Creek has a relatively slow response because of the large amount of undeveloped upstream land. Four other stream sites also were evaluated, but in less detail. Potential explanatory variables included continuously monitored streamflow (discharge), stream stage, specific conductance, turbidity, and time (to account for seasonal processes). Preliminary multiple-regression models were identified using stepwise regression and Mallow's Cp, which maximizes regression correlation coefficients and accounts for the loss of additional degrees of freedom when extra explanatory variables are used. Several data scenarios were created and evaluated for each site to assess the representativeness of existing monitoring data and autosampler-derived data, and to assess the utility of the available data to develop robust predictive models. The goodness-of-fit of candidate predictive models was assessed with diagnostic statistics from validation exercises that compared predictions against a subset of the available data. The regression modeling met with mixed success. Functional model forms that have a high likelihood of success were identified for most (but not all) dependent variables at each site, but there were limitations in the available datasets, notably the lack of samples from high-flows. These limitations increase the uncertainty in the predictions of the models and suggest that the models are not yet ready for use in assessing these streams, particularly under high-flow conditions, without additional data collection and recalibration of model coefficients. Nonetheless, the results reveal opportunities to use existing resources more efficiently. Baseline conditions are well represented in the available data, and, for the most part, the models reproduced these conditions well. Future sampling might therefore focus on high flow conditions, without much loss of ability to characterize the baseline. Seasonal cycles, as represented by trigonometric functions of time, were not significant in the evaluated models, perhaps because the baseline conditions are well characterized in the datasets or because the other explanatory variables indirectly incorporate seasonal aspects. Multicollinearity among independent variabl
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Intensity of interprofessional collaboration among intensive care nurses at a tertiary hospital.
Serrano-Gemes, G; Rich-Ruiz, M
To measure the intensity of interprofessional collaboration (IPC) in nurses of an intensive care unit (ICU) at a tertiary hospital, to check differences between the dimensions of the Intensity of Interprofessional Collaboration Questionnaire, and to identify the influence of personal variables. A cross-sectional descriptive study was conducted with 63 intensive care nurses selected by simple random sampling. Explanatory variables: age, sex, years of experience in nursing, years of experience in critical care, workday type and work shift type; variable of outcome: IPC. The IPC was measured by: Intensity of Interprofessional Collaboration Questionnaire. Descriptive and bivariate statistical analysis (IPC and its dimensions with explanatory variables). 73.8% were women, with a mean age of 46.54 (±6.076) years. The average years experience in nursing and critical care was 23.03 (±6.24) and 14.25 (±8.532), respectively. 77% had a full time and 95.1% had a rotating shift. 62.3% obtained average IPC values. Statistically significant differences were found (P<.05) between IPC (overall score) and overall assessment with years of experience in critical care. This study shows average levels of IPC; the nurses with less experience in critical care obtained higher IPC and overall assessment scores. Copyright © 2016 Sociedad Española de Enfermería Intensiva y Unidades Coronarias (SEEIUC). Publicado por Elsevier España, S.L.U. All rights reserved.
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2013-01-01
Background In statistical modeling, finding the most favorable coding for an exploratory quantitative variable involves many tests. This process involves multiple testing problems and requires the correction of the significance level. Methods For each coding, a test on the nullity of the coefficient associated with the new coded variable is computed. The selected coding corresponds to that associated with the largest statistical test (or equivalently the smallest pvalue). In the context of the Generalized Linear Model, Liquet and Commenges (Stat Probability Lett,71:33–38,2005) proposed an asymptotic correction of the significance level. This procedure, based on the score test, has been developed for dichotomous and Box-Cox transformations. In this paper, we suggest the use of resampling methods to estimate the significance level for categorical transformations with more than two levels and, by definition those that involve more than one parameter in the model. The categorical transformation is a more flexible way to explore the unknown shape of the effect between an explanatory and a dependent variable. Results The simulations we ran in this study showed good performances of the proposed methods. These methods were illustrated using the data from a study of the relationship between cholesterol and dementia. Conclusion The algorithms were implemented using R, and the associated CPMCGLM R package is available on the CRAN. PMID:23758852
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Otto, Monica; Armeni, Patrizio; Jommi, Claudio
2018-01-31
This paper analyses the determinants of cross-regional variations in expenditure and consumption for non-prescription drugs using the Italian Health Care Service as a case study. This research question has never been posed in other literature contributions. Per capita income, the incidence of elderly people, the presence of distribution points alternative to community pharmacies (para-pharmacies and drug corners in supermarkets), and the disease prevalence were included as possible explanatory variables. A trade-off between consumption of non-prescription and prescription-only drugs was also investigated. Correlation was tested through linear regression models with regional fixed-effects. Demand-driven variables, including the prevalence of the target diseases and income, were found to be more influential than supply-side variables, such as the presence of alternative distribution points. Hence, the consumption of non-prescription drugs appears to respond to needs and is not induced by the supply. The expected trade-off between consumption for prescription-only and non-prescription drugs was not empirically found: increasing the use of non-prescription drugs did not automatically imply savings on prescription-only drugs covered by third payers. Despite some caveats (the short period of time covered by the longitudinal data and some missing monthly data), the regression model revealed a high explanatory power of the variability and a strong predictive ability of future values. Copyright © 2018 Elsevier B.V. All rights reserved.
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Balint, Lajos; Dome, Peter; Daroczi, Gergely; Gonda, Xenia; Rihmer, Zoltan
2014-02-01
In the last century Hungary had astonishingly high suicide rates characterized by marked regional within-country inequalities, a spatial pattern which has been quite stable over time. To explain the above phenomenon at the level of micro-regions (n=175) in the period between 2005 and 2011. Our dependent variable was the age and gender standardized mortality ratio (SMR) for suicide while explanatory variables were factors which are supposed to influence suicide risk, such as measures of religious and political integration, travel time accessibility of psychiatric services, alcohol consumption, unemployment and disability pensionery. When applying the ordinary least squared regression model, the residuals were found to be spatially autocorrelated, which indicates the violation of the assumption on the independence of error terms and - accordingly - the necessity of application of a spatial autoregressive (SAR) model to handle this problem. According to our calculations the SARlag model was a better way (versus the SARerr model) of addressing the problem of spatial autocorrelation, furthermore its substantive meaning is more convenient. SMR was significantly associated with the "political integration" variable in a negative and with "lack of religious integration" and "disability pensionery" variables in a positive manner. Associations were not significant for the remaining explanatory variables. Several important psychiatric variables were not available at the level of micro-regions. We conducted our analysis on aggregate data. Our results may draw attention to the relevance and abiding validity of the classic Durkheimian suicide risk factors - such as lack of social integration - apropos of the spatial pattern of Hungarian suicides. © 2013 Published by Elsevier B.V.
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Gbm.auto: A software tool to simplify spatial modelling and Marine Protected Area planning
Officer, Rick; Clarke, Maurice; Reid, David G.; Brophy, Deirdre
2017-01-01
Boosted Regression Trees. Excellent for data-poor spatial management but hard to use Marine resource managers and scientists often advocate spatial approaches to manage data-poor species. Existing spatial prediction and management techniques are either insufficiently robust, struggle with sparse input data, or make suboptimal use of multiple explanatory variables. Boosted Regression Trees feature excellent performance and are well suited to modelling the distribution of data-limited species, but are extremely complicated and time-consuming to learn and use, hindering access for a wide potential user base and therefore limiting uptake and usage. BRTs automated and simplified for accessible general use with rich feature set We have built a software suite in R which integrates pre-existing functions with new tailor-made functions to automate the processing and predictive mapping of species abundance data: by automating and greatly simplifying Boosted Regression Tree spatial modelling, the gbm.auto R package suite makes this powerful statistical modelling technique more accessible to potential users in the ecological and modelling communities. The package and its documentation allow the user to generate maps of predicted abundance, visualise the representativeness of those abundance maps and to plot the relative influence of explanatory variables and their relationship to the response variables. Databases of the processed model objects and a report explaining all the steps taken within the model are also generated. The package includes a previously unavailable Decision Support Tool which combines estimated escapement biomass (the percentage of an exploited population which must be retained each year to conserve it) with the predicted abundance maps to generate maps showing the location and size of habitat that should be protected to conserve the target stocks (candidate MPAs), based on stakeholder priorities, such as the minimisation of fishing effort displacement. Gbm.auto for management in various settings By bridging the gap between advanced statistical methods for species distribution modelling and conservation science, management and policy, these tools can allow improved spatial abundance predictions, and therefore better management, decision-making, and conservation. Although this package was built to support spatial management of a data-limited marine elasmobranch fishery, it should be equally applicable to spatial abundance modelling, area protection, and stakeholder engagement in various scenarios. PMID:29216310
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The gender earnings gap among pharmacists.
Carvajal, Manuel J; Armayor, Graciela M; Deziel, Lisa
2012-01-01
A gender earnings gap exists across professions. Compared with men, women earn consistently lower income levels. The determinants of wages and salaries should be explored to assess whether a gender earnings gap exists in the pharmacy profession. The objectives of this study were to (1) compare the responses of male and female pharmacists' earnings with human-capital stock, workers' preferences, and opinion variables and (2) assess whether the earnings determination models for male and female pharmacists yielded similar results in estimating the wage-and-salary gap through earnings projections, the influence of each explanatory variable, and gender differences in statistical significance. Data were collected through the use of a 37-question survey mailed to registered pharmacists in South Florida, United States. Earnings functions were formulated and tested separately for male and female pharmacists using unlogged and semilog equation forms. Number of hours worked, human-capital stock, job preferences, and opinion variables were hypothesized to explain wage-and-salary differentials. The empirical evidence led to 3 major conclusions: (1) men's and women's earnings sometimes were influenced by different stimuli, and when they responded to the same variables, the effect often was different; (2) although the influence of some explanatory variables on earnings differed in the unlogged and semilog equations, the earnings projections derived from both equation forms for male and female pharmacists were remarkably similar and yielded nearly identical male-female earnings ratios; and (3) controlling for number of hours worked, human-capital stock, job preferences, and opinion variables reduced the initial unadjusted male-female earnings ratios only slightly, which pointed toward the presence of gender bias. After controlling for human-capital stock, job-related characteristics, and opinion variables, male pharmacists continued to earn higher income levels than female pharmacists. Copyright © 2012 Elsevier Inc. All rights reserved.
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Monterde, David; Vela, Emili; Clèries, Montse; García Eroles, Luis; Pérez Sust, Pol
2018-02-09
To compare the performance in terms of goodness of fit and explanatory power of 2morbidity groupers in primary care (PC): adjusted morbidity groups (AMG) and clinical risk groups (CRG). Cross-sectional study. PC in the Catalan Institute for the Health (CIH), Catalonia, Spain. Population allocated in primary care centers of the CIH for the year 2014. Three indicators of interest are analyzed such as urgent hospitalization, number of visits and spending in pharmacy. A stratified analysis by centers is applied adjusting generalized lineal models from the variables age, sex and morbidity grouping to explain each one of the 3variables of interest. The statistical measures to analyze the performance of the different models applied are the Akaike index, the Bayes index and the pseudo-variability explained by deviance change. The results show that in the area of the primary care the explanatory power of the AMGs is higher to that offered by the CRGs, especially for the case of the visits and the pharmacy. The performance of GMAs in the area of the CIH PC is higher than that shown by the CRGs. Copyright © 2018 The Authors. Publicado por Elsevier España, S.L.U. All rights reserved.
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NASA Astrophysics Data System (ADS)
Trowbridge, Cynthia D.; Kachmarik, Katy; Plowman, Caitlin Q.; Little, Colin; Stirling, Penny; McAllen, Rob
2017-03-01
At Lough Hyne Marine Reserve in SW Ireland, shallow subtidal, under-rock biodiversity was investigated to assess (i) any deleterious effects of scientific sampling and (ii) quantitative baseline community patterns. Comparisons were made between 10 sites with annual rock-turning disturbance and 10 with multi-decadal (historical) disturbance. At each site, shallow subtidal rocks (N = 1289 total) were lifted, organisms recorded, and rocks replaced in their original position. Biodiversity indices were calculated to evaluate how diversity varied with location within the lough, frequency of sampling disturbance, degree of hypoxia/anoxia, dissolved oxygen (DO) concentration, and number of rocks turned. The richness of solitary invertebrates surveyed in situ averaged 21 taxa per site with significantly more in the South Basin (near the lough's connection to the ocean) than in the North Basin. The Shannon-Wiener Index did not differ significantly with variables investigated. However, evenness was higher at annually disturbed sites than at historical ones where anemones with algal symbionts often dominated. Several sites were hypoxic to anoxic under the shallow subtidal rocks. Cup corals were most abundant in the South Basin; DO was a crucial explanatory variable of these sensitive species. Solitary ascidians were most abundant at South-Basin annual sites with DO levels being a highly significant explanatory variable.
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Laugesen, Britt; Mohr-Jensen, Christina; Boldsen, Søren Kjærgaard; Jørgensen, Rikke; Sørensen, Erik Elgaard; Grønkjær, Mette; Rasmussen, Philippa; Lauritsen, Marlene Briciet
2018-06-01
To compare the mean number of medical and psychiatric hospital-based services in children with and without attention deficit hyperactivity disorder (ADHD) and to assess the effect of ADHD on hospital-based service use, including child-, parental-, and socioeconomic-related risk factors. A Danish birth cohort was followed through 12 years, and children with ADHD were identified using Danish nationwide registries. Poisson regression analyses were used to assess the association of ADHD with service use and to adjust for a comprehensive set of explanatory variables. Children diagnosed with ADHD used more medical and psychiatric hospital-based healthcare than those without ADHD. In children with ADHD, intellectual disability and parental psychiatric disorder were associated with increased medical and psychiatric service use. Low birth weight and low gestational age were associated with increased medical service use. Psychiatric comorbidity and having a divorced or single parent were associated with increased psychiatric service use. ADHD independently affected medical and psychiatric hospital-based service use even when adjusting for a comprehensive set of explanatory variables. However, the pattern of medical and psychiatric hospital-based service use is complex and cannot exclusively be explained by the child-, parental-, and socioeconomic-related variables examined in this study. Copyright © 2018 Elsevier Inc. All rights reserved.
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Yang, Y-M; Lee, J; Kim, Y-I; Cho, B-H; Park, S-B
2014-08-01
This study aimed to determine the viability of using axial cervical vertebrae (ACV) as biological indicators of skeletal maturation and to build models that estimate ossification level with improved explanatory power over models based only on chronological age. The study population comprised 74 female and 47 male patients with available hand-wrist radiographs and cone-beam computed tomography images. Generalized Procrustes analysis was used to analyze the shape, size, and form of the ACV regions of interest. The variabilities of these factors were analyzed by principal component analysis. Skeletal maturation was then estimated using a multiple regression model. Separate models were developed for male and female participants. For the female estimation model, the adjusted R(2) explained 84.8% of the variability of the Sempé maturation level (SML), representing a 7.9% increase in SML explanatory power over that using chronological age alone (76.9%). For the male estimation model, the adjusted R(2) was over 90%, representing a 1.7% increase relative to the reference model. The simplest possible ACV morphometric information provided a statistically significant explanation of the portion of skeletal-maturation variability not dependent on chronological age. These results verify that ACV is a strong biological indicator of ossification status. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
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2015-09-30
SST), sea surface height anomaly (SSH), chlorophyll a concentration (Chla), and primary productivity (PP). These data are available on similar...between the high and low area, and in areas with low abundance, chlorophyll a concentration was also a significant explanatory variable. For fin
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ERIC Educational Resources Information Center
Oyerinde, Bolanle Adenike
2014-01-01
Low involvement of African American parents in middle school education is a concern in a school district in the southeastern United States. The purpose of this quantitative study was to investigate the relationships between the explanatory variables of parental involvement, socioeconomic status, and level of education, and the achievement of…
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Language Attitudes in Catalan Multilingual Classrooms: Educational Implications
ERIC Educational Resources Information Center
Madariaga, José-María; Huguet, Ángel; Janés, Judit
2016-01-01
Catalonia is the Autonomous Community of Spain with the highest proportion of immigrant students. This study analyses the language attitudes of Catalan, as well as the possible explanatory variables for such attitudes, for a large sample with a high proportion of immigrant students and a great linguistic diversity. A questionnaire was given to…
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A Structural Equation Model Explaining 8th Grade Students' Mathematics Achievements
ERIC Educational Resources Information Center
Yurt, Eyüp; Sünbül, Ali Murat
2014-01-01
The purpose of this study is to investigate, via a model, the explanatory and predictive relationships among the following variables: Mathematical Problem Solving and Reasoning Skills, Sources of Mathematics Self-Efficacy, Spatial Ability, and Mathematics Achievements of Secondary School 8th Grade Students. The sample group of the study, itself…
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Factors Affecting Smoking Tendency and Smoking Intensity
ERIC Educational Resources Information Center
David, Nissim Ben; Zion, Uri Ben
2009-01-01
Purpose: The purpose of this paper is to measure the relative effect of relevant explanatory variable on smoking tendency and smoking intensity. Design/methodology/approach: Using survey data collected by the Israeli Bureau of Statistics in 2003-2004, a probit procedure is estimated for analyzing factors that affect the probability of being a…
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Making the Case for Space: The Effect of Learning Spaces on Teaching and Learning
ERIC Educational Resources Information Center
Byers, Terry; Imms, Wesley; Hartnell-Young, Elizabeth
2014-01-01
An explanatory, mixed method study examined the impact of learning spaces on teachers' pedagogy, student engagement and student learning outcomes in a technology-rich school setting. Its quasi-experimental design allowed examination of differences in these variables between two settings--'traditional' classrooms, and 'new generation learning…
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This study assessed how landcover classification affects associations between landscape characteristics and Lyme disease rate. Landscape variables were derived from the National Land Cover Database (NLCD), including native classes (e.g., deciduous forest, developed low intensity)...
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The Impact of Education on Income Distribution.
ERIC Educational Resources Information Center
Tinbergen, Jan
The author's previously developed theory on income distribution, in which two of the explanatory variables are the average level and the distribution of education, is refined and tested on data selected and processed by the author and data from three studies by Americans. The material consists of data on subdivisions of three countries, the United…
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The Economics of Persistence: Graduation Rates of Athletes as Labor Market Choice.
ERIC Educational Resources Information Center
DeBrock, Lawrence; And Others
1996-01-01
Analysis of data from NCAA Division I schools for male football and male and female basketball players shows that traditional labor market opportunities unrelated to sports are significant explanatory variables for athletes' academic persistence. Professional sports opportunities also have a significant impact on the graduation rate of athletes.…
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Does Price Matter? Overseas Students in UK Higher Education
ERIC Educational Resources Information Center
Soo, Kwok Tong; Elliott, Caroline
2010-01-01
This paper explores the determinants of the choice of UK universities by overseas undergraduate applicants. We use data on overseas applicants in Business Studies and Engineering from 2002 to 2007, to 97 UK universities. Estimating using a Hausman-Taylor model to control for the possible correlation between our explanatory variables and…
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Economic factors influencing land use changes in the South-Central United States
Ralph J. Alig; Fred C. White; Brian C. Murray
1988-01-01
Econometric models of land use change were estimated for two physiographic regions in the South-Central United States. Results are consistent-with the economic hierarchy of land use, with population and personal income being significant explanatory variables. Findings regarding the importance of relative agricultural and forestry market-based incomes in influencing...
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ERIC Educational Resources Information Center
Shieh, Gwowen
2006-01-01
This paper considers the problem of analysis of correlation coefficients from a multivariate normal population. A unified theorem is derived for the regression model with normally distributed explanatory variables and the general results are employed to provide useful expressions for the distributions of simple, multiple, and partial-multiple…
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Function and Functional Explanation in Social Capital Theory: A Philosophical Appraisal
ERIC Educational Resources Information Center
Vorhaus, John
2014-01-01
Social capital is frequently offered up as a variable to explain such educational outcomes as academic attainment, drop-out rates and cognitive development. Yet, despite its popularity amongst social scientists, social capital theory remains the object of some scepticism, particularly in respect of its explanatory ambitions. I provide an account…
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Relationship between Self-Control and Facebook Use: Case of CEIT Students
ERIC Educational Resources Information Center
Firat, Mehmet
2017-01-01
This is an explanatory mixed-method study that analyzes the relationship between the variables of students' self-control and Facebook usage. TIME's online Facebook calculator and the Brief Self-Control Scale are used for data collection. The research participants are 60 students in a department of computer education and instructional technology…
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ERIC Educational Resources Information Center
Borg, Mary O.; Stranahan, Harriet A.
2002-01-01
Demonstrates that personality type is an important explanatory variable in student performance in upper level economics courses. Finds that certain personality types, combined with race and gender effects, produce students who outperform other students. Introverts and those with the Keirsey-Bates temperament combination of sensing/judging…
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Assault Injury Rates, Social Capital, and Fear of Neighborhood Crime
ERIC Educational Resources Information Center
Kruger, Daniel J.; Hutchison, Peter; Monroe, Matthew G.; Reischl, Thomas; Morrel-Samuels, Susan
2007-01-01
This study develops an explanatory framework for fear of neighborhood crime based on respondents' social context and local rates of assault injuries. Rates of assault injuries within zip codes are based on hospital discharge records. We find that only four variables have a significant unique contribution to fear of crime: respondent's sex,…
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Organizing for Instruction in Education Systems and School Organizations: "How" the Subject Matters
ERIC Educational Resources Information Center
Spillane, James P.; Hopkins, Megan
2013-01-01
Teaching, the core technology of schooling, is an essential consideration in investigations of education systems and school organizations. Taking teaching seriously as an explanatory variable in research on education systems and organizations necessitates moving beyond treating it as a unitary practice, so as to take account of the school subjects…
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Mosing, Martina; Böhm, Stephan H; Rasis, Anthea; Hoosgood, Giselle; Auer, Ulrike; Tusman, Gerardo; Bettschart-Wolfensberger, Regula; Schramel, Johannes P
2018-01-01
The arterial to end-tidal CO 2 difference (P (a-ET) CO 2 ) and alveolar dead space fraction (VDalv frac = P (a-ET) CO 2 /PaCO 2 ), are used to estimate Enghoff's "pulmonary dead space" (V/Q Eng ), a factor which is also influenced by venous admixture and other pulmonary perfusion abnormalities and thus is not just a measure of dead space as the name suggests. The aim of this experimental study was to evaluate which factors influence these CO 2 indices in anesthetized spontaneously breathing horses. Six healthy adult horses were anesthetized in dorsal recumbency breathing spontaneously for 3 h. Data to calculate the CO 2 indices (response variables) and dead space variables were measured every 30 min. Bohr's physiological and alveolar dead space variables, cardiac output (CO), mean pulmonary pressure (MPP), venous admixture [Formula: see text], airway dead space, tidal volume, oxygen consumption, and slope III of the volumetric capnogram were evaluated (explanatory variables). Univariate Pearson correlation was first explored for both CO 2 indices before V/Q Eng and the explanatory variables with rho were reported. Multiple linear regression analysis was performed on P (a-ET) CO 2 and VDalv frac assessing which explanatory variables best explained the variance in each response. The simplest, best-fit model was selected based on the maximum adjusted R 2 and smallest Mallow's p (C p ). The R 2 of the selected model, representing how much of the variance in the response could be explained by the selected variables, was reported. The highest correlation was found with the alveolar part of V/Q Eng to alveolar tidal volume ratio for both, P (a-ET) CO 2 ( r = 0.899) and VDalv frac ( r = 0.938). Venous admixture and CO best explained P (a-ET) CO 2 ( R 2 = 0.752; C p = 4.372) and VDalv frac ( R 2 = 0.711; C p = 9.915). Adding MPP (P (a-ET) CO 2 ) and airway dead space (VDalv frac ) to the models improved them only marginally. No "real" dead space variables from Bohr's equation contributed to the explanation of the variance of the two CO 2 indices. P (a-ET) CO 2 and VDalv frac were closely associated with the alveolar part of V/Q Eng and as such, were also influenced by variables representing a dysfunctional pulmonary perfusion. Neither P (a-ET) CO 2 nor VDalv frac should be considered pulmonary dead space, but used as global indices of V/Q mismatching under the described conditions.
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DiNapoli, Pamela Pershing
2003-02-01
The current study of violence prevention is hampered by a lack of consensus on the definition of violence. There is, however, some agreement about the behavioral cues that may predict violent behavior such as aggression. Although it has been shown that individual-level variables (e.g., race, gender, and ethnicity) are correlated with aggressive behavior, it is clear that they alone are not explanatory of aggressive behavior. This article demonstrates how the Interaction Model of Client Health Behavior is an important health behavior framework for the assessment of aggression in adolescents, offering insight into the contextual nature of adolescent aggression. Victimization and witnessing of violence, frequently identified to be precursors of adolescent aggression in current epidemiologic studies, are examined within this framework. On the basis of the interactional nature of the phenomenon, necessary components for successful prevention programs are suggested. Finally, future research implications calling for a well-designed study that integrates individual and contextual variables with the use of this theory-driven explanatory framework are proposed. Copyright 2003, Elsevier Science (USA). All rights reserved.
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Lee, Chris; Li, Xuancheng
2014-10-01
This study analyzes driver's injury severity in single- and two-vehicle crashes and compares the effects of explanatory variables among various types of crashes. The study identified factors affecting injury severity and their effects on severity levels using 5-year crash records for provincial highways in Ontario, Canada. Considering heteroscedasticity in the effects of explanatory variables on injury severity, the heteroscedastic ordered logit (HOL) models were developed for single- and two-vehicle crashes separately. The results of the models show that there exists heteroscedasticity for young drivers (≤30), safety equipment and ejection in the single-vehicle crash model, and female drivers, safety equipment and head-on collision in the two-vehicle crash models. The results also show that young car drivers have opposite effects between single-car and car-car crashes, and sideswipe crashes have opposite effects between car-car and truck-truck crashes. The study demonstrates that separate HOL models for single-vehicle and different types of two-vehicle crashes can identify differential effects of factors on driver's injury severity. Copyright © 2014 Elsevier Ltd. All rights reserved.
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Parental explanatory models of ADHD: gender and cultural variations.
Bussing, Regina; Gary, Faye A; Mills, Terry L; Garvan, Cynthia Wilson
2003-10-01
This study describes parents' explanatory models of Attention Deficit Hyperactivity Disorder (ADHD) and examines model variation by child characteristics. Children with ADHD (N = 182) were identified from a school district population of elementary school students. A reliable coding system was developed for parental responses obtained in ethnographic interviews in order to convert qualitative into numerical data for quantitative analysis. African-American parents were less likely to connect the school system to ADHD problem identification, expressed fewer worries about ADHD-related school problems, and voiced fewer preferences for school interventions than Caucasian parents, pointing to a potential disconnect with the school system. More African-American than Caucasian parents were unsure about potential causes of and treatments for ADHD, indicating a need for culturally appropriate parent education approaches.
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van Rossum, Joris
2006-01-01
In its essence, the explanatory potential of the theory of natural selection is based on the iterative process of random production and variation, and subsequent non-random, directive selection. It is shown that within this explanatory framework, there is no place for the explanation of sexual reproduction. Thus in Darwinistic literature, sexual reproduction - one of nature's most salient characteristics - is often either assumed or ignored, but not explained. This fundamental and challenging gap within a complete naturalistic understanding of living beings calls for the need of a cybernetic account for sexual reproduction, meaning an understanding of the dynamic and creative potential of living beings to continuously and autonomously produce new organisms with unique and specific constellations.
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Burke, Morgen W. V.; Xu, Yeqian; Zheng, Haochi; VanLooy, Jeffrey
2018-01-01
Studies have shown that the agricultural expansion and land use changes in the Midwest of the U.S. are major drivers for increased nonpoint source pollution throughout the regional river systems. In this study, we empirically examined the relationship of planted area and production of three dominant crops with nitrate flux in the Republican River, Kansas, a sub-watershed of Mississippi River Basin. Our results show that land use in the region could not explain the observed changes in nitrate flux in the river. Instead, after including explanatory variables such as precipitation, growing degree days, and well water irrigation in the regression model we found that irrigation and spring precipitation could explain >85% of the variability in nitrate flux from 2000 to 2014. This suggests that changes in crop acreage and production alone cannot explain variability in nitrate flux. Future agricultural policy for the region should focus on controlling both the timing and amount of fertilizer applied to the field to reduce the potential leaching of excess fertilizer through spring time runoff and/or over-irrigation into nearby river systems. PMID:29789462
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The role of family-of-origin violence in men's marital violence perpetration.
Delsol, Catherine; Margolin, Gayla
2004-03-01
This paper presents overall transmission rates between family-of-origin violence and marital violence, as well as theoretical and empirical work on possible mechanisms of transmission. In identified samples, approximately 60% of the maritally violent men report family-of-origin violence, whereas slightly over 20% of the comparison group of maritally nonviolent men report family-of-origin violence. Modest associations between experiencing violence in the family of origin and marital violence are found in community samples and in studies with prospective and longitudinal designs. Variables that intervene in the association between family-of-origin violence and marital violence are reviewed, with a focus on personal characteristics such as antisocial personality, psychological distress, and attitudes condoning violence, as well as on contextual factors, such as marital problems and conflict resolution style. Variables associated with nonviolence in men who grew up in violent families also are identified, including strong interpersonal connections and the ability to create psychological distance from the family-of-origin violence. Continued empirical investigation of variables that potentiate or mitigate the association between family-of-origin violence and marital violence at different developmental stages is needed to identify explanatory mechanisms and, ultimately, to interrupt the intergenerational transmission of marital violence.
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Gentilesca, Tiziana; Rita, Angelo; Brunetti, Michele; Giammarchi, Francesco; Leonardi, Stefano; Magnani, Federico; van Noije, Twan; Tonon, Giustino; Borghetti, Marco
2018-07-01
In this study, we investigated the role of climatic variability and atmospheric nitrogen deposition in driving long-term tree growth in canopy beech trees along a geographic gradient in the montane belt of the Italian peninsula, from the Alps to the southern Apennines. We sampled dominant trees at different developmental stages (from young to mature tree cohorts, with tree ages spanning from 35 to 160 years) and used stem analysis to infer historic reconstruction of tree volume and dominant height. Annual growth volume (G V ) and height (G H ) variability were related to annual variability in model simulated atmospheric nitrogen deposition and site-specific climatic variables, (i.e. mean annual temperature, total annual precipitation, mean growing period temperature, total growing period precipitation, and standard precipitation evapotranspiration index) and atmospheric CO 2 concentration, including tree cambial age among growth predictors. Generalized additive models (GAM), linear mixed-effects models (LMM), and Bayesian regression models (BRM) were independently employed to assess explanatory variables. The main results from our study were as follows: (i) tree age was the main explanatory variable for long-term growth variability; (ii) GAM, LMM, and BRM results consistently indicated climatic variables and CO 2 effects on G V and G H were weak, therefore evidence of recent climatic variability influence on beech annual growth rates was limited in the montane belt of the Italian peninsula; (iii) instead, significant positive nitrogen deposition (N dep ) effects were repeatedly observed in G V and G H ; the positive effects of N dep on canopy height growth rates, which tended to level off at N dep values greater than approximately 1.0 g m -2 y -1 , were interpreted as positive impacts on forest stand above-ground net productivity at the selected study sites. © 2018 John Wiley & Sons Ltd.
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Fortes, Nara Lúcia Perondi; Navas-Cortés, Juan A; Silva, Carlos Alberto; Bettiol, Wagner
2016-01-01
The objectives of this study were to evaluate the combined effects of soil biotic and abiotic factors on the incidence of Fusarium corn stalk rot, during four annual incorporations of two types of sewage sludge into soil in a 5-years field assay under tropical conditions and to predict the effects of these variables on the disease. For each type of sewage sludge, the following treatments were included: control with mineral fertilization recommended for corn; control without fertilization; sewage sludge based on the nitrogen concentration that provided the same amount of nitrogen as in the mineral fertilizer treatment; and sewage sludge that provided two, four and eight times the nitrogen concentration recommended for corn. Increasing dosages of both types of sewage sludge incorporated into soil resulted in increased corn stalk rot incidence, being negatively correlated with corn yield. A global analysis highlighted the effect of the year of the experiment, followed by the sewage sludge dosages. The type of sewage sludge did not affect the disease incidence. A multiple logistic model using a stepwise procedure was fitted based on the selection of a model that included the three explanatory parameters for disease incidence: electrical conductivity, magnesium and Fusarium population. In the selected model, the probability of higher disease incidence increased with an increase of these three explanatory parameters. When the explanatory parameters were compared, electrical conductivity presented a dominant effect and was the main variable to predict the probability distribution curves of Fusarium corn stalk rot, after sewage sludge application into the soil. PMID:27176597
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Peerenboom, L; Collard, R M; Naarding, P; Comijs, H C
2015-08-15
We investigated the association between old age depression and emotional and social loneliness. A cross-sectional study was performed using data from the Netherlands Study of Depression in Older Persons (NESDO). A total of 341 participants diagnosed with a depressive disorder, and 125 non-depressed participants were included. Depression diagnosis was confirmed with the Composite International Diagnostic Interview. Emotional and social loneliness were assessed using the De Jong Gierveld Loneliness Scale. Socio-demographic variables, social support variables, depression characteristics (Inventory of Depressive Symptoms), cognitive functioning (Mini Mental State Examination) and personality factors (the NEO- Five Factor Inventory and the Pearlin Mastery Scale) were considered as possible explanatory factors or confounders. (Multiple) logistic regression analyses were performed. Depression was strongly associated with emotional loneliness, but not with social loneliness. A higher sense of neuroticism and lower sense of mastery were the most important explanatory factors. Also, we found several other explanatory and confounding factors in the association of depression and emotional loneliness; a lower sense of extraversion and higher severity of depression. We performed a cross-sectional observational study. Therefore we cannot add evidence in regard to causation; whether depression leads to loneliness or vice versa. Depression in older persons is strongly associated with emotional loneliness but not with social loneliness. Several personality traits and the severity of depression are important in regard to the association of depression and emotional loneliness. It is important to develop interventions in which both can be treated. Copyright © 2015 Elsevier B.V. All rights reserved.
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The variability puzzle in human memory.
Kahana, Michael J; Aggarwal, Eash V; Phan, Tung D
2018-04-26
Memory performance exhibits a high level of variability from moment to moment. Much of this variability may reflect inadequately controlled experimental variables, such as word memorability, past practice and subject fatigue. Alternatively, stochastic variability in performance may largely reflect the efficiency of endogenous neural processes that govern memory function. To help adjudicate between these competing views, the authors conducted a multisession study in which subjects completed 552 trials of a delayed free-recall task. Applying a statistical model to predict variability in each subject's recall performance uncovered modest effects of word memorability, proactive interference, and other variables. In contrast to the limited explanatory power of these experimental variables, performance on the prior list strongly predicted current list recall. These findings suggest that endogenous factors underlying successful encoding and retrieval drive variability in performance. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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A global map of suitability for coastal Vibrio cholerae under current and future climate conditions.
Escobar, Luis E; Ryan, Sadie J; Stewart-Ibarra, Anna M; Finkelstein, Julia L; King, Christine A; Qiao, Huijie; Polhemus, Mark E
2015-09-01
Vibrio cholerae is a globally distributed water-borne pathogen that causes severe diarrheal disease and mortality, with current outbreaks as part of the seventh pandemic. Further understanding of the role of environmental factors in potential pathogen distribution and corresponding V. cholerae disease transmission over time and space is urgently needed to target surveillance of cholera and other climate and water-sensitive diseases. We used an ecological niche model (ENM) to identify environmental variables associated with V. cholerae presence in marine environments, to project a global model of V. cholerae distribution in ocean waters under current and future climate scenarios. We generated an ENM using published reports of V. cholerae in seawater and freely available remotely sensed imagery. Models indicated that factors associated with V. cholerae presence included chlorophyll-a, pH, and sea surface temperature (SST), with chlorophyll-a demonstrating the greatest explanatory power from variables selected for model calibration. We identified specific geographic areas for potential V. cholerae distribution. Coastal Bangladesh, where cholera is endemic, was found to be environmentally similar to coastal areas in Latin America. In a conservative climate change scenario, we observed a predicted increase in areas with environmental conditions suitable for V. cholerae. Findings highlight the potential for vulnerability maps to inform cholera surveillance, early warning systems, and disease prevention and control. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.
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Bentall, Richard P; Rowse, Georgina; Shryane, Nick; Kinderman, Peter; Howard, Robert; Blackwood, Nigel; Moore, Rosie; Corcoran, Rhiannon
2009-03-01
Paranoid delusions are a common symptom of a range of psychotic disorders. A variety of psychological mechanisms have been implicated in their cause, including a tendency to jump to conclusions, an impairment in the ability to understand the mental states of other people (theory of mind), an abnormal anticipation of threat, and an abnormal explanatory style coupled with low self-esteem. To determine the structure of the relationships among psychological mechanisms contributing to paranoia in a transdiagnostic sample. Cross-sectional design, with relationships between predictor variables and paranoia examined by structural equation models with latent variables. Publicly funded psychiatric services in London and the North West of England. One hundred seventy-three patients with schizophrenia spectrum disorders, major depression, or late-onset schizophrenia-like psychosis, subdivided according to whether they were currently experiencing paranoid delusions. Sixty-four healthy control participants matched for appropriate demographic variables were included. Assessments of theory of mind, jumping to conclusions bias, and general intellectual functioning, with measures of threat anticipation, emotion, self-esteem, and explanatory style. The best fitting (chi(2)(96) = 131.69, P = .01; comparative fit index = 0.95; Tucker-Lewis Index = 0.96; root-mean-square error of approximation = 0.04) and most parsimonious model of the data indicated that paranoid delusions are associated with a combination of pessimistic thinking style (low self-esteem, pessimistic explanatory style, and negative emotion) and impaired cognitive performance (executive functioning, tendency to jump to conclusions, and ability to reason about the mental states of others). Pessimistic thinking correlated highly with paranoia even when controlling for cognitive performance (r = 0.65, P < .001), and cognitive performance correlated with paranoia when controlling for pessimism (r = -0.34, P < .001). Both cognitive and emotion-related processes are involved in paranoid delusions. Treatment for paranoid patients should address both types of processes.
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Calvi‐Gries, Francoise; Blonde, Lawrence; Pilorget, Valerie; Berlingieri, Joseph; Freemantle, Nick
2018-01-01
Aim To identify factors associated with documented symptomatic and severe hypoglycaemia over 4 years in people with type 2 diabetes starting insulin therapy. Materials and methods CREDIT, a prospective international observational study, collected data over 4 years on people starting any insulin in 314 centres; 2729 and 2271 people had hypoglycaemia data during the last 6 months of years 1 and 4, respectively. Multivariable logistic regression was used to select the characteristics associated with documented symptomatic hypoglycaemia, and the model was tested against severe hypoglycaemia. Results The proportions of participants reporting ≥1 non‐severe event were 18.5% and 16.6% in years 1 and 4; the corresponding proportions of those achieving a glycated haemoglobin (HbA1c) concentration <7.0% (<53 mmol/mol) were 24.6% and 18.3%, and 16.5% and 16.2% of those who did not. For severe hypoglycaemia, the proportions were 3.0% and 4.6% of people reaching target vs 1.5% and 1.1% of those not reaching target. Multivariable analysis showed that, for documented symptomatic hypoglycaemia at both years 1 and 4, baseline lower body mass index and more physical activity were predictors, and lower HbA1c was an explanatory variable in the respective year. Models for documented symptomatic hypoglycaemia predicted severe hypoglycaemia. Insulin regimen was a univariate explanatory variable, and was not retained in the multivariable analysis. Conclusions Hypoglycaemia occurred at significant rates, but was stable over 4 years despite increased insulin doses. The association with insulin regimen and with oral agent use declined over that time. Associated predictors and explanatory variables for documented symptomatic hypoglycaemia conformed to clinical impressions and could be extended to severe hypoglycaemia. Better achieved HbA1c was associated with a higher risk of hypoglycaemia. PMID:29205734
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Home, Philip; Calvi-Gries, Francoise; Blonde, Lawrence; Pilorget, Valerie; Berlingieri, Joseph; Freemantle, Nick
2018-04-01
To identify factors associated with documented symptomatic and severe hypoglycaemia over 4 years in people with type 2 diabetes starting insulin therapy. CREDIT, a prospective international observational study, collected data over 4 years on people starting any insulin in 314 centres; 2729 and 2271 people had hypoglycaemia data during the last 6 months of years 1 and 4, respectively. Multivariable logistic regression was used to select the characteristics associated with documented symptomatic hypoglycaemia, and the model was tested against severe hypoglycaemia. The proportions of participants reporting ≥1 non-severe event were 18.5% and 16.6% in years 1 and 4; the corresponding proportions of those achieving a glycated haemoglobin (HbA1c) concentration <7.0% (<53 mmol/mol) were 24.6% and 18.3%, and 16.5% and 16.2% of those who did not. For severe hypoglycaemia, the proportions were 3.0% and 4.6% of people reaching target vs 1.5% and 1.1% of those not reaching target. Multivariable analysis showed that, for documented symptomatic hypoglycaemia at both years 1 and 4, baseline lower body mass index and more physical activity were predictors, and lower HbA1c was an explanatory variable in the respective year. Models for documented symptomatic hypoglycaemia predicted severe hypoglycaemia. Insulin regimen was a univariate explanatory variable, and was not retained in the multivariable analysis. Hypoglycaemia occurred at significant rates, but was stable over 4 years despite increased insulin doses. The association with insulin regimen and with oral agent use declined over that time. Associated predictors and explanatory variables for documented symptomatic hypoglycaemia conformed to clinical impressions and could be extended to severe hypoglycaemia. Better achieved HbA1c was associated with a higher risk of hypoglycaemia. © 2017 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.
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Brusque, Corinne; Alauzet, Aline
2008-01-01
In France, as in many other countries, phoning while driving is legally restricted because of its negative impact on driving performance which increases accident risk. Nevertheless, it is still a frequently observed practice and one which has not been analyzed in detail. This study attempts to identify the profiles of those who use mobile phones while at the wheel and determine the forms taken by this use. A representative sample of 1973 French people was interviewed by phone on their driving practices and mobile phone use in everyday life and their mobile phone use while driving. Logistics regressions have been conducted to highlight the explanatory factors of phoning while driving. Strong differences between males and females have been shown. For the male population, age is the main explanatory factor of phoning while driving, followed by phone use for work-related reasons and extensive mobile phone use in everyday life. For females, high mileage and intensive use of mobile phone are the only two explanatory factors. We defined the intensive phone use at the wheel group as drivers who receive or send at least five or more calls per day while driving. There is no socio-demographic variable related to this practice. Car and phone uses in everyday life are the only explanatory factors for this intensive mobile use of the phone at the wheel.
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Poisson Regression Analysis of Illness and Injury Surveillance Data
DOE Office of Scientific and Technical Information (OSTI.GOV)
Frome E.L., Watkins J.P., Ellis E.D.
2012-12-12
The Department of Energy (DOE) uses illness and injury surveillance to monitor morbidity and assess the overall health of the work force. Data collected from each participating site include health events and a roster file with demographic information. The source data files are maintained in a relational data base, and are used to obtain stratified tables of health event counts and person time at risk that serve as the starting point for Poisson regression analysis. The explanatory variables that define these tables are age, gender, occupational group, and time. Typical response variables of interest are the number of absences duemore » to illness or injury, i.e., the response variable is a count. Poisson regression methods are used to describe the effect of the explanatory variables on the health event rates using a log-linear main effects model. Results of fitting the main effects model are summarized in a tabular and graphical form and interpretation of model parameters is provided. An analysis of deviance table is used to evaluate the importance of each of the explanatory variables on the event rate of interest and to determine if interaction terms should be considered in the analysis. Although Poisson regression methods are widely used in the analysis of count data, there are situations in which over-dispersion occurs. This could be due to lack-of-fit of the regression model, extra-Poisson variation, or both. A score test statistic and regression diagnostics are used to identify over-dispersion. A quasi-likelihood method of moments procedure is used to evaluate and adjust for extra-Poisson variation when necessary. Two examples are presented using respiratory disease absence rates at two DOE sites to illustrate the methods and interpretation of the results. In the first example the Poisson main effects model is adequate. In the second example the score test indicates considerable over-dispersion and a more detailed analysis attributes the over-dispersion to extra-Poisson variation. The R open source software environment for statistical computing and graphics is used for analysis. Additional details about R and the data that were used in this report are provided in an Appendix. Information on how to obtain R and utility functions that can be used to duplicate results in this report are provided.« less
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Salvia, Marie-Virginie; Cren-Olivé, Cécile; Vulliet, Emmanuelle
2013-11-08
Numerous chemical products are dispersed in our environment. Many of them are recognized as harmful to humans and the ecosystem. Among these harmful substances are antibiotics and steroid hormones. Currently, very few data are available on the presence and fate of these substances in the environment, in particular for solid matrices, mainly due to a lack of analytical methodologies. Indeed, soil is a very complex matrix, and the nature and composition of the soil has a significant impact on the extraction efficiency and the sensitivity of the method. For this reason a statistical approach was performed to study the influence of soil parameters (clay, silt, sand and organic carbon percentages and cation exchange capacity (CEC)) on recoveries and matrix effects of various pharmaceuticals and steroids. Thus, an analysis of covariance (ANCOVA) was performed when several substances were analyzed simultaneously, whereas a Pearson correlation was used to study the compounds individually. To the best of our knowledge, this study is the first time such an experiment was performed. The results showed that clay and organic carbon percentages as well as the CEC have an impact on the recoveries of most of the target substances, the variables being anti-correlated. This result suggests that the compounds are trapped in soils with high levels of clay and organic carbon and a high CEC. For the matrix effects, it was shown that the organic carbon content has a significant effect on steroid hormones and penicillin G matrix effects (positive correlation). Finally, interaction effects (first order) were evaluated. This latter point corresponds to the crossed effects that occur between explanatory variables (soil parameters). Indeed, the value taken by an explanatory variable can have an influence on the effect that another explanatory variable has on a dependent variable. For instance, it was shown that some parameters (silt, sand) have an impact on the effect that clay content has on recoveries. Besides, CEC and silt affect the influence that organic carbon percentage has on matrix effect. This original approach provides a better understanding of the complex interactions that occur in soil and could be useful to understand and predict the performance of an analytical method. Copyright © 2013 Elsevier B.V. All rights reserved.
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McAneney, Helen; Tully, Mark A; Hunter, Ruth F; Kouvonen, Anne; Veal, Philip; Stevenson, Michael; Kee, Frank
2015-12-12
It has been argued that though correlated with mental health, mental well-being is a distinct entity. Despite the wealth of literature on mental health, less is known about mental well-being. Mental health is something experienced by individuals, whereas mental well-being can be assessed at the population level. Accordingly it is important to differentiate the individual and population level factors (environmental and social) that could be associated with mental health and well-being, and as people living in deprived areas have a higher prevalence of poor mental health, these relationships should be compared across different levels of neighbourhood deprivation. A cross-sectional representative random sample of 1,209 adults from 62 Super Output Areas (SOAs) in Belfast, Northern Ireland (Feb 2010 - Jan 2011) were recruited in the PARC Study. Interview-administered questionnaires recorded data on socio-demographic characteristics, health-related behaviours, individual social capital, self-rated health, mental health (SF-8) and mental well-being (WEMWBS). Multi-variable linear regression analyses, with inclusion of clustering by SOAs, were used to explore the associations between individual and perceived community characteristics and mental health and mental well-being, and to investigate how these associations differed by the level of neighbourhood deprivation. Thirty-eight and 30 % of variability in the measures of mental well-being and mental health, respectively, could be explained by individual factors and the perceived community characteristics. In the total sample and stratified by neighbourhood deprivation, age, marital status and self-rated health were associated with both mental health and well-being, with the 'social connections' and local area satisfaction elements of social capital also emerging as explanatory variables. An increase of +1 in EQ-5D-3 L was associated with +1SD of the population mean in both mental health and well-being. Similarly, a change from 'very dissatisfied' to 'very satisfied' for local area satisfaction would result in +8.75 for mental well-being, but only in the more affluent of areas. Self-rated health was associated with both mental health and mental well-being. Of the individual social capital explanatory variables, 'social connections' was more important for mental well-being. Although similarities in the explanatory variables of mental health and mental well-being exist, socio-ecological interventions designed to improve them may not have equivalent impacts in rich and poor neighbourhoods.
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Barrett, Kirsten; Loboda, Tatiana; McGuire, A. David; Genet, Hélène; Hoy, Elizabeth; Kasischke, Eric
2016-01-01
Wildfire, a dominant disturbance in boreal forests, is highly variable in occurrence and behavior at multiple spatiotemporal scales. New data sets provide more detailed spatial and temporal observations of active fires and the post-burn environment in Alaska. In this study, we employ some of these new data to analyze variations in fire activity by developing three explanatory models to examine the occurrence of (1) seasonal periods of elevated fire activity using the number of MODIS active fire detections data set (MCD14DL) within an 11-day moving window, (2) unburned patches within a burned area using the Monitoring Trends in Burn Severity fire severity product, and (3) short-to-moderate interval (<60 yr) fires using areas of burned area overlap in the Alaska Large Fire Database. Explanatory variables for these three models included dynamic variables that can change over the course of the fire season, such as weather and burn date, as well as static variables that remain constant over a fire season, such as topography, drainage, vegetation cover, and fire history. We found that seasonal periods of high fire activity are associated with both seasonal timing and aggregated weather conditions, as well as the landscape composition of areas that are burning. Important static inputs to the model of seasonal fire activity indicate that when fire weather conditions are suitable, areas that typically resist fire (e.g., deciduous stands) may become more vulnerable to burning and therefore less effective as fire breaks. The occurrence of short-to-moderate interval fires appears to be primarily driven by weather conditions, as these were the only relevant explanatory variables in the model. The unique importance of weather in explaining short-to-moderate interval fires implies that fire return intervals (FRIs) will be sensitive to projected climate changes in the region. Unburned patches occur most often in younger stands, which may be related to a greater deciduous fraction of vegetation as well as lower fuel loads compared with mature stands. The fraction of unburned patches may therefore increase in response to decreasing FRIs and increased deciduousness in the region, or these may decrease if fire weather conditions become more severe.
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Spinal Cord Stimulation: Clinical Efficacy and Potential Mechanisms.
Sdrulla, Andrei D; Guan, Yun; Raja, Srinivasa N
2018-03-11
Spinal cord stimulation (SCS) is a minimally invasive therapy used for the treatment of chronic neuropathic pain. SCS is a safe and effective alternative to medications such as opioids, and multiple randomized controlled studies have demonstrated efficacy for difficult-to-treat neuropathic conditions such as failed back surgery syndrome. Conventional SCS is believed mediate pain relief via activation of dorsal column Aβ fibers, resulting in variable effects on sensory and pain thresholds, and measurable alterations in higher order cortical processing. Although potentiation of inhibition, as suggested by Wall and Melzack's gate control theory, continues to be the leading explanatory model, other segmental and supraspinal mechanisms have been described. Novel, non-standard, stimulation waveforms such as high-frequency and burst have been shown in some studies to be clinically superior to conventional SCS, however their mechanisms of action remain to be determined. Additional studies are needed, both mechanistic and clinical, to better understand optimal stimulation strategies for different neuropathic conditions, improve patient selection and optimize efficacy. © 2018 World Institute of Pain.
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Farris, Samantha G; Zvolensky, Michael J; Norton, Peter J; Hogan, Julianna; Smith, Angela H; Talkovsky, Alexander M; Garey, Lorra; Schmidt, Norman B
2016-01-01
Limited work has examined worry, or apprehensive anticipation about future negative events, in terms of smoking. One potential explanatory factor is the tendency to respond inflexibly and with avoidance in the presence of smoking-related distress (smoking-specific experiential avoidance). Participants (n = 465) were treatment-seeking daily smokers. Cross-sectional (pre-treatment) self-report data were utilized to assess trait worry, smoking-specific experiential avoidance, and four smoking criterion variables: nicotine dependence, motivational aspects of quitting, perceived barriers to smoking cessation, and severity of problematic symptoms reported in past quit attempts. Trait worry was significantly associated with greater levels of nicotine dependence, motivation to quit smoking, perceived barriers for smoking cessation, and more severe problems while quitting in the past; associations occurred indirectly through higher levels of smoking-specific experiential avoidance. Findings provide initial support for the potential role of smoking-specific experiential avoidance in explaining the association between trait worry and a variety of smoking processes.
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Modeling the association between HR variability and illness in elite swimmers
Hellard, Philippe; Guimaraes, Fanny; Avalos, Marta; Houel, Nicolas; Hausswirth, Christophe; Toussaint, Jean François
2011-01-01
Purpose To determine whether heart rate variability, an indirect measure of autonomic control, is associated with upper respiratory tract and pulmonary infections, muscular affections and all-type pathologies in elite swimmers. Methods Seven elite international and 11 national swimmers were followed weekly for two years. The indexes of cardiac autonomic regulation in supine and orthostatic position were assessed as explanatory variables by time-domain (SD1, SD2) and spectral analyses (high frequency- HF; 0.15 Hz-0.40Hz, low frequency-LF; 0.04-0.15 Hz and HF/LF ratio) of heart rate variability. Logistic mixed models described the relationship between the explanatory variables and the risk of upper respiratory tract and pulmonary infections, muscular affections and all-type pathologies. Results The risk of all-type pathologies was higher for national swimmers and in winter (p<0.01). An increase in the parasympathetic indexes (HF, SD1) in supine position assessed one week earlier was linked to a higher risk of upper respiratory tract and pulmonary infections (p<0.05), and to a higher risk of muscular affections (increase in HF, p<0.05). Multivariate analyses showed: (1) a higher all-type pathologies risk in winter, and for an increase in the total power of heart rate variability associated with a decline SD1 in supine position; (2) a higher all-type pathologies risk in winter associated with a decline in HF assessed one week earlier in orthostatic position; and (3) a higher risk of muscular affections in winter associated with a decrease SD1 and an increase LF in orthostatic position. Conclusion Swimmers’ health maintenance requires particular attention when autonomic balance shows a sudden increase in parasympathetic indices in supine position assessed one week earlier evolving toward sympathetic predominance in supine and orthostatic positions. PMID:21085039
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Bayesian LASSO, scale space and decision making in association genetics.
Pasanen, Leena; Holmström, Lasse; Sillanpää, Mikko J
2015-01-01
LASSO is a penalized regression method that facilitates model fitting in situations where there are as many, or even more explanatory variables than observations, and only a few variables are relevant in explaining the data. We focus on the Bayesian version of LASSO and consider four problems that need special attention: (i) controlling false positives, (ii) multiple comparisons, (iii) collinearity among explanatory variables, and (iv) the choice of the tuning parameter that controls the amount of shrinkage and the sparsity of the estimates. The particular application considered is association genetics, where LASSO regression can be used to find links between chromosome locations and phenotypic traits in a biological organism. However, the proposed techniques are relevant also in other contexts where LASSO is used for variable selection. We separate the true associations from false positives using the posterior distribution of the effects (regression coefficients) provided by Bayesian LASSO. We propose to solve the multiple comparisons problem by using simultaneous inference based on the joint posterior distribution of the effects. Bayesian LASSO also tends to distribute an effect among collinear variables, making detection of an association difficult. We propose to solve this problem by considering not only individual effects but also their functionals (i.e. sums and differences). Finally, whereas in Bayesian LASSO the tuning parameter is often regarded as a random variable, we adopt a scale space view and consider a whole range of fixed tuning parameters, instead. The effect estimates and the associated inference are considered for all tuning parameters in the selected range and the results are visualized with color maps that provide useful insights into data and the association problem considered. The methods are illustrated using two sets of artificial data and one real data set, all representing typical settings in association genetics.
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Santini, María Soledad; Utgés, María Eugenia; Berrozpe, Pablo; Manteca Acosta, Mariana; Casas, Natalia; Heuer, Paola; Salomón, O. Daniel
2015-01-01
The principal objective of this study was to assess a modeling approach to Lu. longipalpis distribution in an urban scenario, discriminating micro-scale landscape variables at microhabitat and macrohabitat scales and the presence from the abundance of the vector. For this objective, we studied vectors and domestic reservoirs and evaluated different environmental variables simultaneously, so we constructed a set of 13 models to account for micro-habitats, macro-habitats and mixed-habitats. We captured a total of 853 sandflies, of which 98.35% were Lu. longipalpis. We sampled a total of 197 dogs; 177 of which were associated with households where insects were sampled. Positive rK39 dogs represented 16.75% of the total, of which 47% were asymptomatic. Distance to the border of the city and high to medium density vegetation cover ended to be the explanatory variables, all positive, for the presence of sandflies in the city. All variables in the abundance model ended to be explanatory, trees around the trap, distance to the stream and its quadratic, being the last one the only one with negative coefficient indicating that the maximum abundance was associated with medium values of distance to the stream. The spatial distribution of dogs infected with L. infantum showed a heterogeneous pattern throughout the city; however, we could not confirm an association of the distribution with the variables assessed. In relation to Lu. longipalpis distribution, the strategy to discriminate the micro-spatial scales at which the environmental variables were recorded allowed us to associate presence with macrohabitat variables and abundance with microhabitat and macrohabitat variables. Based on the variables associated with Lu. longipalpis, the model will be validated in other cities and environmental surveillance, and control interventions will be proposed and evaluated in the microscale level and integrated with socio-cultural approaches and programmatic and village (mesoscale) strategies. PMID:26274318
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Santini, María Soledad; Utgés, María Eugenia; Berrozpe, Pablo; Manteca Acosta, Mariana; Casas, Natalia; Heuer, Paola; Salomón, O Daniel
2015-01-01
The principal objective of this study was to assess a modeling approach to Lu. longipalpis distribution in an urban scenario, discriminating micro-scale landscape variables at microhabitat and macrohabitat scales and the presence from the abundance of the vector. For this objective, we studied vectors and domestic reservoirs and evaluated different environmental variables simultaneously, so we constructed a set of 13 models to account for micro-habitats, macro-habitats and mixed-habitats. We captured a total of 853 sandflies, of which 98.35% were Lu. longipalpis. We sampled a total of 197 dogs; 177 of which were associated with households where insects were sampled. Positive rK39 dogs represented 16.75% of the total, of which 47% were asymptomatic. Distance to the border of the city and high to medium density vegetation cover ended to be the explanatory variables, all positive, for the presence of sandflies in the city. All variables in the abundance model ended to be explanatory, trees around the trap, distance to the stream and its quadratic, being the last one the only one with negative coefficient indicating that the maximum abundance was associated with medium values of distance to the stream. The spatial distribution of dogs infected with L. infantum showed a heterogeneous pattern throughout the city; however, we could not confirm an association of the distribution with the variables assessed. In relation to Lu. longipalpis distribution, the strategy to discriminate the micro-spatial scales at which the environmental variables were recorded allowed us to associate presence with macrohabitat variables and abundance with microhabitat and macrohabitat variables. Based on the variables associated with Lu. longipalpis, the model will be validated in other cities and environmental surveillance, and control interventions will be proposed and evaluated in the microscale level and integrated with socio-cultural approaches and programmatic and village (mesoscale) strategies.
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Bayesian dynamical systems modelling in the social sciences.
Ranganathan, Shyam; Spaiser, Viktoria; Mann, Richard P; Sumpter, David J T
2014-01-01
Data arising from social systems is often highly complex, involving non-linear relationships between the macro-level variables that characterize these systems. We present a method for analyzing this type of longitudinal or panel data using differential equations. We identify the best non-linear functions that capture interactions between variables, employing Bayes factor to decide how many interaction terms should be included in the model. This method punishes overly complicated models and identifies models with the most explanatory power. We illustrate our approach on the classic example of relating democracy and economic growth, identifying non-linear relationships between these two variables. We show how multiple variables and variable lags can be accounted for and provide a toolbox in R to implement our approach.
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Smooth conditional distribution function and quantiles under random censorship.
Leconte, Eve; Poiraud-Casanova, Sandrine; Thomas-Agnan, Christine
2002-09-01
We consider a nonparametric random design regression model in which the response variable is possibly right censored. The aim of this paper is to estimate the conditional distribution function and the conditional alpha-quantile of the response variable. We restrict attention to the case where the response variable as well as the explanatory variable are unidimensional and continuous. We propose and discuss two classes of estimators which are smooth with respect to the response variable as well as to the covariate. Some simulations demonstrate that the new methods have better mean square error performances than the generalized Kaplan-Meier estimator introduced by Beran (1981) and considered in the literature by Dabrowska (1989, 1992) and Gonzalez-Manteiga and Cadarso-Suarez (1994).
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ERIC Educational Resources Information Center
Hong, Jun Sung; Espelage, Dorothy L.; Grogan-Kaylor, Andrew; Allen-Meares, Paula
2012-01-01
A growing body of literature is demonstrating associations between childhood maltreatment and bullying involvement at school. In this literature review, four potential mediators (explanatory) and three potential moderators (mitigates or exacerbates) of the association between childhood maltreatment and school bullying are proposed. Mediators…
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Comparison of stream invertebrate response models for bioassessment metric
Waite, Ian R.; Kennen, Jonathan G.; May, Jason T.; Brown, Larry R.; Cuffney, Thomas F.; Jones, Kimberly A.; Orlando, James L.
2012-01-01
We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R2 from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R2 values than RICHTOL for the two regions tested. Modeled O/E R2 values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.
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Identifying the controls of wildfire activity in Namibia using multivariate statistics
NASA Astrophysics Data System (ADS)
Mayr, Manuel; Le Roux, Johan; Samimi, Cyrus
2015-04-01
Despite large areas of Namibia being unaffected by fires due to aridity, substantial burning in the northern and north-eastern parts of the country is observed every year. Within the fire-affected regions, a strong spatial and inter-annual variability characterizes the dry-season fire situation. In order to understand these patterns, it appears critical to identify the causative factors behind fire occurrence and to examine their interactions in detail. Furthermore, most studies dealing with causative factor examination focus either on the local or the regional scale. However, these scales seem to be inappropriate from a management perspective, as fire-related strategic action plans are most often set up nationwide. Here, we will present an examination of the fire regimes of Namibia based on a dataset conducted by Le Roux (2011). A decade-spanning fire record (1994-2003) derived from NOAA's Advanced Very High Resolution Radiometer (AVHRR) imagery was used to generate four fire regime metrics (Burned Area, Fire Season Length, Month of Peak Fire Season, and Fire Return Period) and quantitative information on vegetation and phenology derived from Normalized Difference Vegetation Index (NDVI) time series. Further variables contained by this dataset are related to climate, biodiversity, and human activities. Le Roux (2011) analyzed the correlations between the fire metrics mentioned above and the predictor variables. We hypothesize that linear correlations (as estimated by correlation coefficients) simplify the interactions between response and predictor variables. For instance, moderate population densities could induce the highest number of fires, whereas the complete absence of humans lacks one major source of ignition. Around highly populated areas, in contrary, fuels are usually reduced and space is more fragmented - thus, the initiation and spread of a potential fire could as well be inhibited. From a total of over 40 explanatory variables, we will initially use data mining techniques to select a conceivable set of variables by their explanatory value and to remove redundancy. We will then apply two multivariate statistical methods suitable to a large variety of data types and frequently used for (non-linear) causative factor identification: Non-metric Multidimensional Scaling (NMDS) and Regression Trees. The assumed value of these analyses is i) to determine the most important predictor variables of fire activity in Namibia, ii) to decipher their complex interactions in driving fire variability in Namibia, and iii) to compare the performance of two state-of-the-art statistical methods. References: Le Roux, J. (2011): The effect of land use practices on the spatial and temporal characteristics of savanna fires in Namibia. Doctoral thesis at the University of Erlangen-Nuremberg/Germany - 155 pages.
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Missed Nursing Care and Unit-Level Nurse Workload in the Acute and Post-Acute Settings.
Orique, Sabrina B; Patty, Christopher M; Woods, Ellen
2016-01-01
This study replicates previous research on the nature and causes of missed nursing care and adds an explanatory variable: unit-level nurse workload (patient turnover percentage). The study was conducted in California, which legally mandates nurse staffing ratios. Findings demonstrated no significant relationship between patient turnover and missed nursing care.
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Nature and Nurture by Definition Means Both: A Response to Males
ERIC Educational Resources Information Center
DeLisi, Matt; Wright, John Paul; Vaughn, Michael G.; Beaver, Kevin M.
2010-01-01
Recognition of the interplay between nature and nurture is decades old in fields such as psychiatry, but other fields in the social sciences continue to be hampered by the idea that social and biological variables compete for explanatory relevance. In a recent study of the adolescent brain and risk taking, Males critiqued biologically oriented…
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Broken Homes: Stable Risk, Changing Reasons, Changing Forms.
ERIC Educational Resources Information Center
Sweetser, Dorrian Apple
1985-01-01
Cohort membership and two measures of social disadvantage were used as explanatory variables in analysis of the risk of growing up in a broken home and of the living arrangements of children with broken homes. The risk of a broken home by age 16 proved to be stable across cohorts and greater for those from disadvantaged homes. (Author/BL)
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ERIC Educational Resources Information Center
Gutierrez, Antonio P.; Price, Addison F.
2017-01-01
This study investigated changes in male and female students' prediction and postdiction calibration accuracy and bias scores, and the predictive effects of explanatory styles on these variables beyond gender. Seventy undergraduate students rated their confidence in performance before and after a 40-item exam. There was an improvement in students'…
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12 CFR Appendix A to Subpart B of... - Risk-Based Capital Test Methodology and Specifications
Code of Federal Regulations, 2013 CFR
2013-01-01
....3.2, Mortgage Amortization Schedule Inputs 3-32, Loan Group Inputs for Mortgage Amortization... Prepayment Explanatory Variables F 3.6.3.5.2, Multifamily Default and Prepayment Inputs 3-38, Loan Group... Group inputs for Gross Loss Severity F 3.3.4, Interest Rates Outputs3.6.3.3.4, Mortgage Amortization...
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12 CFR Appendix A to Subpart B of... - Risk-Based Capital Test Methodology and Specifications
Code of Federal Regulations, 2011 CFR
2011-01-01
....3.2, Mortgage Amortization Schedule Inputs 3-32, Loan Group Inputs for Mortgage Amortization... Prepayment Explanatory Variables F 3.6.3.5.2, Multifamily Default and Prepayment Inputs 3-38, Loan Group... Group inputs for Gross Loss Severity F 3.3.4, Interest Rates Outputs3.6.3.3.4, Mortgage Amortization...
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ERIC Educational Resources Information Center
McCormick, Alexander C.; Pike, Gary R.; Kuh, George D.; Chen, Pu-Shih Daniel
2009-01-01
This study compares the explanatory power of the 2000 edition of Carnegie Classification, the 2005 revision of the classification, and selected variables underlying Carnegie's expanded 2005 classification system using data from the National Survey of Student Engagement's spring 2004 administration. Results indicate that the 2000 and 2005…
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Location and Lifestyle: The Comparative Explanatory Ability of Urbanism and Rurality
ERIC Educational Resources Information Center
Lowe, George D.; Peek, Charles W.
1974-01-01
The article focuses on 2 questions pivotal to the issue of rural-urban differences: 1) "Do attitudinal differences remain among the rural and urban residents independent of differences generated by other potent variables?"; and 2) "Will any increase in the predictive utility of rurality be generated by use of a composite definition (residence plus…
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Five Decades of Educational Assortative Mating in 10 East Asian Societies
ERIC Educational Resources Information Center
Smits, Jeroen; Park, Hyunjoon
2009-01-01
We study trends in educational homogamy at six boundaries in the educational structure of 10 East-Asian societies and explain its variation using explanatory variables at the country, cohort and boundary level. Educational homogamy was higher at the higher boundaries in the educational structure. Since the 1950s it decreased at all but the lowest…
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ERIC Educational Resources Information Center
Alltucker, Kevin W.; Bullis, Michael; Close, Daniel; Yovanoff, Paul
2006-01-01
We examined the differences between early and late start juvenile delinquents in a sample of 531 previously incarcerated youth in Oregon's juvenile justice system. Data were analyzed with logistic regression to predict early start delinquency based on four explanatory variables: foster care experience, family criminality, special education…
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ERIC Educational Resources Information Center
Köhler, Carmen; Pohl, Steffi; Carstensen, Claus H.
2017-01-01
Competence data from low-stakes educational large-scale assessment studies allow for evaluating relationships between competencies and other variables. The impact of item-level nonresponse has not been investigated with regard to statistics that determine the size of these relationships (e.g., correlations, regression coefficients). Classical…
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ERIC Educational Resources Information Center
Dammeyer, Jesper
2010-01-01
Research has shown a prevalence of psychosocial difficulties ranging from about 20% to 50% among children with hearing loss. This study evaluates the prevalence of psychosocial difficulties in a Danish population in relation to different explanatory variables. Five scales and questionnaires measuring sign language, spoken language, hearing…
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John W. Coulston
2011-01-01
Tropospheric ozone occurs at phytotoxic levels in the United States (Lefohn and Pinkerton 1988). Several plant species, including commercially important timber species, are sensitive to elevated ozone levels. Exposure to elevated ozone can cause growth reduction and foliar injury and make trees more susceptible to secondary stressors such as insects and pathogens (...
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Predicting Item Difficulty of Science National Curriculum Tests: The Case of Key Stage 2 Assessments
ERIC Educational Resources Information Center
El Masri, Yasmine H.; Ferrara, Steve; Foltz, Peter W.; Baird, Jo-Anne
2017-01-01
Predicting item difficulty is highly important in education for both teachers and item writers. Despite identifying a large number of explanatory variables, predicting item difficulty remains a challenge in educational assessment with empirical attempts rarely exceeding 25% of variance explained. This paper analyses 216 science items of key stage…
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Basic Math Skills and Performance in an Introductory Economics Class
ERIC Educational Resources Information Center
Ballard, Charles L.; Johnson, Marianne F.
2004-01-01
The authors measure math skills with a broader set of explanatory variables than have been used in previous studies. To identify what math skills are important for student success in introductory microeconomics, they examine (1) the student's score on the mathematics portion of the ACT Assessment Test, (2) whether the student has taken calculus,…
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A Question of Effectiveness: Recruitment of Special Educators within High School Peer Support Groups
ERIC Educational Resources Information Center
Zascavage, Victoria; Winterman, Kathy; Armstrong, Philip; Schroeder-Steward, Jennifer
2008-01-01
The present study combines information about support groups for students with disabilities from 187 East Texas high schools with explanatory variables taken from data of the Texas Education Agency Academic Excellence Indicator System. This study is a tangential section of a larger study on the influence of peer support groups in East Texas…
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12 CFR Appendix A to Subpart B of... - Risk-Based Capital Test Methodology and Specifications
Code of Federal Regulations, 2012 CFR
2012-01-01
....3.2, Mortgage Amortization Schedule Inputs 3-32, Loan Group Inputs for Mortgage Amortization... Prepayment Explanatory Variables F 3.6.3.5.2, Multifamily Default and Prepayment Inputs 3-38, Loan Group... Group inputs for Gross Loss Severity F 3.3.4, Interest Rates Outputs3.6.3.3.4, Mortgage Amortization...
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12 CFR Appendix A to Subpart B of... - Risk-Based Capital Test Methodology and Specifications
Code of Federal Regulations, 2014 CFR
2014-01-01
....3.2, Mortgage Amortization Schedule Inputs 3-32, Loan Group Inputs for Mortgage Amortization... Prepayment Explanatory Variables F 3.6.3.5.2, Multifamily Default and Prepayment Inputs 3-38, Loan Group... Group inputs for Gross Loss Severity F 3.3.4, Interest Rates Outputs3.6.3.3.4, Mortgage Amortization...
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Skalická, Věra; Ringdal, Kristen; Witvliet, Margot I.
2015-01-01
Background Socioeconomic inequalities in mortality can be explained by different groups of risk factors. However, little is known whether repeated measurement of risk factors can provide better explanation of socioeconomic inequalities in health. Our study examines the extent to which relative educational and income inequalities in mortality might be explained by explanatory risk factors (behavioral, psychosocial, biomedical risk factors and employment) measured at two points in time, as compared to one measurement at baseline. Methods and Findings From the Norwegian total county population-based HUNT Study (years 1984–86 and 1995–1997, respectively) 61 513 men and women aged 25–80 (82.5% of all enrolled) were followed-up for mortality in 25 years until 2009, employing a discrete time survival analysis. Socioeconomic inequalities in mortality were observed. As compared to their highest socioeconomic counterparts, the lowest educated men had an OR (odds ratio) of 1.41 (95% CI 1.29–1.55) and for the lowest income quartile OR = 1.59 (1.48–1.571), for women OR = 1.35 (1.17–1.55), and OR = 1.40 (1.28–1.52), respectively. Baseline explanatory variables attenuated the association between education and income with mortality by 54% and 54% in men, respectively, and by 69% and 18% in women. After entering time-varying variables, this attainment increased to 63% and 59% in men, respectively, and to 25% (income) in women, with no improvement in regard to education in women. Change in biomedical factors and employment did not amend the explanation. Conclusions Addition of a second measurement for risk factors provided only a modest improvement in explaining educational and income inequalities in mortality in Norwegian men and women. Accounting for change in behavior provided the largest improvement in explained inequalities in mortality for both men and women, as compared to measurement at baseline. Psychosocial factors explained the largest share of income inequalities in mortality for men, but repeated measurement of these factors contributed only to modest improvement in explanation. Further comparative research on the relative importance of explanatory pathways assessed over time is needed. PMID:25853571
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Skalická, Věra; Ringdal, Kristen; Witvliet, Margot I
2015-01-01
Socioeconomic inequalities in mortality can be explained by different groups of risk factors. However, little is known whether repeated measurement of risk factors can provide better explanation of socioeconomic inequalities in health. Our study examines the extent to which relative educational and income inequalities in mortality might be explained by explanatory risk factors (behavioral, psychosocial, biomedical risk factors and employment) measured at two points in time, as compared to one measurement at baseline. From the Norwegian total county population-based HUNT Study (years 1984-86 and 1995-1997, respectively) 61 513 men and women aged 25-80 (82.5% of all enrolled) were followed-up for mortality in 25 years until 2009, employing a discrete time survival analysis. Socioeconomic inequalities in mortality were observed. As compared to their highest socioeconomic counterparts, the lowest educated men had an OR (odds ratio) of 1.41 (95% CI 1.29-1.55) and for the lowest income quartile OR = 1.59 (1.48-1.571), for women OR = 1.35 (1.17-1.55), and OR = 1.40 (1.28-1.52), respectively. Baseline explanatory variables attenuated the association between education and income with mortality by 54% and 54% in men, respectively, and by 69% and 18% in women. After entering time-varying variables, this attainment increased to 63% and 59% in men, respectively, and to 25% (income) in women, with no improvement in regard to education in women. Change in biomedical factors and employment did not amend the explanation. Addition of a second measurement for risk factors provided only a modest improvement in explaining educational and income inequalities in mortality in Norwegian men and women. Accounting for change in behavior provided the largest improvement in explained inequalities in mortality for both men and women, as compared to measurement at baseline. Psychosocial factors explained the largest share of income inequalities in mortality for men, but repeated measurement of these factors contributed only to modest improvement in explanation. Further comparative research on the relative importance of explanatory pathways assessed over time is needed.
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Applying the Expectancy-Value Model to understand health values.
Zhang, Xu-Hao; Xie, Feng; Wee, Hwee-Lin; Thumboo, Julian; Li, Shu-Chuen
2008-03-01
Expectancy-Value Model (EVM) is the most structured model in psychology to predict attitudes by measuring attitudinal attributes (AAs) and relevant external variables. Because health value could be categorized as attitude, we aimed to apply EVM to explore its usefulness in explaining variances in health values and investigate underlying factors. Focus group discussion was carried out to identify the most common and significant AAs toward 5 different health states (coded as 11111, 11121, 21221, 32323, and 33333 in EuroQol Five-Dimension (EQ-5D) descriptive system). AAs were measured in a sum of multiplications of subjective probability (expectancy) and perceived value of attributes with 7-point Likert scales. Health values were measured using visual analog scales (VAS, range 0-1). External variables (age, sex, ethnicity, education, housing, marital status, and concurrent chronic diseases) were also incorporated into survey questionnaire distributed by convenience sampling among eligible respondents. Univariate analyses were used to identify external variables causing significant differences in VAS. Multiple linear regression model (MLR) and hierarchical regression model were used to investigate the explanatory power of AAs and possible significant external variable(s) separately or in combination, for each individual health state and a mixed scenario of five states, respectively. Four AAs were identified, namely, "worsening your quality of life in terms of health" (WQoL), "adding a burden to your family" (BTF), "making you less independent" (MLI) and "unable to work or study" (UWS). Data were analyzed based on 232 respondents (mean [SD] age: 27.7 [15.07] years, 49.1% female). Health values varied significantly across 5 health states, ranging from 0.12 (33333) to 0.97 (11111). With no significant external variables identified, EVM explained up to 62% of the variances in health values across 5 health states. The explanatory power of 4 AAs were found to be between 13% and 28% in separate MLR models (P < 0.05). When data were analyzed for each health state, variances in health values became small and explanatory power of EVM was reduced to a range between 8% and 23%. EVM was useful in explaining variances of health values and predicting important factors. Its power to explain small variances might be restricted due to limitations of 7-point Likert scale to measure AAs accurately. With further improvement and validation of a compatible continuous scale for more accurate measurement, EVM is expected to explain health values to a larger extent.
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Blame attribution as a moderator of perceptions of sexual orientation-based hate crimes.
Cramer, Robert J; Chandler, Joseph F; Wakeman, Emily E
2010-05-01
Blame attribution is a valuable mechanism explaining decision making. However, present literature mainly employs blame attribution as a dependent variable. The shortcoming of this fact is that blame attribution offers a potentially valuable explanatory mechanism for decision making. The authors designed two studies to investigate blame attribution as a moderator of sentencing decisions in sexual orientation-based hate crimes. Study 1 showed that mock jurors punished perpetrators of hate crimes more severely than a control condition. Also, degree of victim blame influenced punitive decision making. In Study 2, mock jurors extended findings that perpetrators of hate crimes are more harshly punished than those of other types of crimes. Victim and perpetrator blame failed to moderate decision making in this more complex scenario. Results are discussed in relation to hate crimes definitions and attribution theory.
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Do Changes in Hospital Outpatient Payments Affect the Setting of Care?
He, Daifeng; Mellor, Jennifer M
2013-01-01
Objective To examine whether decreases in Medicare outpatient payment rates under the Outpatient Prospective Payment System (OPPS) caused outpatient care to shift toward the inpatient setting. Data Sources/Study Setting Hospital inpatient and outpatient discharge files from the Florida Agency for Health Care Administration from 1997 through 2008. Study Design This study focuses on inguinal hernia repair surgery, one of the most commonly performed surgical procedures in the United States. We estimate multivariate regressions of inguinal hernia surgery counts in the outpatient setting and in the inpatient setting. The key explanatory variable is the time-varying Medicare payment rate specific to the procedure and hospital. Control variables include time-varying hospital and county characteristics and hospital and year-fixed effects. Principal Findings Outpatient hernia surgeries fell in response to OPPS-induced rate cuts. The volume of inpatient hernia repair surgeries did not increase in response to reductions in the outpatient reimbursement rate. Conclusions Potential substitution from the outpatient setting to the inpatient setting does not pose a serious threat to Medicare's efforts to contain hospital outpatient costs. PMID:23701048
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Watershed Influences on Residence Time and Oxygen Reduction Rates in an Agricultural Landscape
NASA Astrophysics Data System (ADS)
Shope, C. L.; Tesoriero, A. J.
2015-12-01
Agricultural use of synthetic fertilizers and animal manure has led to increased crop production, but also elevated nitrogen concentrations in groundwater, resulting in impaired water quality. Groundwater oxygen concentrations are a key indicator of potential biogeochemical processes, which control water/aquifer interactions and contaminant transport. The U.S. Geological Survey's National Water-Quality Assessment Program has a long-history of studying nutrient transport and processing across the United States and the Glacial Aquifer system in particular. A series of groundwater well networks in Eastern Wisconsin is being used to evaluate the distribution of redox reaction rates over a range of scales with a focus on dissolved O2 reduction rates. An analysis of these multi-scale networks elucidates the influence of explanatory variables (i.e.: soil type, land use classification) on reduction rates and redox reactions throughout the Fox-Wolf-Peshtigo watersheds. Multiple tracers including dissolved gasses, tritium, helium, chlorofluorocarbons, sulfur hexafluoride, and carbon-14 were used to estimate groundwater ages (0.8 to 61.2 yr) at over 300 locations. Our results indicate O2 reduction rates along a flowpath study area (1.2 km2) of 0.15 mg O2 L-1 yr-1 (0.12 to 0.18 mg O2 L-1 yr-1) up to 0.41 mg O2 L-1 yr-1 (0.23 to 0.89 mg O2 L-1 yr-1) for a larger scale land use study area (3,300 km2). Preliminary explanatory variables that can be used to describe the variability in reduction rates include soil type (hydrologic group, bulk density) and chemical concentrations (nitrite plus nitrate, silica). The median residence time expected to reach suboxic conditions (≤ 0.4 mg O2 L-1) for the flowpath and the land use study areas was 66 and 25 yr, respectively. These results can be used to elucidate and differentiate the impact of residence time on groundwater quality vulnerability and sustainability in agricultural regions without complex flow models.
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Coping with Stress and Types of Burnout: Explanatory Power of Different Coping Strategies
Montero-Marin, Jesus; Prado-Abril, Javier; Piva Demarzo, Marcelo Marcos; Gascon, Santiago; García-Campayo, Javier
2014-01-01
Background Burnout occurs when professionals use ineffective coping strategies to try to protect themselves from work-related stress. The dimensions of ‘overload’, ‘lack of development’ and ‘neglect’, belonging to the ‘frenetic’, ‘under-challenged’ and ‘worn-out’ subtypes, respectively, comprise a brief typological definition of burnout. The aim of the present study was to estimate the explanatory power of the different coping strategies on the development of burnout subtypes. Methods This was a cross-sectional survey with a random sample of university employees, stratified by occupation (n = 429). Multivariate linear regression models were constructed between the ‘Burnout Clinical Subtypes Questionnaire’, with its three dimensions –overload, lack of development and neglect– as dependent variables, and the ‘Coping Orientation for Problem Experiences’, with its fifteen dimensions, as independent variables. Adjusted multiple determination coefficients and beta coefficients were calculated to evaluate and compare the explanatory capacity of the different coping strategies. Results The ‘Coping Orientation for Problem Experiences’ subscales together explained 15% of the ‘overload’ (p<0.001), 9% of the ‘lack of development’ (p<0.001), and 21% of the ‘neglect’ (p<0.001). ‘Overload’ was mainly explained by ‘venting of emotions’ (Beta = 0.34; p<0.001); ‘lack of development’ by ‘cognitive avoidance’ (Beta = 0.21; p<0.001); and ‘neglect’ by ‘behavioural disengagement’ (Beta = 0.40; p<0.001). Other interesting associations were observed. Conclusions These findings further our understanding of the way in which the effectiveness of interventions for burnout may be improved, by influencing new treatments and preventive programmes using features of the strategies for handling stress in the workplace. PMID:24551223
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Oden, Timothy D.; Asquith, William H.; Milburn, Matthew S.
2009-01-01
In December 2005, the U.S. Geological Survey in cooperation with the City of Houston, Texas, began collecting discrete water-quality samples for nutrients, total organic carbon, bacteria (total coliform and Escherichia coli), atrazine, and suspended sediment at two U.S. Geological Survey streamflow-gaging stations upstream from Lake Houston near Houston (08068500 Spring Creek near Spring, Texas, and 08070200 East Fork San Jacinto River near New Caney, Texas). The data from the discrete water-quality samples collected during 2005-07, in conjunction with monitored real-time data already being collected - physical properties (specific conductance, pH, water temperature, turbidity, and dissolved oxygen), streamflow, and rainfall - were used to develop regression models for predicting water-quality constituent concentrations for inflows to Lake Houston. Rainfall data were obtained from a rain gage monitored by Harris County Homeland Security and Emergency Management and colocated with the Spring Creek station. The leaps and bounds algorithm was used to find the best subsets of possible regression models (minimum residual sum of squares for a given number of variables). The potential explanatory or predictive variables included discharge (streamflow), specific conductance, pH, water temperature, turbidity, dissolved oxygen, rainfall, and time (to account for seasonal variations inherent in some water-quality data). The response variables at each site were nitrite plus nitrate nitrogen, total phosphorus, organic carbon, Escherichia coli, atrazine, and suspended sediment. The explanatory variables provide easily measured quantities as a means to estimate concentrations of the various constituents under investigation, with accompanying estimates of measurement uncertainty. Each regression equation can be used to estimate concentrations of a given constituent in real time. In conjunction with estimated concentrations, constituent loads were estimated by multiplying the estimated concentration by the corresponding streamflow and applying the appropriate conversion factor. By computing loads from estimated constituent concentrations, a continuous record of estimated loads can be available for comparison to total maximum daily loads. The regression equations presented in this report are site specific to the Spring Creek and East Fork San Jacinto River streamflow-gaging stations; however, the methods that were developed and documented could be applied to other tributaries to Lake Houston for estimating real-time water-quality data for streams entering Lake Houston.
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Fernandez, C; Descamps, I; Fabjanska, K; Kaschke, I; Marks, L
2016-03-01
To evaluate the oral condition and treatment needs of young athletes with intellectual disability (ID) from 53 countries of Europe and Eurasia who participated in the Special Olympics European Games held in Antwerp, October 2014. A cross- sectional study was undertaken with data collected through standardised procedures from consenting athletes under 21 years of age. Oral hygiene habits, reports of oral pain and presence of gingival signs, sealants, untreated caries and missing teeth were recorded. Data analysis was performed in SPSS to produce descriptive statistics and explanatory variables for untreated decay, and gingival signs of disease were tested with Multilevel Generalized Linear Mixed Models. Five hundred three athletes participated in this study (mean age 17 yrs). Untreated decay was recorded in 33.4% of the participants and 38.7% of them had signs of gingival disease. Absence of untreated decay was associated with lower chances of gingival signs, while absence of sealants was related with higher chances of untreated decay. There is consistent evidence of persistent need for increased promotion of oral health, as well as preventive and restorative treatment in young athletes with ID in Europe and Eurasia. Due to the limited predictive capacity of the studied variables for oral disease, further studies including other related factors are needed.
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Martín Fernández, J; Martínez Marcos, M; Ferrándiz Santos, J
2001-04-30
To compare the evaluation of reaction of an activity of continuous education (CE) in minor surgery (MS), with the impact in the realization of MS in a health area. Observational cross-sectional study. Setting. 27 centers in a health area that offer MS between their services. The valuation was studied in a scale from 1 to 10, of 9 theoretical-practical activities of CE in MS and the consideration of its utility. The number of activities of MS (NMS) carried out in all the units, was picked up during one year, and a model of lineal regression was built. The independent variable was the NMS, and the explanatory ones the assistance pressure (AP), the postgraduate formation (PF), the staff of the unit, the equipment (E), and the carried out CE. The valuation of the CE had a median of 8 (with interquartile range 1), 85.1% of the people who realized CE in MS said that this would be of utility. However in the explanatory regression model the PF was the only significant variable (beta = 6.7; 95% CI, 0.12-12.22). Neither the CE, nor the AP, nor the E, explained the variability among the NMS. The CE in MS with conventional methodology has a very positive reaction evaluation, but its impact in the later realization of MS is not significant.
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Duinen, Rianne van; Filatova, Tatiana; Geurts, Peter; Veen, Anne van der
2015-04-01
Drought-induced water shortage and salinization are a global threat to agricultural production. With climate change, drought risk is expected to increase as drought events are assumed to occur more frequently and to become more severe. The agricultural sector's adaptive capacity largely depends on farmers' drought risk perceptions. Understanding the formation of farmers' drought risk perceptions is a prerequisite to designing effective and efficient public drought risk management strategies. Various strands of literature point at different factors shaping individual risk perceptions. Economic theory points at objective risk variables, whereas psychology and sociology identify subjective risk variables. This study investigates and compares the contribution of objective and subjective factors in explaining farmers' drought risk perception by means of survey data analysis. Data on risk perceptions, farm characteristics, and various other personality traits were collected from farmers located in the southwest Netherlands. From comparing the explanatory power of objective and subjective risk factors in separate models and a full model of risk perception, it can be concluded that farmers' risk perceptions are shaped by both rational and emotional factors. In a full risk perception model, being located in an area with external water supply, owning fields with salinization issues, cultivating drought-/salt-sensitive crops, farm revenue, drought risk experience, and perceived control are significant explanatory variables of farmers' drought risk perceptions. © 2014 Society for Risk Analysis.
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NASA Astrophysics Data System (ADS)
Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.
2017-12-01
The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.
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Treating Cancer with Amplitude-Modulated Electromagnetic Fields: A Potential Paradigm Shift, Again?
The Zimmerman et al. (2011) study published here, coupled with the group's two preceding papers (Barbault et al. (2009), Costa et al. (2011)), identify a potential modality for treating tumors at a dramatic reduction in trauma and cost. This set of clinical and explanatory labora...
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2006-03-01
identify if an explanatory variable may have been omitted due to model misspecification ( Ramsey , 1979). The RESET test resulted in failure to...Prob > F 0.0094 This model was also regressed using Huber-White estimators. Again, the Ramsey RESET test was done to ensure relevant...Aircraft. Annapolis, MD: Naval Institute Press, 2004. Ramsey , J. B. “ Tests for Specification Errors in Classical Least-Squares Regression Analysis
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ERIC Educational Resources Information Center
Donche, Vincent; De Maeyer, Sven; Coertjens, Liesje; Van Daal, Tine; Van Petegem, Peter
2013-01-01
Background. Although the evidence in support of the variability of students' learning strategies has expanded in recent years, less is known about the explanatory base of these individual differences in terms of the joint Influences of personal and contextual characteristics. Aims. Previous studies have often investigated how student learning is…
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ERIC Educational Resources Information Center
Champion, Denisha A.; Lewis, Todd F.; Myers, Jane E.
2015-01-01
The U.S. Surgeon General described college alcohol abuse as the most significant public health concern on university campuses (DHHS, 2007). Social norms have been identified as a strong predictor of college drinking and yet programs based on norms have had limited effectiveness in changing drinking behavior. Other theoretical explanations, such as…
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ERIC Educational Resources Information Center
Calvert, Carol Elaine
2014-01-01
This case study relates to distance learning students on open access courses. It demonstrates the use of predictive analytics to generate a model of the probabilities of success and retention at different points, or milestones, in a student journey. A core set of explanatory variables has been established and their varying relative importance at…
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Can Animation Be Used to Improve Comprehension of Instructional Text?
ERIC Educational Resources Information Center
Moremoholo, T. P.
2008-01-01
The aim of the study was to determine whether the animation of a linear process, requiring explanatory text, can assist students to form a better understanding of the text. Tertiary students (N = 61) participated in a pre-test, post-test experimental study during which they were exposed to 4 treatment variables: text (T), video and text (VT),…
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ERIC Educational Resources Information Center
Loreman, Tim; Sharma, Umesh; Forlin, Chris
2013-01-01
This paper reports the results of an international study examining pre-service teacher reports of teaching self-efficacy for inclusive education; principally focusing on the explanatory relationship between a scale designed to measure teaching self-efficacy in this area and key demographic variables within Canada, Australia, Hong Kong, and…
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ERIC Educational Resources Information Center
Chen, Chau-Kuang
2005-01-01
Logistic and Cox regression methods are practical tools used to model the relationships between certain student learning outcomes and their relevant explanatory variables. The logistic regression model fits an S-shaped curve into a binary outcome with data points of zero and one. The Cox regression model allows investigators to study the duration…
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Fuel load modeling from mensuration attributes in temperate forests in northern Mexico
Maricela Morales-Soto; Marín Pompa-Garcia
2013-01-01
The study of fuels is an important factor in defining the vulnerability of ecosystems to forest fires. The aim of this study was to model a dead fuel load based on forest mensuration attributes from forest management inventories. A scatter plot analysis was performed and, from explanatory trends between the variables considered, correlation analysis was carried out...
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ERIC Educational Resources Information Center
Kerkvliet, J.; Nowell, C.
2005-01-01
We develop and empirically implement a model of university student retention using opportunity cost, financial aid, academic and social integration, and students' background explanatory variables. For one year, we tracked students from Weber State University (WSU) and Oregon State University (OSU) to learn whether they remained enrolled for 0, 1,…
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POLO2: a user's guide to multiple Probit Or LOgit analysis
Robert M. Russell; N. E. Savin; Jacqueline L. Robertson
1981-01-01
This guide provides instructions for the use of POLO2, a computer program for multivariate probit or logic analysis of quantal response data. As many as 3000 test subjects may be included in a single analysis. Including the constant term, up to nine explanatory variables may be used. Examples illustrating input, output, and uses of the program's special features...
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The Impact of Normative Environments on Learner Motivation and L2 Reading Ability Growth
ERIC Educational Resources Information Center
Sasaki, Miyuki; Kozaki, Yoko; Ross, Steven J.
2017-01-01
This study explores the effects of various motivational variables operating within 44 English classes on 1-year-long gains in the English reading proficiency of 1,149 Japanese university students. The study adds new knowledge to the recent outcomes of second language (L2) motivational studies in 3 major ways. First, the explanatory variables…
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2002-06-01
3 = Divorced ) Number of Dependents Self-Explanatory Continuous Variable Related Job Experience Was job experience related to college program...Crawford Naval Postgraduate School Monterrey , CA 7. Professor Roger Little U. S. Naval Academy Annapolis, MD 8. LT Nicholas A. Kristof Chester, MD 9. Mr. and Mrs. Zoltan J. Kristof Pittsburgh, PA
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Misut, P.
1995-01-01
Ninety shallow monitoring wells on Long Island, N.Y., were used to test the hypothesis that the correlation between the detection of volatile organic compounds (VOC's) at a well and explanatory variables representing land use, population density, and hydrogeologic conditions around the well is affected by the size and shape of the area defined as the contributing area. Explanatory variables are quantified through overlay of various specified contributing areas on 1:24 000-scale landuse and population-density geographic information system (GIS) coverages. Four methods of contributing-area delineation were used: (a) centering a circle of selected radius on the well site, (b) orienting a triangular area along the direction of horizontal ground-water flow to the well, (c) generating a shaped based on direction and magnitude of horizontal flow to the well, and (d) generating a shape based on three-dimensional particle pathlines backtracked from the well screen to the water table. The strongest correlations with VOC detections were obtained from circles of 400- to 1 000-meter radius. Improvement in correlation through delineations based on ground-water flow would require geographic overlay on more highly detailed GIS coverages than those used in the study.
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Kohler, Friedbert; Renton, Roger; Dickson, Hugh G; Estell, John; Connolly, Carol E
2011-02-01
We sought the best predictors for length of stay, discharge destination and functional improvement for inpatients undergoing rehabilitation following a stroke and compared these predictors against AN-SNAP v2. The Oxfordshire classification subgroup, sociodemographic data and functional data were collected for patients admitted between 1997 and 2007, with a diagnosis of recent stroke. The data were factor analysed using Principal Components Analysis for categorical data (CATPCA). Categorical regression analyses was performed to determine the best predictors of length of stay, discharge destination, and functional improvement. A total of 1154 patients were included in the study. Principal components analysis indicated that the data were effectively unidimensional, with length of stay being the most important component. Regression analysis demonstrated that the best predictor was the admission motor FIM score, explaining 38.9% of variance for length of stay, 37.4%.of variance for functional improvement and 16% of variance for discharge destination. The best explanatory variable in our inpatient rehabilitation service is the admission motor FIM. AN- SNAP v2 classification is a less effective explanatory variable. This needs to be taken into account when using AN-SNAP v2 classification for clinical or funding purposes.
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Anezaki, Katsunori; Nakano, Takeshi; Kashiwagi, Nobuhisa
2016-01-19
Using the chemical balance method, and considering the presence of unidentified sources, we estimated the origins of PCB contamination in surface sediments of Muroran Port, Japan. It was assumed that these PCBs originated from four types of Kanechlor products (KC300, KC400, KC500, and KC600), combustion and two kinds of pigments (azo and phthalocyanine). The characteristics of these congener patterns were summarized on the basis of principal component analysis and explanatory variables determined. A Bayesian semifactor model (CMBK2) was applied to the explanatory variables to analyze the sources of PCBs in the sediments. The resulting estimates of the contribution ratio of each kind of sediment indicate that the existence of unidentified sources can be ignored and that the assumed seven sources are adequate to account for the contamination. Within the port, the contribution ratio of KC500 and KC600 (used as paints for ship hulls) was extremely high, but outside the port, the influence of azo pigments was observable to a limited degree. This indicates that environmental PCBs not derived from technical PCBs are present at levels that cannot be ignored.
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Variable selection with stepwise and best subset approaches
2016-01-01
While purposeful selection is performed partly by software and partly by hand, the stepwise and best subset approaches are automatically performed by software. Two R functions stepAIC() and bestglm() are well designed for stepwise and best subset regression, respectively. The stepAIC() function begins with a full or null model, and methods for stepwise regression can be specified in the direction argument with character values “forward”, “backward” and “both”. The bestglm() function begins with a data frame containing explanatory variables and response variables. The response variable should be in the last column. Varieties of goodness-of-fit criteria can be specified in the IC argument. The Bayesian information criterion (BIC) usually results in more parsimonious model than the Akaike information criterion. PMID:27162786
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Lin, Guojun; Stralberg, Diana; Gong, Guiquan; Huang, Zhongliang; Ye, Wanhui; Wu, Linfang
2013-01-01
Quantifying the relative contributions of environmental conditions and spatial factors to species distribution can help improve our understanding of the processes that drive diversity patterns. In this study, based on tree inventory, topography and soil data from a 20-ha stem-mapped permanent forest plot in Guangdong Province, China, we evaluated the influence of different ecological processes at different spatial scales using canonical redundancy analysis (RDA) at the community level and multiple linear regression at the species level. At the community level, the proportion of explained variation in species distribution increased with grid-cell sizes, primarily due to a monotonic increase in the explanatory power of environmental variables. At the species level, neither environmental nor spatial factors were important determinants of overstory species' distributions at small cell sizes. However, purely spatial variables explained most of the variation in the distributions of understory species at fine and intermediate cell sizes. Midstory species showed patterns that were intermediate between those of overstory and understory species. At the 20-m cell size, the influence of spatial factors was stronger for more dispersal-limited species, suggesting that much of the spatial structuring in this community can be explained by dispersal limitation. Comparing environmental factors, soil variables had higher explanatory power than did topography for species distribution. However, both topographic and edaphic variables were highly spatial structured. Our results suggested that dispersal limitation has an important influence on fine-intermediate scale (from several to tens of meters) species distribution, while environmental variability facilitates species distribution at intermediate (from ten to tens of meters) and broad (from tens to hundreds of meters) scales.
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Sáez, M
2003-01-01
In Spain, the degree and characteristics of primary care services utilization have been the subject of analysis since at least the 1980s. One of the main reasons for this interest is to assess the extent to which utilization matches primary care needs. In fact, the provision of an adequate health service for those who most need it is a generally accepted priority. The evidence shows that individual characteristics, mainly health status, are the factors most closely related to primary care utilization. Other personal characteristics, such as gender and age, could act as modulators of health care need. Some family and/or cultural variables, as well as factors related to the health care professional and institutions, could explain some of the observed variability in primary care services utilization. Socioeconomic variables, such as income, reveal a paradox. From an aggregate perspective, income is the main determinant of utilization as well as of health care expenditure. When data are analyzed for individuals, however, income is not related to primary health utilization. The situation is controversial, with methodological implications and, above all, consequences for the assessment of the efficiency in primary care utilization. Review of the literature reveals certain methodological inconsistencies that could at least partly explain the disparity of the empirical results. Among others, the following flaws can be highlighted: design problems, measurement errors, misspecification, and misleading statistical methods.Some solutions, among others, are quasi-experiments, the use of large administrative databases and of primary data sources (design problems); differentiation between types of utilization and between units of analysis other than consultations, and correction of measurement errors in the explanatory variables (measurement errors); consideration of relevant explanatory variables (misspecification); and the use of multilevel models (statistical methods).
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A successful backward step correlates with hip flexion moment of supporting limb in elderly people.
Takeuchi, Yahiko
2018-01-01
The objective of this study was to determine the positional relationship between the center of mass (COM) and the center of pressure (COP) at the time of step landing, and to examine their relationship with the joint moments exerted by the supporting limb, with regard to factors of the successful backward step response. The study population comprised 8 community-dwelling elderly people that were observed to take successive multi steps after the landing of a backward stepping. Using a motion capture system and force plate, we measured the COM, COP and COM-COP deviation distance on landing during backward stepping. In addition, we measured the moment of the supporting limb joint during backward stepping. The multi-step data were compared with data from instances when only one step was taken (single-step). Variables that differed significantly between the single- and multi-step data were used as objective variables and the joint moments of the supporting limb were used as explanatory variables in single regression analyses. The COM-COP deviation in the anteroposterior was significantly larger in the single-step. A regression analysis with COM-COP deviation as the objective variable obtained a significant regression equation in the hip flexion moment (R2 = 0.74). The hip flexion moment of supporting limb was shown to be a significant explanatory variable in both the PS and SS phases for the relationship with COM-COP distance. This study found that to create an appropriate backward step response after an external disturbance (i.e. the ability to stop after 1 step), posterior braking of the COM by a hip flexion moment are important during the single-limbed standing phase.
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Sundbom, Fredrik; Malinovschi, Andrei; Lindberg, Eva; Alving, Kjell; Janson, Christer
2016-01-01
Asthma-related quality of life has previously been shown to be associated with asthma control. The aims of the present study were to further analyze this correlation, identify other variables with impact on asthma-related quality of life and investigate the covariance among these variables. Information was retrieved from a cohort of 369 patients, aged 12-35, with physician-diagnosed asthma requiring anti-inflammatory treatment for at least 3 months per year. Questionnaire data [including the mini-Asthma Quality of Life Questionnaire (mAQLQ), asthma control test (ACT) and Hospital Anxiety and Depression Scale (HADS)], quality of sleep, lung function data and blood samples were analyzed. Linear regression models with the mAQLQ score as the dependent scalar variable were calculated. ACT was the single variable that had the highest explanatory value for the mAQLQ score (51.5%). High explanatory power was also observed for anxiety and depression (17.0%) and insomnia (14.1%). The population was divided into groups depending on the presence of anxiety and depression, uncontrolled asthma and insomnia. The group that reported none of these conditions had the highest mean mAQLQ score (6.3 units), whereas the group reporting all of these conditions had the lowest mAQLQ score (3.8 units). The ACT score was the single most important variable in predicting asthma-related quality of life. Combining the ACT score with the data on insomnia, anxiety and depression showed considerable additive effects of the conditions. Hence, we recommend the routine use of the ACT and careful attention to symptoms of insomnia, anxiety or depression in the clinical evaluation of asthma-related quality of life.
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Logistic regression modeling to assess groundwater vulnerability to contamination in Hawaii, USA
NASA Astrophysics Data System (ADS)
Mair, Alan; El-Kadi, Aly I.
2013-10-01
Capture zone analysis combined with a subjective susceptibility index is currently used in Hawaii to assess vulnerability to contamination of drinking water sources derived from groundwater. In this study, we developed an alternative objective approach that combines well capture zones with multiple-variable logistic regression (LR) modeling and applied it to the highly-utilized Pearl Harbor and Honolulu aquifers on the island of Oahu, Hawaii. Input for the LR models utilized explanatory variables based on hydrogeology, land use, and well geometry/location. A suite of 11 target contaminants detected in the region, including elevated nitrate (> 1 mg/L), four chlorinated solvents, four agricultural fumigants, and two pesticides, was used to develop the models. We then tested the ability of the new approach to accurately separate groups of wells with low and high vulnerability, and the suitability of nitrate as an indicator of other types of contamination. Our results produced contaminant-specific LR models that accurately identified groups of wells with the lowest/highest reported detections and the lowest/highest nitrate concentrations. Current and former agricultural land uses were identified as significant explanatory variables for eight of the 11 target contaminants, while elevated nitrate was a significant variable for five contaminants. The utility of the combined approach is contingent on the availability of hydrologic and chemical monitoring data for calibrating groundwater and LR models. Application of the approach using a reference site with sufficient data could help identify key variables in areas with similar hydrogeology and land use but limited data. In addition, elevated nitrate may also be a suitable indicator of groundwater contamination in areas with limited data. The objective LR modeling approach developed in this study is flexible enough to address a wide range of contaminants and represents a suitable addition to the current subjective approach.
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Effects of Cognitive Load on Driving Performance: The Cognitive Control Hypothesis.
Engström, Johan; Markkula, Gustav; Victor, Trent; Merat, Natasha
2017-08-01
The objective of this paper was to outline an explanatory framework for understanding effects of cognitive load on driving performance and to review the existing experimental literature in the light of this framework. Although there is general consensus that taking the eyes off the forward roadway significantly impairs most aspects of driving, the effects of primarily cognitively loading tasks on driving performance are not well understood. Based on existing models of driver attention, an explanatory framework was outlined. This framework can be summarized in terms of the cognitive control hypothesis: Cognitive load selectively impairs driving subtasks that rely on cognitive control but leaves automatic performance unaffected. An extensive literature review was conducted wherein existing results were reinterpreted based on the proposed framework. It was demonstrated that the general pattern of experimental results reported in the literature aligns well with the cognitive control hypothesis and that several apparent discrepancies between studies can be reconciled based on the proposed framework. More specifically, performance on nonpracticed or inherently variable tasks, relying on cognitive control, is consistently impaired by cognitive load, whereas the performance on automatized (well-practiced and consistently mapped) tasks is unaffected and sometimes even improved. Effects of cognitive load on driving are strongly selective and task dependent. The present results have important implications for the generalization of results obtained from experimental studies to real-world driving. The proposed framework can also serve to guide future research on the potential causal role of cognitive load in real-world crashes.
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NASA Astrophysics Data System (ADS)
Abdussalam, Auwal; Thornes, John; Leckebusch, Gregor
2015-04-01
Nigeria has a number of climate-sensitive infectious diseases; one of the most important of these diseases that remains a threat to public health is cholera. This study investigates the influences of both meteorological and socioeconomic factors on the spatiotemporal variability of cholera in Nigeria. A stepwise multiple regression models are used to estimate the influence of the year-to-year variations of cholera cases and deaths for individual states in the country and as well for three groups of states that are classified based on annual rainfall amount. Specifically, seasonal mean maximum and minimum temperatures and annual rainfall totals were analysed with annual aggregate count of cholera cases and deaths, taking into account of the socioeconomic factors that are potentially enhancing vulnerability such as: absolute poverty, adult literacy, access to pipe borne water and population density. Result reveals that the most important explanatory meteorological and socioeconomic variables in explaining the spatiotemporal variability of the disease are rainfall totals, seasonal mean maximum temperature, absolute poverty, and accessibility to pipe borne water. The influences of socioeconomic factors appeared to be more pronounced in the northern part of the country, and vice-versa in the case of meteorological factors. Also, cross validated models output suggests a strong possibility of disease prediction, which will help authorities to put effective control measures in place which depend on prevention, and or efficient response.
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Drivers for spatial variability in agricultural soil organic carbon stocks in Germany
NASA Astrophysics Data System (ADS)
Vos, Cora; Don, Axel; Hobley, Eleanor; Prietz, Roland; Heidkamp, Arne; Freibauer, Annette
2017-04-01
Soil organic carbon is one of the largest components of the global carbon cycle. It has recently gained importance in global efforts to mitigate climate change through carbon sequestration. In order to find locations suitable for carbon sequestration, and estimate the sequestration potential, however, it is necessary to understand the factors influencing the high spatial variability of soil organic carbon stocks. Due to numerous interacting factors that influence its dynamics, soil organic carbon stocks are difficult to predict. In the course of the German Agricultural Soil Inventory over 2500 agricultural sites were sampled and their soil organic carbon stocks determined. Data relating to more than 200 potential drivers of SOC stocks were compiled from laboratory measurements, farmer questionnaires and climate stations. The aims of this study were to 1) give an overview of soil organic carbon stocks in Germany's agricultural soils, 2) to quantify and explain the influence of explanatory variables on soil organic carbon stocks. Two different machine learning algorithms were used to identify the most important variables and multiple regression models were used to explore the influence of those variables. Models for predicting carbon stocks in different depth increments between 0-100 cm were developed, explaining up to 62% (validation, 98% calibration) of total variance. Land-use, land-use history, clay content and electrical conductivity were main predictors in the topsoil, while bedrock material, relief and electrical conductivity governed the variability of subsoil carbon stocks. We found 32% of all soils to be deeply anthropogenically transformed. The influence of climate related variables was surprisingly small (≤5% of explained variance), while site variables explained a large share of soil carbon variability (46-100% of explained variance), in particular in the subsoil. Thus, the understanding of SOC dynamics at regional scale requires a thorough description of the variability in soil physical parameters. Agronomic management impact on SOC stocks is important near the soil surface, but is mainly attributable to land-use and not to other management factors on this large regional scale. The importance of historical land-use practices as well as anthropogenic soil transformations to SOC stocks highlights the need for prudent soil management and conservation policies.
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Leung, Gabriel M.; Yu, Philip L. H.; Wong, Irene O. L.; Johnston, Janice M.; Tin, Keith Y. K.
2003-01-01
Objective: Given the slow adoption of medical informatics in Hong Kong and Asia, we sought to understand the contributory barriers and potential incentives associated with information technology implementation. Design and Measurements: A representative sample of 949 doctors (response rate = 77.0%) was asked through a postal survey to rank a list of nine barriers associated with clinical computerization according to self-perceived importance. They ranked seven incentives or catalysts that may influence computerization. We generated mean rank scores and used multidimensional preference analysis to explore key explanatory dimensions of these variables. A hierarchical cluster analysis was performed to identify homogenous subgroups of respondents. We further determined the relationships between the sets of barriers and incentives/catalysts collectively using canonical correlation. Results: Time costs, lack of technical support and large capital investments were the biggest barriers to computerization, whereas improved office efficiency and better-quality care were ranked highest as potential incentives to computerize. Cost vs. noncost, physician-related vs. patient-related, and monetary vs. nonmonetary factors were the key dimensions explaining the barrier variables. Similarly, within-practice vs external and “push” vs “pull” factors accounted for the incentive variables. Four clusters were identified for barriers and three for incentives/catalysts. Canonical correlation revealed that respondents who were concerned with the costs of computerization also perceived financial incentives and government regulation to be important incentives/catalysts toward computerization. Those who found the potential interference with communication important also believed that the promise of improved care from computerization to be a significant incentive. Conclusion: This study provided evidence regarding common barriers associated with clinical computerization. Our findings also identified possible incentive strategies that may be employed to accelerate uptake of computer systems. PMID:12595409
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A model for field toxicity tests
Kaiser, Mark S.; Finger, Susan E.
1996-01-01
Toxicity tests conducted under field conditions present an interesting challenge for statistical modelling. In contrast to laboratory tests, the concentrations of potential toxicants are not held constant over the test. In addition, the number and identity of toxicants that belong in a model as explanatory factors are not known and must be determined through a model selection process. We present one model to deal with these needs. This model takes the record of mortalities to form a multinomial distribution in which parameters are modelled as products of conditional daily survival probabilities. These conditional probabilities are in turn modelled as logistic functions of the explanatory factors. The model incorporates lagged values of the explanatory factors to deal with changes in the pattern of mortalities over time. The issue of model selection and assessment is approached through the use of generalized information criteria and power divergence goodness-of-fit tests. These model selection criteria are applied in a cross-validation scheme designed to assess the ability of a model to both fit data used in estimation and predict data deleted from the estimation data set. The example presented demonstrates the need for inclusion of lagged values of the explanatory factors and suggests that penalized likelihood criteria may not provide adequate protection against overparameterized models in model selection.
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Decision tree analysis of factors influencing rainfall-related building damage
NASA Astrophysics Data System (ADS)
Spekkers, M. H.; Kok, M.; Clemens, F. H. L. R.; ten Veldhuis, J. A. E.
2014-04-01
Flood damage prediction models are essential building blocks in flood risk assessments. Little research has been dedicated so far to damage of small-scale urban floods caused by heavy rainfall, while there is a need for reliable damage models for this flood type among insurers and water authorities. The aim of this paper is to investigate a wide range of damage-influencing factors and their relationships with rainfall-related damage, using decision tree analysis. For this, district-aggregated claim data from private property insurance companies in the Netherlands were analysed, for the period of 1998-2011. The databases include claims of water-related damage, for example, damages related to rainwater intrusion through roofs and pluvial flood water entering buildings at ground floor. Response variables being modelled are average claim size and claim frequency, per district per day. The set of predictors include rainfall-related variables derived from weather radar images, topographic variables from a digital terrain model, building-related variables and socioeconomic indicators of households. Analyses were made separately for property and content damage claim data. Results of decision tree analysis show that claim frequency is most strongly associated with maximum hourly rainfall intensity, followed by real estate value, ground floor area, household income, season (property data only), buildings age (property data only), ownership structure (content data only) and fraction of low-rise buildings (content data only). It was not possible to develop statistically acceptable trees for average claim size, which suggest that variability in average claim size is related to explanatory variables that cannot be defined at the district scale. Cross-validation results show that decision trees were able to predict 22-26% of variance in claim frequency, which is considerably better compared to results from global multiple regression models (11-18% of variance explained). Still, a large part of the variance in claim frequency is left unexplained, which is likely to be caused by variations in data at subdistrict scale and missing explanatory variables.
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Bailie, Ross S; Stevens, Matthew; McDonald, Elizabeth L
2014-05-19
The mental health of carers is an important proximate factor in the causal web linking housing conditions to child health, as well as being important in its own right. Improved understanding of the nature of the relationships between housing conditions, carer mental health and child health outcomes is therefore important for informing the development of housing programs. This paper examines the relationship between the mental health of the carers of young children, housing conditions, and other key factors in the socio-physical environment. This analysis is part of a broader prospective cohort study of children living in Aboriginal communities in the Northern Territory (NT) of Australia at the time of major new community housing programs. Carer's mental health was assessed using two validated scales: the Affect Balance scale and the Brief Screen for Depression. The quality of housing infrastructure was assessed through detailed surveys. Secondary explanatory variables included a range of socio-environmental factors, including validated measures of stressful life events. Hierarchical regression modelling was used to assess associations between outcome and explanatory variables at baseline, and associations between change in housing conditions and change in outcomes between baseline and follow-up. There was no clear or consistent evidence of a causal relationship between the functional state of household infrastructure and the mental health of carers of young children. The strongest and most consistent associations with carer mental health were the measures of negative life events, with a dose-response relationship, and adjusted odds ratio of over 6 for carers in the highest stress exposure category at baseline, and consistent associations in the follow up analysis. The findings highlight the need for housing programs to be supported by social, behavioral and community-wide environmental programs if potential health gains are to be more fully realized, and for rigorous evaluation of such programs for the purpose of informing future housing initiatives.
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NASA Astrophysics Data System (ADS)
Nanus, Leora; Clow, David; Saros, Jasmine; McMurray, Jill; Blett, Tamara; Sickman, James
2017-04-01
High-elevation aquatic ecosystems in Wilderness areas of the western United States are impacted by current and historic atmospheric nitrogen (N) deposition associated with local and regional air pollution. Documented effects include elevated surface water nitrate concentrations, increased algal productivity, and changes in diatom species assemblages. A predictive framework was developed for sensitive high-elevation basins across the western United States at multiple spatial scales including the Rocky Mountain Region (Rockies), the Greater Yellowstone Area (GYA), and Yosemite (YOSE) and Sequoia & Kings Canyon (SEKI) National Parks. Spatial trends in critical loads of N deposition for nutrient enrichment of aquatic ecosystems were quantified and mapped using a geostatistical approach, with modeled N deposition, topography, vegetation, geology, and climate as potential explanatory variables. Multiple predictive models were created using various combinations of explanatory variables; this approach allowed for better quantification of uncertainty and identification of areas most sensitive to high atmospheric N deposition (> 3 kg N ha-1 yr-1). For multiple spatial scales, the lowest critical loads estimates (<1.5 + 1 kg N ha-1 yr-1) occurred in high-elevation basins with steep slopes, sparse vegetation, and exposed bedrock and talus. Based on a nitrate threshold of 1 μmol L-1, estimated critical load exceedances (>1.5 + 1 kg N ha-1 yr-1) correspond with areas of high N deposition and vary spatially ranging from less than 20% to over 40% of the study area for the Rockies, GYA, YOSE, and SEKI. These predictive models and maps identify sensitive aquatic ecosystems that may be impacted by excess atmospheric N deposition and can be used to help protect against future anthropogenic disturbance. The approach presented here may be transferable to other remote and protected high-elevation ecosystems at multiple spatial scales that are sensitive to adverse effects of pollutant loading in the US and around the world.
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NASA Astrophysics Data System (ADS)
Hasan, Mejs; Moody, Aaron
2017-04-01
The fast-paced conflicts in the Middle East have the potential to disrupt management and supply of water resources in the region. In this research, we use the normalized difference water index (NDWI) in order to monitor changes in the extent of various water bodies over the time span of the Landsat 4, 5, 7, and 8 satellites (1984-present). We focused on Mosul and Haditha dam lakes, located on the Tigris and Euphrates Rivers, respectively, each of which has experienced changes in sovereignty over the last few years of conflict. We established two areas, one land and one water, on each image, plotted the distributions of all NDWI values for each area, and used the number of standard deviations between the two distributions in order to set a dynamic NDWI threshold for each image. Using this threshold, we determined water pixels and lake surface area, and computed daily percent change in lake extent between images. Furthermore, we took account of explanatory water resource variables, such as upstream dam management (via surface extent of upstream Turkish dams), precipitation (via globally-compiled databases), evaporation (based on surface area decreases during non-rainy months), and irrigation withdrawals (based on MODIS Enhanced Vegetation Indices). We used these explanatory variables in order to build a general model of expected dam lake surface extent, and we looked to see if anomalies from expected surface area corresponded with periods of conflict. We found that the recent years of conflict do not appear to have had as much impact on the Mosul and Haditha dam lakes as did the conflicts related to the earlier Gulf Wars. The dam lakes have recorded an overall decrease in surface area simultaneous to increases of upstream dams. A strong seasonal signal driven by springtime Turkish snowmelt and summer evaporation is also evident.
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Felicitas, Jamie Q.; Tanenbaum, Hilary C.; Li, Yawen; Chou, Chih-Ping; Palmer, Paula H.; Spruijt-Metz, Donna; Reynolds, Kim D.; Johnson, C. Anderson; Xie, Bin
2015-01-01
This paper explores the longitudinal effects of socioeconomic factors (i.e., parent education and family income level), foreign media, and attitude toward appearance on general and central adiposity among Chinese adolescents. A longitudinal analysis was performed using data from the China Seven Cities Study, a health promotion and smoking prevention study conducted in seven cities across Mainland China between 2002 and 2005. Participants included 5,020 middle and high school students and their parents. Explanatory variables included foreign media exposure, attitude toward appearance, parent education, and family income. Three-level, random-effect models were used to predict general adiposity (i.e., body mass index) and central adiposity (i.e., waist circumference). The Generalized Estimating Equation approach was utilized to determine the effect of explanatory variables on overweight status. Among girls, foreign media exposure was significantly negatively associated with general adiposity over time (β = − 0.06, p = 0.01 for middle school girls; β = − 0.06, p = 0.03 for high school girls). Attitude toward appearance was associated with lesser odds of being overweight, particularly among high school girls (OR = 0.86, p < 0.01). Among boys, parental education was significantly positively associated with general adiposity (β = 0.62, p < 0.01 for middle school boys; β = 0.37, p = 0.02 for high school boys) and associated with greater odds of being overweight (OR = 1.55, p < 0.01 for middle school boys; OR = 1.26, p = 0.04 for high school boys). Across all gender and grade levels, family income was significantly negatively associated with central adiposity over time. Interventions addressing Chinese adolescent overweight/obesity should consider these factors as potential focus areas. PMID:26279973
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NASA Astrophysics Data System (ADS)
Sasaki, H.; Matsuno, K.; Fujiwara, A.; Onuka, M.; Yamaguchi, A.; Ueno, H.; Watanuki, Y.; Kikuchi, T.
2015-11-01
The advection of warm Pacific water and the reduction of sea-ice extent in the western Arctic Ocean may influence the abundance and distribution of copepods, i.e., a key component in food webs. To understand the factors affecting abundance of copepods in the northern Bering Sea and Chukchi Sea, we constructed habitat models explaining the spatial patterns of the large and small Arctic copepods and the Pacific copepods, separately, using generalized additive models. Copepods were sampled by NORPAC net. Vertical profiles of density, temperature and salinity in the seawater were measured using CTD, and concentration of chlorophyll a in seawater was measured with a fluorometer. The timing of sea-ice retreat was determined using the satellite image. To quantify the structure of water masses, the magnitude of pycnocline and averaged density, temperature and salinity in upper and bottom layers were scored along three axes using principal component analysis (PCA). The structures of water masses indexed by the scores of PCAs were selected as explanatory variables in the best models. Large Arctic copepods were abundant in the water mass with high salinity water in bottom layer or with cold/low salinity water in upper layer and cold/high salinity water in bottom layer, and small Arctic copepods were abundant in the water mass with warm/saline water in upper layer and cold/high salinity water in bottom layers, while Pacific copepods were abundant in the water mass with warm/saline in upper layer and cold/high salinity water in bottom layer. All copepod groups were abundant in areas with deeper depth. Although chlorophyll a in upper and bottom layers were selected as explanatory variables in the best models, apparent trends were not observed. All copepod groups were abundant where the sea-ice retreated at earlier timing. Our study might indicate potential positive effects of the reduction of sea-ice extent on the distribution of all groups of copepods in the Arctic Ocean.
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Spatial analysis of agri-environmental policy uptake and expenditure in Scotland.
Yang, Anastasia L; Rounsevell, Mark D A; Wilson, Ronald M; Haggett, Claire
2014-01-15
Agri-environment is one of the most widely supported rural development policy measures in Scotland in terms of number of participants and expenditure. It comprises 69 management options and sub-options that are delivered primarily through the competitive 'Rural Priorities scheme'. Understanding the spatial determinants of uptake and expenditure would assist policy-makers in guiding future policy targeting efforts for the rural environment. This study is unique in examining the spatial dependency and determinants of Scotland's agri-environmental measures and categorised options uptake and payments at the parish level. Spatial econometrics is applied to test the influence of 40 explanatory variables on farming characteristics, land capability, designated sites, accessibility and population. Results identified spatial dependency for each of the dependent variables, which supported the use of spatially-explicit models. The goodness of fit of the spatial models was better than for the aspatial regression models. There was also notable improvement in the models for participation compared with the models for expenditure. Furthermore a range of expected explanatory variables were found to be significant and varied according to the dependent variable used. The majority of models for both payment and uptake showed a significant positive relationship with SSSI (Sites of Special Scientific Interest), which are designated sites prioritised in Scottish policy. These results indicate that environmental targeting efforts by the government for AEP uptake in designated sites can be effective. However habitats outside of SSSI, termed here the 'wider countryside' may not be sufficiently competitive to receive funding in the current policy system. Copyright © 2013 Elsevier Ltd. All rights reserved.
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Untangling Trends and Drivers of Changing River Discharge Along Florida's Gulf Coast
NASA Astrophysics Data System (ADS)
Glodzik, K.; Kaplan, D. A.; Klarenberg, G.
2017-12-01
Along the relatively undeveloped Big Bend coastline of Florida, discharge in many rivers and springs is decreasing. The causes are unclear, though they likely include a combination of groundwater extraction for water supply, climate variability, and altered land use. Saltwater intrusion from altered freshwater influence and sea level rise is causing transformative ecosystem impacts along this flat coastline, including coastal forest die-off and oyster reef collapse. A key uncertainty for understanding river discharge change is predicting discharge from rainfall, since Florida's karstic bedrock stores large amounts of groundwater, which has a long residence time. This study uses Dynamic Factor Analysis (DFA), a multivariate data reduction technique for time series, to find common trends in flow and reveal hydrologic variables affecting flow in eight Big Bend rivers since 1965. The DFA uses annual river flows as response time series, and climate data (annual rainfall and evapotranspiration by watershed) and climatic indices (El Niño Southern Oscillation [ENSO] Index and North Atlantic Oscillation [NAO] Index) as candidate explanatory variables. Significant explanatory variables (one evapotranspiration and three rainfall time series) explained roughly 50% of discharge variation across rivers. Significant trends (representing unexplained variation) were shared among rivers, with geographical grouping of five northern rivers and three southern rivers, along with a strong downward trend affecting six out of eight systems. ENSO and NAO had no significant impact. Advancing knowledge of these dynamics is necessary for forecasting how altered rainfall and temperatures from climate change may impact flows. Improved forecasting is especially important given Florida's reliance on groundwater extraction to support its growing population.
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Groundwater salinity in a floodplain forest impacted by saltwater intrusion
NASA Astrophysics Data System (ADS)
Kaplan, David A.; Muñoz-Carpena, Rafael
2014-11-01
Coastal wetlands occupy a delicate position at the intersection of fresh and saline waters. Changing climate and watershed hydrology can lead to saltwater intrusion into historically freshwater systems, causing plant mortality and loss of freshwater habitat. Understanding the hydrological functioning of tidally influenced floodplain forests is essential for advancing ecosystem protection and restoration goals, however finding direct relationships between hydrological inputs and floodplain hydrology is complicated by interactions between surface water, groundwater, and atmospheric fluxes in variably saturated soils with heterogeneous vegetation and topography. Thus, an alternative method for identifying common trends and causal factors is required. Dynamic factor analysis (DFA), a time series dimension reduction technique, models temporal variation in observed data as linear combinations of common trends, which represent unexplained common variability, and explanatory variables. DFA was applied to model shallow groundwater salinity in the forested floodplain wetlands of the Loxahatchee River (Florida, USA), where altered watershed hydrology has led to changing hydroperiod and salinity regimes and undesired vegetative changes. Long-term, high-resolution groundwater salinity datasets revealed dynamics over seasonal and yearly time periods as well as over tidal cycles and storm events. DFA identified shared trends among salinity time series and a full dynamic factor model simulated observed series well (overall coefficient of efficiency, Ceff = 0.85; 0.52 ≤ Ceff ≤ 0.99). A reduced multilinear model based solely on explanatory variables identified in the DFA had fair to good results (Ceff = 0.58; 0.38 ≤ Ceff ≤ 0.75) and may be used to assess the effects of restoration and management scenarios on shallow groundwater salinity in the Loxahatchee River floodplain.
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The linkage between geopotential height and monthly precipitation in Iran
NASA Astrophysics Data System (ADS)
Shirvani, Amin; Fadaei, Amir Sabetan; Landman, Willem A.
2018-04-01
This paper investigates the linkage between large-scale atmospheric circulation and monthly precipitation during November to April over Iran. Canonical correlation analysis (CCA) is used to set up the statistical linkage between the 850 hPa geopotential height large-scale circulation and monthly precipitation over Iran for the period 1968-2010. The monthly precipitation dataset for 50 synoptic stations distributed in different climate regions of Iran is considered as the response variable in the CCA. The monthly geopotential height reanalysis dataset over an area between 10° N and 60° N and from 20° E to 80° E is utilized as the explanatory variable in the CCA. Principal component analysis (PCA) as a pre-filter is used for data reduction for both explanatory and response variables before applying CCA. The optimal number of principal components and canonical variables to be retained in the CCA equations is determined using the highest average cross-validated Kendall's tau value. The 850 hPa geopotential height pattern over the Red Sea, Saudi Arabia, and Persian Gulf is found to be the major pattern related to Iranian monthly precipitation. The Pearson correlation between the area averaged of the observed and predicted precipitation over the study area for Jan, Feb, March, April, November, and December months are statistically significant at the 5% significance level and are 0.78, 0.80, 0.82, 0.74, 0.79, and 0.61, respectively. The relative operating characteristic (ROC) indicates that the highest scores for the above- and below-normal precipitation categories are, respectively, for February and April and the lowest scores found for December.
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On the effect of networks of cycle-tracks on the risk of cycling. The case of Seville.
Marqués, R; Hernández-Herrador, V
2017-05-01
We analyze the evolution of the risk of cycling in Seville before and after the implementation of a network of segregated cycle tracks in the city. Specifically, we study the evolution of the risk for cyclists of being involved in a collision with a motor vehicle, using data reported by the traffic police along the period 2000-2013, i.e. seven years before and after the network was built. A sudden drop of such risk was observed after the implementation of the network of bikeways. We study, through a multilinear regression analysis, the evolution of the risk by means of explanatory variables representing changes in the built environment, specifically the length of the bikeways and a stepwise jump variable taking the values 0/1 before/after the network was implemented. We found that this last variable has a high value as explanatory variable, even higher than the length of the network, thus suggesting that networking the bikeways has a substantial effect on cycling safety by itself and beyond the mere increase in the length of the bikeways. We also analyze safety in numbers through a non-linear regression analysis. Our results fully agree qualitatively and quantitatively with the results previously reported by Jacobsen (2003), thus providing an independent confirmation of Jacobsen's results. Finally, the mutual causal relationships between the increase in safety, the increase in the number of cyclists and the presence of the network of bikeways are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Vargas-Palacios, Armando; Gutiérrez, Juan Pablo; Carreón-Rodríguez, Víctor
2006-01-01
To estimate the effectiveness of using standardized health vignettes to adjust self-reported health taking into account household and community variables to correct for systematic bias. The national health survey "Evaluación del Desempeño 2002" (Mexican component of the World Health Survey) was used. This survey analyzed subject's health perception based on their responses to hypothetical questions referring to third parties in the vignettes within eight domains. Variations in responses were attributed to socio-demographic, socioeconomic, community, differences of the subjects. To assess those variations, an index for each domain was constructed and used as a variable in a series of linear regression models to estimate the relation between health perceptions, self-reported health, socioeconomic and socio-demographic characteristics. The health perception index derived from the vignettes showed a positive, logarithmic correlation with household expenditure for each health domain, after controlling for socio-demographic, health and community characteristics. No relationship was found between the health status described in the vignettes and self-reported health status. In no case was the explanatory power above 10%. The low explanatory power of the models, and the lack of correlation between self reported health status and the health perception index, suggest that the variability in the vignettes responses cannot be explained by differences in self-reported health status or socioeconomic and socio-demographic characteristics. These results from Mexico suggest that vignette-based methods to correct for systematic variability in perception of own health status are of limited efficacy and reinforce the importance of collecting objective measures of health status in health surveys.
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NASA Astrophysics Data System (ADS)
Hadley, Brian Christopher
This dissertation assessed remotely sensed data and geospatial modeling technique(s) to map the spatial distribution of total above-ground biomass present on the surface of the Savannah River National Laboratory's (SRNL) Mixed Waste Management Facility (MWMF) hazardous waste landfill. Ordinary least squares (OLS) regression, regression kriging, and tree-structured regression were employed to model the empirical relationship between in-situ measured Bahia (Paspalum notatum Flugge) and Centipede [Eremochloa ophiuroides (Munro) Hack.] grass biomass against an assortment of explanatory variables extracted from fine spatial resolution passive optical and LIDAR remotely sensed data. Explanatory variables included: (1) discrete channels of visible, near-infrared (NIR), and short-wave infrared (SWIR) reflectance, (2) spectral vegetation indices (SVI), (3) spectral mixture analysis (SMA) modeled fractions, (4) narrow-band derivative-based vegetation indices, and (5) LIDAR derived topographic variables (i.e. elevation, slope, and aspect). Results showed that a linear combination of the first- (1DZ_DGVI), second- (2DZ_DGVI), and third-derivative of green vegetation indices (3DZ_DGVI) calculated from hyperspectral data recorded over the 400--960 nm wavelengths of the electromagnetic spectrum explained the largest percentage of statistical variation (R2 = 0.5184) in the total above-ground biomass measurements. In general, the topographic variables did not correlate well with the MWMF biomass data, accounting for less than five percent of the statistical variation. It was concluded that tree-structured regression represented the optimum geospatial modeling technique due to a combination of model performance and efficiency/flexibility factors.
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Longitudinal Course of Risk for Parental Post-Adoption Depression
Foli, Karen J.; South, Susan C.; Lim, Eunjung; Hebdon, Megan
2016-01-01
Objective To determine whether the Postpartum Depression Predictors Inventory-Revised (PDPI-R) could be used to reveal distinct classes of adoptive parents across time. Design Longitudinal data were collected via online surveys at 4-6 weeks pre-placement, 4-6 weeks post-placement, and 5-6 months post-placement. Setting Participants were primarily clients of the largest adoption agency in the United States. Participants Participants included 127 adoptive parents (68 mothers and 59 fathers). Methods We applied a latent class growth analysis to the PDPI-R and conducted mixed effects modeling of class, time, and class×time interaction for the following categories of explanatory variables: parental expectations; interpersonal variables; psychological symptoms; and life orientation. Results Four latent trajectory classes were found. Class 1 (55% of sample) showed a stably low level of PDPI-R scores over time. Class 2 (32%) reported mean scores below the cut-off points at all three time points. Class 3 (8%) started at an intermediate level and increased after post-placement, but decreased at 5-6 months post-placement. Class 4 (5%) had high mean scores at all three time points. Significant main effects were found for almost all explanatory variables for class and for several variables for time. Significant interactions between class and time were found for expectations about the child and amount of love and ambivalence in parent's intimate relationship. Conclusion Findings may assist nurses to be alert to trajectories of risk for post-adoption depression. Additional factors, not included in the PDPI-R, to determine risk for post-adoption depression may be needed for adoptive parents. PMID:26874267
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Sharma, Nivita D
2017-09-01
Several explanations for the inconsistent results on the effects of breastfeeding on childhood asthma have been suggested. The purpose of this study was to investigate one unexplored explanation, which is the presence of a potential endogenous relationship between breastfeeding and childhood asthma. Endogeneity exists when an explanatory variable is correlated with the error term for reasons such as selection bias, reverse causality, and unmeasured confounders. Unadjusted endogeneity will bias the effect of breastfeeding on childhood asthma. To investigate potential endogeneity, a cross-sectional study of breastfeeding practices and incidence of childhood asthma in 87 pediatric patients in Georgia, the USA, was conducted using generalized linear modeling and a two-stage instrumental variable analysis. First, the relationship between breastfeeding and childhood asthma was analyzed without considering endogeneity. Second, tests for presence of endogeneity were performed and having detected endogeneity between breastfeeding and childhood asthma, a two-stage instrumental variable analysis was performed. The first stage of this analysis estimated the duration of breastfeeding and the second-stage estimated the risk of childhood asthma. When endogeneity was not taken into account, duration of breastfeeding was found to significantly increase the risk of childhood asthma (relative risk ratio [RR]=2.020, 95% confidence interval [CI]: [1.143-3.570]). After adjusting for endogeneity, duration of breastfeeding significantly reduced the risk of childhood asthma (RR=0.003, 95% CI: [0.000-0.240]). The findings suggest that researchers should consider evaluating how the presence of endogeneity could affect the relationship between duration of breastfeeding and the risk of childhood asthma. © 2017 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.
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Feldthusen, Caroline; Grimby-Ekman, Anna; Forsblad-d'Elia, Helena; Jacobsson, Lennart; Mannerkorpi, Kaisa
2016-04-28
To investigate the impact of disease-related aspects on long-term variations in fatigue in persons with rheumatoid arthritis. Observational longitudinal study. Sixty-five persons with rheumatoid arthritis, age range 20-65 years, were invited to a clinical examination at 4 time-points during the 4 seasons. Outcome measures were: general fatigue rated on visual analogue scale (0-100) and aspects of fatigue assessed by the Bristol Rheumatoid Arthritis Fatigue Multidimensional Questionnaire. Disease-related variables were: disease activity (erythrocyte sedimentation rate), pain threshold (pressure algometer), physical capacity (six-minute walk test), pain (visual analogue scale (0-100)), depressive mood (Hospital Anxiety and Depression scale, depression subscale), personal factors (age, sex, body mass index) and season. Multivariable regression analysis, linear mixed effects models were applied. The strongest explanatory factors for all fatigue outcomes, when recorded at the same time-point as fatigue, were pain threshold and depressive mood. Self-reported pain was an explanatory factor for physical aspects of fatigue and body mass index contributed to explaining the consequences of fatigue on everyday living. For predicting later fatigue pain threshold and depressive mood were the strongest predictors. Pain threshold and depressive mood were the most important factors for fatigue in persons with rheumatoid arthritis.
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Social support and clinical and functional outcome in people with schizophrenia.
Vázquez Morejón, Antonio J; León Rubio, Jose Mª; Vázquez-Morejón, Raquel
2018-05-01
The impact of Social Support (SS) on the clinical and functional evolution of patients diagnosed with schizophrenia was studied from a multidimensional concept of SS in the framework of the vulnerability-stress model. In total, 152 patients diagnosed with schizophrenia according to the International Classification of Diseases, Tenth Edition (ICD-10) treated in a Community Mental Health Unit were assessed using the Mannheim Interview on Social Support (MISS) and the Brief Psychiatric Rating Scale (BPRS). Then they were followed up for 3 years with a final assessment for the period using the Social Functioning Scale. The impact of SS was explored in clinical and functional measurements with a multiple regression analysis in a 3-year longitudinal prospective design. The quality of Global Social Support (GSS) and satisfaction with GSS appeared to be protective factors from frequency and duration of hospital admissions, with explanatory intensity varying from 9% in survival time to relapse to 13% in number of relapses. Concerning functional measurements, GSS quantity, quality and satisfaction showed an explanatory power for several different dimensions of social functioning, varying from 12% in isolation to 20% in communication. The results confirm SS as a protective factor in the evolution of schizophrenia patients and enable the SS variables with the most explanatory power in their clinical and functional evolution to be identified.
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Improving Space Project Cost Estimating with Engineering Management Variables
NASA Technical Reports Server (NTRS)
Hamaker, Joseph W.; Roth, Axel (Technical Monitor)
2001-01-01
Current space project cost models attempt to predict space flight project cost via regression equations, which relate the cost of projects to technical performance metrics (e.g. weight, thrust, power, pointing accuracy, etc.). This paper examines the introduction of engineering management parameters to the set of explanatory variables. A number of specific engineering management variables are considered and exploratory regression analysis is performed to determine if there is statistical evidence for cost effects apart from technical aspects of the projects. It is concluded that there are other non-technical effects at work and that further research is warranted to determine if it can be shown that these cost effects are definitely related to engineering management.
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An Update on Statistical Boosting in Biomedicine.
Mayr, Andreas; Hofner, Benjamin; Waldmann, Elisabeth; Hepp, Tobias; Meyer, Sebastian; Gefeller, Olaf
2017-01-01
Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates. They are extremely flexible, as the underlying base-learners (regression functions defining the type of effect for the explanatory variables) can be combined with any kind of loss function (target function to be optimized, defining the type of regression setting). In this review article, we highlight the most recent methodological developments on statistical boosting regarding variable selection, functional regression, and advanced time-to-event modelling. Additionally, we provide a short overview on relevant applications of statistical boosting in biomedicine.
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On Explaining Language Shift: Sociology or Social Psychology of Language?
ERIC Educational Resources Information Center
Maitz, Peter
2011-01-01
This study investigates the potentials and limits of sociolinguistic research on language shift. Starting from a position that the ultimate goal of the research must be to create a general theory of language shift of predictive power, the author examines the explanatory potential of current mainstream research methodology now regarded as canonical…
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NASA Astrophysics Data System (ADS)
Aidi, Muhammad Nur; Sari, Resty Indah
2012-05-01
A decision of credit that given by bank or another creditur must have a risk and it called credit risk. Credit risk is an investor's risk of loss arising from a borrower who does not make payments as promised. The substantial of credit risk can lead to losses for the banks and the debtor. To minimize this problem need a further study to identify a potential new customer before the decision given. Identification of debtor can using various approaches analysis, one of them is by using discriminant analysis. Discriminant analysis in this study are used to classify whether belonging to the debtor's good credit or bad credit. The result of this study are two discriminant functions that can identify new debtor. Before step built the discriminant function, selection of explanatory variables should be done. Purpose of selection independent variable is to choose the variable that can discriminate the group maximally. Selection variables in this study using different test, for categoric variable selection of variable using proportion chi-square test, and stepwise discriminant for numeric variable. The result of this study are two discriminant functions that can identify new debtor. The selected variables that can discriminating two groups of debtor maximally are status of existing checking account, credit history, credit amount, installment rate in percentage of disposable income, sex, age in year, other installment plans, and number of people being liable to provide maintenance. This classification produce a classification accuracy rate is good enough, that is equal to 74,70%. Debtor classification using discriminant analysis has risk level that is small enough, and it ranged beetwen 14,992% and 17,608%. Based on that credit risk rate, using discriminant analysis on the classification of credit status can be used effectively.
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Some methodological issues in the longitudinal analysis of demographic data.
Krishinan, P
1982-12-01
Most demographic data are macro (or aggregate) in nature. Some relevant methodological issues are presented here in a time series study using aggregate data. The micro-macro distinction is relative. Time enters into the micro and macro variables in different ways. A simple micro model of rural-urban migration is given. Method 1 is to assume homogeneity in behavior. Method 2 is a Bayesian estimation. A discusssion of the results follows. Time series models of aggregate data are given. The nature of the model--predictive or explanatory--must be decided on. Explanatory models in longitudinal studies have been developed. Ways to go to the micro level from the macro are discussed. The aggregation-disaggregation problem in demography is not similar to that in econometrics. To understand small populations, separate micro level data have to be collected and analyzed and appropriate models developed. Both types of models have their uses.
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Education And Gender Bias in the Sex Ratio At Birth: Evidence From India
ECHÁVARRI, REBECA A.; EZCURRA, ROBERTO
2010-01-01
This article investigates the possible existence of a nonlinear link between female disadvantage in natality and education. To this end, we devise a theoretical model based on the key role of social interaction in explaining people’s acquisition of preferences, which justifies the existence of a nonmonotonic relationship between female disadvantage in natality and education. The empirical validity of the proposed model is examined for the case of India, using district-level data. In this context, our econometric analysis pays particular attention to the role of spatial dependence to avoid any potential problems of misspecification. The results confirm that the relationship between the sex ratio at birth and education in India follows an inverted U-shape. This finding is robust to the inclusion of additional explanatory variables in the analysis, and to the choice of the spatial weight matrix used to quantify the spatial interdependence between the sample districts. PMID:20355693
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NASA Astrophysics Data System (ADS)
Yao, Bing; Yang, Hui
2016-12-01
This paper presents a novel physics-driven spatiotemporal regularization (STRE) method for high-dimensional predictive modeling in complex healthcare systems. This model not only captures the physics-based interrelationship between time-varying explanatory and response variables that are distributed in the space, but also addresses the spatial and temporal regularizations to improve the prediction performance. The STRE model is implemented to predict the time-varying distribution of electric potentials on the heart surface based on the electrocardiogram (ECG) data from the distributed sensor network placed on the body surface. The model performance is evaluated and validated in both a simulated two-sphere geometry and a realistic torso-heart geometry. Experimental results show that the STRE model significantly outperforms other regularization models that are widely used in current practice such as Tikhonov zero-order, Tikhonov first-order and L1 first-order regularization methods.
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Education and gender bias in the sex ratio at birth: evidence from India.
Echávarri, Rebeca A; Ezcurra, Roberto
2010-02-01
This article investigates the possible existence of a nonlinear link between female disadvantage in natality and education. To this end, we devise a theoretical model based on the key role of social interaction in explaining people's acquisition of preferences, which justifies the existence of a nonmonotonic relationship between female disadvantage in natality and education. The empirical validity of the proposed model is examined for the case of India, using district-level data. In this context, our econometric analysis pays particular attention to the role of spatial dependence to avoid any potential problems of misspecification. The results confirm that the relationship between the sex ratio at birth and education in India follows an inverted U-shape. This finding is robust to the inclusion of additional explanatory variables in the analysis, and to the choice of the spatial weight matrix used to quantify the spatial interdependence between the sample districts.
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Smith, Ashlyn L.; Romski, MaryAnn; Sevcik, Rose A.; Adamson, Lauren B.; Barker, R. Michael
2013-01-01
This study extended research on the Down syndrome advantage by examining differences in parent stress and parent perceptions of language development between 29 parents of young children with Down syndrome and 82 parents of children with other developmental disabilities. Parents of children with Down syndrome reported lower levels of total stress, child-related stress, and stress surrounding the parent-child interaction. Parents of children in both groups reported that they felt successful in their ability to impact their children’s communication development but did differ on perceptions of difficulty such that parents of children with Down syndrome perceived their children’s communication difficulties as less severe despite the children exhibiting similar language skills. Finally, after accounting for potential explanatory confounding variables, child diagnosis remained a significant predictor of parent stress and perceptions of language development. Results highlight the importance of considering etiology when assisting families raising a child with a disability. PMID:24753637
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Dimitriou, Loukas; Stylianou, Katerina; Abdel-Aty, Mohamed A
2018-03-01
Rear-end crashes are one of the most frequently occurring crash types, especially in urban networks. An understanding of the contributing factors and their significant association with rear-end crashes is of practical importance and will help in the development of effective countermeasures. The objective of this study is to assess rear-end crash potential at a microscopic level in an urban environment, by investigating vehicle-by-vehicle interactions. To do so, several traffic parameters at the individual vehicle level have been taken into consideration, for capturing car-following characteristics and vehicle interactions, and to investigate their effect on potential rear-end crashes. In this study rear-end crash potential was estimated based on stopping distance between two consecutive vehicles, and four rear-end crash potential cases were developed. The results indicated that 66.4% of the observations were estimated as rear-end crash potentials. It was also shown that rear-end crash potential was presented when traffic flow and speed standard deviation were higher. Also, locational characteristics such as lane of travel and location in the network were found to affect drivers' car following decisions and additionally, it was shown that speeds were lower and headways higher when Heavy Goods Vehicles lead. Finally, a model-based behavioral analysis based on Multinomial Logit regression was conducted to systematically identify the statistically significant variables in explaining rear-end risk potential. The modeling results highlighted the significance of the explanatory variables associated with rear-end crash potential, however it was shown that their effect varied among different model configurations. The outcome of the results can be of significant value for several purposes, such as real-time monitoring of risk potential, allocating enforcement units in urban networks and designing targeted proactive safety policies. Copyright © 2018 Elsevier Ltd. All rights reserved.
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ERIC Educational Resources Information Center
Saar, Ellu; Unt, Marge; Helemäe, Jelena; Oras, Kaja; Täht, Kadri
2014-01-01
Since the 1980s, growing globalisation and economic restructuring coupled with expansion of tertiary education contributed to tremendous change in the labour market entry process in Europe. Most previous studies have been quantitative, concentrated on the supply aspect and analysed the role of education as the explanatory variable of youth labour…
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Predictive equations for dimensions and leaf area of coastal Southern California street trees
P.J. Peper; E.G. McPherson; S.M. Mori
2001-01-01
Tree height, crown height, crown width, diameter at breast height (dbh), and leaf area were measured for 16 species of commonly planted street trees in the coastal southern California city of Santa Monica, USA. The randomly sampled trees were planted from 1 to 44 years ago. Using number of years after planting or dbh as explanatory variables, mean values of dbh, tree...
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2016-08-01
ice have catastrophic effects on facilities, infrastructure, and military testing and training. Permafrost temperature , thickness, and geographic...treeline) and fire severity (~0 to ~100% SOL consumption ), they provide an excellent suite of sites to test and quantify the effects of fire severity...stages .........................59 Table 6.1. Variables included in explanatory matrix for black spruce dominance ............68 Table 6.2. Mixed effect
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2016-08-01
catastrophic effects on facilities, infrastructure, and military testing and training. Permafrost temperature , thickness, and geographic continuity...and fire severity (~0 to ~100% SOL consumption ), they provide an excellent suite of sites to test and quantify the effects of fire severity on plant...59 Table 6.1. Variables included in explanatory matrix for black spruce dominance ............68 Table 6.2. Mixed effect model
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ERIC Educational Resources Information Center
Lewis, Todd F.; Likis-Werle, Elizabeth; Fulton, Cheryl L.
2012-01-01
Drinking patterns and rates at historically Black colleges and universities (HBCU) are not well understood. Social norms and perceptions of risk are two explanatory mechanisms that have accounted for a significant amount of variance in college student drinking at predominantly White campuses. However, these models have not been examined among…
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ERIC Educational Resources Information Center
Riegel, Lisa A.
2012-01-01
The goal of this research was to explore the construct of academic optimism at the principal level and examine possible explanatory variables for the factors that emerged from the principal academic optimism scale. Academic optimism contains efficacy, trust and academic emphasis (Hoy, Tarter & Woolfolk Hoy, 2006). It has been studied at the…
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Efficacy of generic allometric equations for estimating biomass: a test in Japanese natural forests.
Ishihara, Masae I; Utsugi, Hajime; Tanouchi, Hiroyuki; Aiba, Masahiro; Kurokawa, Hiroko; Onoda, Yusuke; Nagano, Masahiro; Umehara, Toru; Ando, Makoto; Miyata, Rie; Hiura, Tsutom
2015-07-01
Accurate estimation of tree and forest biomass is key to evaluating forest ecosystem functions and the global carbon cycle. Allometric equations that estimate tree biomass from a set of predictors, such as stem diameter and tree height, are commonly used. Most allometric equations are site specific, usually developed from a small number of trees harvested in a small area, and are either species specific or ignore interspecific differences in allometry. Due to lack of site-specific allometries, local equations are often applied to sites for which they were not originally developed (foreign sites), sometimes leading to large errors in biomass estimates. In this study, we developed generic allometric equations for aboveground biomass and component (stem, branch, leaf, and root) biomass using large, compiled data sets of 1203 harvested trees belonging to 102 species (60 deciduous angiosperm, 32 evergreen angiosperm, and 10 evergreen gymnosperm species) from 70 boreal, temperate, and subtropical natural forests in Japan. The best generic equations provided better biomass estimates than did local equations that were applied to foreign sites. The best generic equations included explanatory variables that represent interspecific differences in allometry in addition to stem diameter, reducing error by 4-12% compared to the generic equations that did not include the interspecific difference. Different explanatory variables were selected for different components. For aboveground and stem biomass, the best generic equations had species-specific wood specific gravity as an explanatory variable. For branch, leaf, and root biomass, the best equations had functional types (deciduous angiosperm, evergreen angiosperm, and evergreen gymnosperm) instead of functional traits (wood specific gravity or leaf mass per area), suggesting importance of other traits in addition to these traits, such as canopy and root architecture. Inclusion of tree height in addition to stem diameter improved the performance of the generic equation only for stem biomass and had no apparent effect on aboveground, branch, leaf, and root biomass at the site level. The development of a generic allometric equation taking account of interspecific differences is an effective approach for accurately estimating aboveground and component biomass in boreal, temperate, and subtropical natural forests.
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Jung, Hungu; Yamasaki, Masahiro
2016-12-08
Reduced lower extremity range of motion (ROM) and muscle strength are related to functional disability in older adults who cannot perform one or more activities of daily living (ADL) independently. The purpose of this study was to determine which factors of seven lower extremity ROMs and two muscle strengths play dominant roles in the physical performance of community-dwelling older women. Ninety-five community-dwelling older women (mean age ± SD, 70.7 ± 4.7 years; age range, 65-83 years) were enrolled in this study. Seven lower extremity ROMs (hip flexion, hip extension, knee flexion, internal and external hip rotation, ankle dorsiflexion, and ankle plantar flexion) and two muscle strengths (knee extension and flexion) were measured. Physical performance tests, including functional reach test (FRT), 5 m gait test, four square step test (FSST), timed up and go test (TUGT), and five times sit-to-stand test (FTSST) were performed. Stepwise regression models for each of the physical performance tests revealed that hip extension ROM and knee flexion strength were important explanatory variables for FRT, FSST, and FTSST. Furthermore, ankle plantar flexion ROM and knee extension strength were significant explanatory variables for the 5 m gait test and TUGT. However, ankle dorsiflexion ROM was a significant explanatory variable for FRT alone. The amount of variance on stepwise multiple regression for the five physical performance tests ranged from 25 (FSST) to 47% (TUGT). Hip extension, ankle dorsiflexion, and ankle plantar flexion ROMs, as well as knee extension and flexion strengths may play primary roles in the physical performance of community-dwelling older women. Further studies should assess whether specific intervention programs targeting older women may achieve improvements in lower extremity ROM and muscle strength, and thereby play an important role in the prevention of dependence on daily activities and loss of physical function, particularly focusing on hip extension, ankle dorsiflexion, and ankle plantar flexion ROMs as well as knee extension and flexion strength.
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Using satellite-based measurements to explore ...
New particle formation (NPF) can potentially alter regional climate by increasing aerosol particle (hereafter particle) number concentrations and ultimately cloud condensation nuclei. The large scales on which NPF is manifest indicate potential to use satellite-based (inherently spatially averaged) measurements of atmospheric conditions to diagnose the occurrence of NPF and NPF characteristics. We demonstrate the potential for using satellite-measurements of insolation (UV), trace gas concentrations (sulfur dioxide (SO2), nitrogen dioxide (NO2), ammonia (NH3), formaldehyde (HCHO), ozone (O3)), aerosol optical properties (aerosol optical depth (AOD), Ångström exponent (AE)), and a proxy of biogenic volatile organic compound emissions (leaf area index (LAI), temperature (T)) as predictors for NPF characteristics: formation rates, growth rates, survival probabilities, and ultrafine particle (UFP) concentrations at five locations across North America. NPF at all sites is most frequent in spring, exhibits a one-day autocorrelation, and is associated with low condensational sink (AOD×AE) and HCHO concentrations, and high UV. However, there are important site-to-site variations in NPF frequency and characteristics, and in which of the predictor variables (particularly gas concentrations) significantly contribute to the explanatory power of regression models built to predict those characteristics. This finding may provide a partial explanation for the reported spatia
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Curran, Janet H.; Barth, Nancy A.; Veilleux, Andrea G.; Ourso, Robert T.
2016-03-16
Estimates of the magnitude and frequency of floods are needed across Alaska for engineering design of transportation and water-conveyance structures, flood-insurance studies, flood-plain management, and other water-resource purposes. This report updates methods for estimating flood magnitude and frequency in Alaska and conterminous basins in Canada. Annual peak-flow data through water year 2012 were compiled from 387 streamgages on unregulated streams with at least 10 years of record. Flood-frequency estimates were computed for each streamgage using the Expected Moments Algorithm to fit a Pearson Type III distribution to the logarithms of annual peak flows. A multiple Grubbs-Beck test was used to identify potentially influential low floods in the time series of peak flows for censoring in the flood frequency analysis.For two new regional skew areas, flood-frequency estimates using station skew were computed for stations with at least 25 years of record for use in a Bayesian least-squares regression analysis to determine a regional skew value. The consideration of basin characteristics as explanatory variables for regional skew resulted in improvements in precision too small to warrant the additional model complexity, and a constant model was adopted. Regional Skew Area 1 in eastern-central Alaska had a regional skew of 0.54 and an average variance of prediction of 0.45, corresponding to an effective record length of 22 years. Regional Skew Area 2, encompassing coastal areas bordering the Gulf of Alaska, had a regional skew of 0.18 and an average variance of prediction of 0.12, corresponding to an effective record length of 59 years. Station flood-frequency estimates for study sites in regional skew areas were then recomputed using a weighted skew incorporating the station skew and regional skew. In a new regional skew exclusion area outside the regional skew areas, the density of long-record streamgages was too sparse for regional analysis and station skew was used for all estimates. Final station flood frequency estimates for all study streamgages are presented for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities.Regional multiple-regression analysis was used to produce equations for estimating flood frequency statistics from explanatory basin characteristics. Basin characteristics, including physical and climatic variables, were updated for all study streamgages using a geographical information system and geospatial source data. Screening for similar-sized nested basins eliminated hydrologically redundant sites, and screening for eligibility for analysis of explanatory variables eliminated regulated peaks, outburst peaks, and sites with indeterminate basin characteristics. An ordinary least‑squares regression used flood-frequency statistics and basin characteristics for 341 streamgages (284 in Alaska and 57 in Canada) to determine the most suitable combination of basin characteristics for a flood-frequency regression model and to explore regional grouping of streamgages for explaining variability in flood-frequency statistics across the study area. The most suitable model for explaining flood frequency used drainage area and mean annual precipitation as explanatory variables for the entire study area as a region. Final regression equations for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probability discharge in Alaska and conterminous basins in Canada were developed using a generalized least-squares regression. The average standard error of prediction for the regression equations for the various annual exceedance probabilities ranged from 69 to 82 percent, and the pseudo-coefficient of determination (pseudo-R2) ranged from 85 to 91 percent.The regional regression equations from this study were incorporated into the U.S. Geological Survey StreamStats program for a limited area of the State—the Cook Inlet Basin. StreamStats is a national web-based geographic information system application that facilitates retrieval of streamflow statistics and associated information. StreamStats retrieves published data for gaged sites and, for user-selected ungaged sites, delineates drainage areas from topographic and hydrographic data, computes basin characteristics, and computes flood frequency estimates using the regional regression equations.
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Wu, Liang; Deng, Fei; Xie, Zhong; Hu, Sheng; Shen, Shu; Shi, Junming; Liu, Dan
2016-01-01
Severe fever with thrombocytopenia syndrome (SFTS) is caused by severe fever with thrombocytopenia syndrome virus (SFTSV), which has had a serious impact on public health in parts of Asia. There is no specific antiviral drug or vaccine for SFTSV and, therefore, it is important to determine the factors that influence the occurrence of SFTSV infections. This study aimed to explore the spatial associations between SFTSV infections and several potential determinants, and to predict the high-risk areas in mainland China. The analysis was carried out at the level of provinces in mainland China. The potential explanatory variables that were investigated consisted of meteorological factors (average temperature, average monthly precipitation and average relative humidity), the average proportion of rural population and the average proportion of primary industries over three years (2010–2012). We constructed a geographically weighted logistic regression (GWLR) model in order to explore the associations between the selected variables and confirmed cases of SFTSV. The study showed that: (1) meteorological factors have a strong influence on the SFTSV cover; (2) a GWLR model is suitable for exploring SFTSV cover in mainland China; (3) our findings can be used for predicting high-risk areas and highlighting when meteorological factors pose a risk in order to aid in the implementation of public health strategies. PMID:27845737
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Predicting macropores in space and time by earthworms and abiotic controls
NASA Astrophysics Data System (ADS)
Hohenbrink, Tobias Ludwig; Schneider, Anne-Kathrin; Zangerlé, Anne; Reck, Arne; Schröder, Boris; van Schaik, Loes
2017-04-01
Macropore flow increases infiltration and solute leaching. The macropore density and connectivity, and thereby the hydrological effectiveness, vary in space and time due to earthworms' burrowing activity and their ability to refill their burrows in order to survive drought periods. The aim of our study was to predict the spatiotemporal variability of macropore distributions by a set of potentially controlling abiotic variables and abundances of different earthworm species. We measured earthworm abundances and effective macropore distributions using tracer rainfall infiltration experiments in six measurement campaigns during one year at six field sites in Luxembourg. Hydrologically effective macropores were counted in three soil depths (3, 10, 30 cm) and distinguished into three diameter classes (<2, 2-6, >6 mm). Earthworms were sampled and determined to species-level. In a generalized linear modelling framework, we related macropores to potential spatial and temporal controlling factors. Earthworm species such as Lumbricus terrestris and Aporrectodea longa, local abiotic site conditions (land use, TWI, slope), temporally varying weather conditions (temperature, humidity, precipitation) and soil moisture affected the number of effective macropores. Main controlling factors and explanatory power of the models (uncertainty and model performance) varied depending on the depth and diameter class of macropores. We present spatiotemporal predictions of macropore density by daily-resolved, one year time series of macropore numbers and maps of macropore distributions at specific dates in a small-scale catchment with 5 m resolution.
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Selenium in irrigated agricultural areas of the western United States
Nolan, B.T.; Clark, M.L.
1997-01-01
A logistic regression model was developed to predict the likelihood that Se exceeds the USEPA chronic criterion for aquatic life (5 ??g/L) in irrigated agricultural areas of the western USA. Preliminary analysis of explanatory variables used in the model indicated that surface-water Se concentration increased with increasing dissolved solids (DS) concentration and with the presence of Upper Cretaceous, mainly marine sediment. The presence or absence of Cretaceous sediment was the major variable affecting Se concentration in surface-water samples from the National Irrigation Water Quality Program. Median Se concentration was 14 ??g/L in samples from areas underlain by Cretaceous sediments and < 1 ??g/L in samples from areas underlain by non-Cretaceous sediments. Wilcoxon rank sum tests indicated that elevated Se concentrations in samples from areas with Cretaceous sediments, irrigated areas, and from closed lakes and ponds were statistically significant. Spearman correlations indicated that Se was positively correlated with a binary geology variable (0.64) and DS (0.45). Logistic regression models indicated that the concentration of Se in surface water was almost certain to exceed the Environmental Protection Agency aquatic-life chronic criterion of 5 ??g/L when DS was greater than 3000 mg/L in areas with Cretaceous sediments. The 'best' logistic regression model correctly predicted Se exceedances and nonexceedances 84.4% of the time, and model sensitivity was 80.7%. A regional map of Cretaceous sediment showed the location of potential problem areas. The map and logistic regression model are tools that can be used to determine the potential for Se contamination of irrigated agricultural areas in the western USA.
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Estimation of Nitrogen Vertical Distribution by Bi-Directional Canopy Reflectance in Winter Wheat
Huang, Wenjiang; Yang, Qinying; Pu, Ruiliang; Yang, Shaoyuan
2014-01-01
Timely measurement of vertical foliage nitrogen distribution is critical for increasing crop yield and reducing environmental impact. In this study, a novel method with partial least square regression (PLSR) and vegetation indices was developed to determine optimal models for extracting vertical foliage nitrogen distribution of winter wheat by using bi-directional reflectance distribution function (BRDF) data. The BRDF data were collected from ground-based hyperspectral reflectance measurements recorded at the Xiaotangshan Precision Agriculture Experimental Base in 2003, 2004 and 2007. The view zenith angles (1) at nadir, 40° and 50°; (2) at nadir, 30° and 40°; and (3) at nadir, 20° and 30° were selected as optical view angles to estimate foliage nitrogen density (FND) at an upper, middle and bottom layer, respectively. For each layer, three optimal PLSR analysis models with FND as a dependent variable and two vegetation indices (nitrogen reflectance index (NRI), normalized pigment chlorophyll index (NPCI) or a combination of NRI and NPCI) at corresponding angles as explanatory variables were established. The experimental results from an independent model verification demonstrated that the PLSR analysis models with the combination of NRI and NPCI as the explanatory variables were the most accurate in estimating FND for each layer. The coefficients of determination (R2) of this model between upper layer-, middle layer- and bottom layer-derived and laboratory-measured foliage nitrogen density were 0.7335, 0.7336, 0.6746, respectively. PMID:25353983
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Impact of multicollinearity on small sample hydrologic regression models
NASA Astrophysics Data System (ADS)
Kroll, Charles N.; Song, Peter
2013-06-01
Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.
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Emsley, Richard; Dunn, Graham; White, Ian R
2010-06-01
Complex intervention trials should be able to answer both pragmatic and explanatory questions in order to test the theories motivating the intervention and help understand the underlying nature of the clinical problem being tested. Key to this is the estimation of direct effects of treatment and indirect effects acting through intermediate variables which are measured post-randomisation. Using psychological treatment trials as an example of complex interventions, we review statistical methods which crucially evaluate both direct and indirect effects in the presence of hidden confounding between mediator and outcome. We review the historical literature on mediation and moderation of treatment effects. We introduce two methods from within the existing causal inference literature, principal stratification and structural mean models, and demonstrate how these can be applied in a mediation context before discussing approaches and assumptions necessary for attaining identifiability of key parameters of the basic causal model. Assuming that there is modification by baseline covariates of the effect of treatment (i.e. randomisation) on the mediator (i.e. covariate by treatment interactions), but no direct effect on the outcome of these treatment by covariate interactions leads to the use of instrumental variable methods. We describe how moderation can occur through post-randomisation variables, and extend the principal stratification approach to multiple group methods with explanatory models nested within the principal strata. We illustrate the new methodology with motivating examples of randomised trials from the mental health literature.
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Hajek, A; Brettschneider, C; van den Bussche, H; Kaduszkiewicz, H; Oey, A; Wiese, B; Weyerer, S; Werle, J; Fuchs, A; Pentzek, M; Stein, J; Luck, T; Bickel, H; Mösch, E; Heser, K; Bleckwenn, M; Scherer, M; Riedel-Heller, S G; Maier, W; König, H-H
2018-01-01
The aim of this study was to identify determinants of outpatient health care utilization among the oldest old in Germany longitudinally. Multicenter prospective cohort "Study on Needs, health service use, costs and health-related quality of life in a large sample of oldest-old primary care patients (85+)" (AgeQualiDe). Individuals in very old age were recruited via GP offices at six study centers in Germany. The course of outpatient health care was observed over 10 months (two waves). Primary care patients aged 85 years and over (at baseline: n=861, with mean age of 89.0 years±2.9 years; 85-100 years). Self-reported numbers of outpatient visits to general practitioners (GP) and specialists in the past three months were used as dependent variables. Widely used scales were used to quantify explanatory variables (e.g., Geriatric Depression Scale, Instrumental Activities of Daily Living Scale, or Global Deterioration Scale). Fixed effects regressions showed that increases in GP visits were associated with increases in cognitive impairment, whereas they were not associated with changes in marital status, functional decline, increasing number of chronic conditions, increasing age, and changes in social network. Increases in specialist visits were not associated with changes in the explanatory variables. Our findings underline the importance of cognitive impairment for GP visits. Creating strategies to postpone cognitive decline might be beneficial for the health care system.
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Haney, Meryem Ozturk; Erdogan, Semra
2013-06-01
To report a study conducted to describe the determinants of Turkish school-aged children's dietary habits and body mass index. Over the past two decades, children's unhealthy dietary habits and obesity have increased rapidly. Nurses have an essential role in minimizing health-risk behaviours and promoting healthy lifestyles. Using the Interaction Model of Client Health Behavior to measure children's dietary habits and body mass index values helps to prepare health-promotion interventions. A descriptive, correlational study. The study was conducted, based on a sample of 420 fifth-grade students and their parents in one city in Turkey. The data were collected during 2007 using a questionnaire designed to assess the dietary habits and anthropometric indices. Data were analysed using quantitative analysis to identify key variables. The girls scored healthier on dietary habits than did the boys. Although dietary self-efficacy was statistically significant as an explanatory variable of dietary habits for both genders, the dietary attitude was the only explanatory variable of dietary habits for the girls. No difference was detected in the prevalence of overweight between boys and girls. Nurses are well-situated to give children dietary self-efficacy improvement, dietary attitude enhancement, and family-centred and school-based intervention programmes to reduce their unhealthy dietary habits. The model guides researchers to identify the background characteristics of children that result in the body mass index. © 2012 Blackwell Publishing Ltd.
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Agreement between self-reported sleep patterns and actigraphy in fibromyalgia and healthy women.
Segura-Jiménez, Víctor; Camiletti-Moirón, Daniel; Munguía-Izquierdo, Diego; Álvarez-Gallardo, Inmaculada C; Ruiz, Jonatan R; Ortega, Francisco B; Delgado-Fernández, Manuel
2015-01-01
To examine the agreement between objective (accelerometer) and subjective measures of sleep in fibromyalgia women (FW) and healthy women (HW). To identify explanatory variables of the discrepancies between the objective and subjective measures in FW and in HW. 127 diagnosed FW and 53 HW filled the Fibromyalgia Impact Questionnaire (FIQ) and wore the SenseWear Pro Armband (SWA) for 7 days in order to assess sleep over the last week. Participants completed the Pittsburgh Sleep Quality Index (PSQI) when the SWA was returned. The SWA showed greater total duration (74 vs. 88 min/day) and average duration (7 vs. 9 min) of wake after sleep onset in FW compared with HW. The PSQI showed poorer sleep quality in all the variables studied in FW than in HW (all, p<0.001), except time in bed. There was a lack of inter-method agreement for total sleep time, sleep time without naps and sleep latency in FW. Age and educational status explained the inter-method mean difference in sleep time in FW. High discrepancy in sleep time between the SWA and the PSQI was related to higher FIQ scores (p<0.05). The objective measure only showed higher frequency and average duration of wake after sleep onset in FW compared with HW. The agreement between the SWA and the PSQI measures of sleep were poor in the FW group. Age, educational level and the impact of fibromyalgia might be explanatory variables of the inter-method discrepancies in FW.
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Estimation of nitrogen vertical distribution by bi-directional canopy reflectance in winter wheat.
Huang, Wenjiang; Yang, Qinying; Pu, Ruiliang; Yang, Shaoyuan
2014-10-28
Timely measurement of vertical foliage nitrogen distribution is critical for increasing crop yield and reducing environmental impact. In this study, a novel method with partial least square regression (PLSR) and vegetation indices was developed to determine optimal models for extracting vertical foliage nitrogen distribution of winter wheat by using bi-directional reflectance distribution function (BRDF) data. The BRDF data were collected from ground-based hyperspectral reflectance measurements recorded at the Xiaotangshan Precision Agriculture Experimental Base in 2003, 2004 and 2007. The view zenith angles (1) at nadir, 40° and 50°; (2) at nadir, 30° and 40°; and (3) at nadir, 20° and 30° were selected as optical view angles to estimate foliage nitrogen density (FND) at an upper, middle and bottom layer, respectively. For each layer, three optimal PLSR analysis models with FND as a dependent variable and two vegetation indices (nitrogen reflectance index (NRI), normalized pigment chlorophyll index (NPCI) or a combination of NRI and NPCI) at corresponding angles as explanatory variables were established. The experimental results from an independent model verification demonstrated that the PLSR analysis models with the combination of NRI and NPCI as the explanatory variables were the most accurate in estimating FND for each layer. The coefficients of determination (R2) of this model between upper layer-, middle layer- and bottom layer-derived and laboratory-measured foliage nitrogen density were 0.7335, 0.7336, 0.6746, respectively.
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Dimensions of integration in interdisciplinary explanations of the origin of evolutionary novelty.
Love, Alan C; Lugar, Gary L
2013-12-01
Many philosophers of biology have embraced a version of pluralism in response to the failure of theory reduction but overlook how concepts, methods, and explanatory resources are in fact coordinated, such as in interdisciplinary research where the aim is to integrate different strands into an articulated whole. This is observable for the origin of evolutionary novelty-a complex problem that requires a synthesis of intellectual resources from different fields to arrive at robust answers to multiple allied questions. It is an apt locus for exploring new dimensions of explanatory integration because it necessitates coordination among historical and experimental disciplines (e.g., geology and molecular biology). These coordination issues are widespread for the origin of novel morphologies observed in the Cambrian Explosion. Despite an explicit commitment to an integrated, interdisciplinary explanation, some potential disciplinary contributors are excluded. Notable among these exclusions is the physics of ontogeny. We argue that two different dimensions of integration-data and standards-have been insufficiently distinguished. This distinction accounts for why physics-based explanatory contributions to the origin of novelty have been resisted: they do not integrate certain types of data and differ in how they conceptualize the standard of uniformitarianism in historical, causal explanations. Our analysis of these different dimensions of integration contributes to the development of more adequate and integrated explanatory frameworks. Copyright © 2013 Elsevier Ltd. All rights reserved.
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Rokuda, Mitsuhiro; Matsumaru, Naoki; Tsukamoto, Katsura
2018-02-01
Multiregional clinical trials (MRCT) are a standard strategy used to improve global drug approval efficiency and the feasibility of clinical trials. Japan is the world's third largest drug market with a unique health care system, making it a key inclusion as an operational region for MRCT (MRCT-JP) for global drug development. We aimed to identify the factors required for efficient drug development by comprehensively reviewing the clinical trials of drugs approved in Japan to identify the factors associated with whether or not MRCT-JP is implemented. We surveyed the review reports and summaries of application data published by the Pharmaceuticals and Medical Devices Agency. We identified drugs for which the clinical trial data package included MRCT-JP and selected the same number of drugs for which the clinical trial data package did not include MRCT-JP from the most recent survey period for comparison. We also examined other publication information, in addition to the review reports, as necessary. The influence of each explanatory variable was analyzed by logistic regression analysis, with whether or not MRCT-JP was implemented as the explanatory variable. Statistical significance was set at 5%. In the survey period up to September 2017, 165 drugs developed with MRCT-JP were approved for manufacture and sale in Japan. "Respiratory system," "inhalation," "biological drug," and "under review" evaluation status for the United States, European Union, and other areas, "approved" evaluation status for the United States, "new ingredients," "priority review," "non-Japanese firm," and "Top 1-10" and "Top 11-20" drug sales rankings for pharmaceutical companies were identified as potential factors leading to the implementation of MRCT-JP. In contrast, "general anti-infectives for systemic use," "various," "external," "chemical compound," "unsubmitted" evaluation status for both the United States and European Union, and "Top 51+" drug sales rankings were potential factors for not implementing MRCT-JP. Therapeutic classification and agent type, in addition to capital type and United States and European Union evaluation status suggested by a previous study, were associated with implementing MRCT-JP. It is important to determine the best way to utilize MRCT-JP to maximize the value of products. Our findings were based on successful cases and may therefore be helpful for designing clinical development plans. Appropriate use of MRCT-JP will improve productivity in the pharmaceutical industry. Copyright © 2018 Elsevier HS Journals, Inc. All rights reserved.
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Senior, Lisa A.
2017-09-15
Several streams used for recreational activities, such as fishing, swimming, and boating, in Chester County, Pennsylvania, are known to have periodic elevated concentrations of fecal coliform bacteria, a type of bacteria used to indicate the potential presence of fecally related pathogens that may pose health risks to humans exposed through water contact. The availability of near real-time continuous stream discharge, turbidity, and other water-quality data for some streams in the county presents an opportunity to use surrogates to estimate near real-time concentrations of fecal coliform (FC) bacteria and thus provide some information about associated potential health risks during recreational use of streams.The U.S. Geological Survey (USGS), in cooperation with the Chester County Health Department (CCHD) and the Chester County Water Resources Authority (CCWRA), has collected discrete stream samples for analysis of FC concentrations during March–October annually at or near five gaging stations where near real-time continuous data on stream discharge, turbidity, and water temperature have been collected since 2007 (or since 2012 at 2 of the 5 stations). In 2014, the USGS, in cooperation with the CCWRA and CCHD, began to develop regression equations to estimate FC concentrations using available near real-time continuous data. Regression equations included possible explanatory variables of stream discharge, turbidity, water temperature, and seasonal factors calculated using Julian Day with base-10 logarithmic (log) transformations of selected variables.The regression equations were developed using the data from 2007 to 2015 (101–106 discrete bacteria samples per site) for three gaging stations on Brandywine Creek (West Branch Brandywine Creek at Modena, East Branch Brandywine Creek below Downingtown, and Brandywine Creek at Chadds Ford) and from 2012 to 2015 (37–38 discrete bacteria samples per site) for one station each on French Creek near Phoenixville and White Clay Creek near Strickersville. Fecal coliform bacteria data collected by USGS in 2016 (about nine samples per site) were used to validate the equations. The best-fit regression equations included log turbidity and seasonality factors computed using Julian Day as explanatory variables to estimate log FC concentrations at all five stream sites. The adjusted coefficient of determination for the equations ranged from 0.61 to 0.76, with the strength of the regression equations likely affected in part by the limited amount and variability of FC bacteria data. During summer months, the estimated and measured FC concentrations commonly were greater than the Pennsylvania Department of Environmental Protection established standards of 200 and 400 colonies per 100 milliliters for water contact from May through September at the 5 stream sites, with concentrations typically higher at 2 sites (White Clay Creek and West Branch Brandywine Creek at Modena) than at the other 3 sites. The estimated concentrations of FC bacteria during the summer months commonly were higher than measured concentrations and therefore could be considered cautious estimates of potential human-health risk. Additional water-quality data are needed to maintain and (or) improve the ability of regression equations to estimate FC concentrations by use of surrogate data.
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ERIC Educational Resources Information Center
Lowther, Malcolm A.; And Others
This study examined the quality of teachers' work lives, teachers' job satisfaction, and the relationship between teachers' work experiences and their wider network of life experiences. Age was used as a key explanatory variable in each phase of this analysis. Data analyzed were from three sets of national surveys: (1) the 1969 Survey of Working…
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ERIC Educational Resources Information Center
Luna, Andrew L.
2007-01-01
The purpose of this study was to determine if a market ratio factor was a better predictor of faculty salaries than the use of k-1 dummy variables representing the various disciplines. This study used two multiple regression analyses to develop an explanatory model to determine which model might best explain faculty salaries. A total of 20 out of…
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Equal Pay for Equal Work in Academic Obstetrics and Gynecology.
Eichelberger, Kacey Y
2018-02-01
The most compelling data suggest women in academic obstetrics and gynecology earn approximately $36,000 less than male colleagues per year in regression models correcting for commonly cited explanatory variables. Although residual confounding may exist, academic departments in the United States should consider rigorous examination of their own internal metrics around salary to ensure gender-neutral compensation, commonly referred to as equal pay for equal work.
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Roth, Michal
2016-12-06
High-pressure phase behavior of systems containing water, carbon dioxide and organics has been important in several environment- and energy-related fields including carbon capture and storage, CO 2 sequestration and CO 2 -assisted enhanced oil recovery. Here, partition coefficients (K-factors) of organic solutes between water and supercritical carbon dioxide have been correlated with extended linear solvation energy relationships (LSERs). In addition to the Abraham molecular descriptors of the solutes, the explanatory variables also include the logarithm of solute vapor pressure, the solubility parameters of carbon dioxide and water, and the internal pressure of water. This is the first attempt to include also the properties of water as explanatory variables in LSER correlations of K-factor data in CO 2 -water-organic systems. Increasing values of the solute hydrogen bond acidity, the solute hydrogen bond basicity, the solute dipolarity/polarizability, the internal pressure of water and the solubility parameter of water all tend to reduce the K-factor, that is, to favor the solute partitioning to the water-rich phase. On the contrary, increasing values of the solute characteristic volume, the solute vapor pressure and the solubility parameter of CO 2 tend to raise the K-factor, that is, to favor the solute partitioning to the CO 2 -rich phase.
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A data-centric approach to understanding the pricing of financial options
NASA Astrophysics Data System (ADS)
Healy, J.; Dixon, M.; Read, B.; Cai, F. F.
2002-05-01
We investigate what can be learned from a purely phenomenological study of options prices without modelling assumptions. We fitted neural net (NN) models to LIFFE ``ESX'' European style FTSE 100 index options using daily data from 1992 to 1997. These non-parametric models reproduce the Black-Scholes (BS) analytic model in terms of fit and performance measures using just the usual five inputs (S, X, t, r, IV). We found that adding transaction costs (bid-ask spread) to these standard five parameters gives a comparable fit and performance. Tests show that the bid-ask spread can be a statistically significant explanatory variable for option prices. The difference in option prices between the models with transaction costs and those without ranges from about -3.0 to +1.5 index points, varying with maturity date. However, the difference depends on the moneyness (S/X), being greatest in-the-money. This suggests that use of a five-factor model can result in a pricing difference of up to #10 to #30 per call option contract compared with modelling under transaction costs. We found that the influence of transaction costs varied between different yearly subsets of the data. Open interest is also a significant explanatory variable, but volume is not.
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Oh, EunJung; Song, EunJu; Shin, JungEun
2017-12-01
The purposes of this study were to identify correlations between body mass index, body image, and self-esteem in patients with schizophrenia and to analyse the specific factors affecting self-esteem. This study had a descriptive design, utilising a cross-sectional survey. Participants were patients with schizophrenia who were admitted to a mental health facility in South Korea. A total of 180 questionnaires were distributed, and an appropriate total sample size of 167 valid questionnaires was analysed. Self-esteem was significantly correlated with body image, the subscale of appearance orientation, and body areas satisfaction. However, body mass index exhibited no significant correlation with any variable. The variables found to have a significant explanatory power of 21.4% were appearance orientation and body areas satisfaction. The explanatory power of all factors was 33.6%. The self-esteem of patients with schizophrenia was influenced by body mass index and body image. The positive symptoms of schizophrenia can be controlled by medication, whereas negative symptoms can be improved through education and nursing care with medication. Thus, psychiatric nurses should develop education and care programs that contribute to the positive body image and self-esteem of patients with schizophrenia. Copyright © 2017 Elsevier Inc. All rights reserved.
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Vyncke, Bart; Perko, Tanja; Van Gorp, Baldwin
2017-03-01
The media play an important role in risk communication, providing information about accidents, both nearby and far away. Each media source has its own presentation style, which could influence how the audience perceives the presented risk. This study investigates the explanatory power of 12 information sources (traditional media, new media, social media, and interpersonal communication) for the perceived risk posed by radiation released from the damaged Fukushima nuclear power plant on respondents' own health and that of the population in general. The analysis controlled for attitude toward nuclear energy, gender, education, satisfaction with the media coverage, and duration of attention paid to the coverage. The study uses a large empirical data set from a public opinion survey, which is representative for the Belgian population with respect to six sociodemographic variables. Results show that three information sources are significant regressors of perceived health-related risk of the nuclear accident: television, interpersonal communication, and the category of miscellaneous online sources. More favorable attitudes toward nuclear power, longer attention to the coverage, and higher satisfaction with the provided information lead to lower risk perception. Taken together, the results suggest that the media can indeed have a modest influence on how the audience perceives a risk. © 2016 Society for Risk Analysis.
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Fossum, Kenneth D.; O'Day, Christie M.; Wilson, Barbara J.; Monical, Jim E.
2001-01-01
Stormwater and streamflow in Maricopa County were monitored to (1) describe the physical, chemical, and toxicity characteristics of stormwater from areas having different land uses, (2) describe the physical, chemical, and toxicity characteristics of streamflow from areas that receive urban stormwater, and (3) estimate constituent loads in stormwater. Urban stormwater and streamflow had similar ranges in most constituent concentrations. The mean concentration of dissolved solids in urban stormwater was lower than in streamflow from the Salt River and Indian Bend Wash. Urban stormwater, however, had a greater chemical oxygen demand and higher concentrations of most nutrients. Mean seasonal loads and mean annual loads of 11 constituents and volumes of runoff were estimated for municipalities in the metropolitan Phoenix area, Arizona, by adjusting regional regression equations of loads. This adjustment procedure uses the original regional regression equation and additional explanatory variables that were not included in the original equation. The adjusted equations had standard errors that ranged from 161 to 196 percent. The large standard errors of the prediction result from the large variability of the constituent concentration data used in the regression analysis. Adjustment procedures produced unsatisfactory results for nine of the regressions?suspended solids, dissolved solids, total phosphorus, dissolved phosphorus, total recoverable cadmium, total recoverable copper, total recoverable lead, total recoverable zinc, and storm runoff. These equations had no consistent direction of bias and no other additional explanatory variables correlated with the observed loads. A stepwise-multiple regression or a three-variable regression (total storm rainfall, drainage area, and impervious area) and local data were used to develop local regression equations for these nine constituents. These equations had standard errors from 15 to 183 percent.
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Naavaal, Shillpa; Barker, Laurie K; Griffin, Susan O
2017-12-01
We examined the association between utilization of care for a dental problem (utilization-DP) and parent-reported dental problem (DP) urgency among children with DP by type of health care insurance coverage. We used weighted 2008 National Health Interview Survey data from 2,834 children, aged 2-17 years with at least one DP within the 6 months preceding survey. Explanatory variables were selected based on Andersen's model of healthcare utilization. Need was considered urgent if DP included toothache, bleeding gums, broken or missing teeth, broken or missing filling, or decayed teeth and otherwise as non-urgent. The primary enabling variable, insurance, had four categories: none, private health no dental coverage (PHND), private health and dental (PHD), or Medicaid/State Children's Health Insurance Program (SCHIP). Predisposing variables included sociodemographic characteristics. We used bivariate and multivariate analyses to identify explanatory variables' association with utilization-DP. Using logistic regression, we obtained adjusted estimates of utilization-DP by urgency for each insurance category. In bivariate analyses, utilization-DP was associated with both insurance and urgency. In multivariate analyses, the difference in percent utilizing care for an urgent versus non-urgent DP among children covered by Medicaid/SCHIP was 32 percentage points; PHD, 25 percentage points; PHND, 12 percentage points; and no insurance, 14 percentage points. The difference in utilization by DP urgency was higher for children with Medicaid/SCHIP compared with either PHND or uninsured children. Expansion of Medicaid/SCHIP may permit children to receive care for urgent DPs who otherwise may not, due to lack of dental insurance. © 2016 American Association of Public Health Dentistry.
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Groundwater salinity in a floodplain forest impacted by saltwater intrusion.
Kaplan, David A; Muñoz-Carpena, Rafael
2014-11-15
Coastal wetlands occupy a delicate position at the intersection of fresh and saline waters. Changing climate and watershed hydrology can lead to saltwater intrusion into historically freshwater systems, causing plant mortality and loss of freshwater habitat. Understanding the hydrological functioning of tidally influenced floodplain forests is essential for advancing ecosystem protection and restoration goals, however finding direct relationships between hydrological inputs and floodplain hydrology is complicated by interactions between surface water, groundwater, and atmospheric fluxes in variably saturated soils with heterogeneous vegetation and topography. Thus, an alternative method for identifying common trends and causal factors is required. Dynamic factor analysis (DFA), a time series dimension reduction technique, models temporal variation in observed data as linear combinations of common trends, which represent unexplained common variability, and explanatory variables. DFA was applied to model shallow groundwater salinity in the forested floodplain wetlands of the Loxahatchee River (Florida, USA), where altered watershed hydrology has led to changing hydroperiod and salinity regimes and undesired vegetative changes. Long-term, high-resolution groundwater salinity datasets revealed dynamics over seasonal and yearly time periods as well as over tidal cycles and storm events. DFA identified shared trends among salinity time series and a full dynamic factor model simulated observed series well (overall coefficient of efficiency, Ceff=0.85; 0.52≤Ceff≤0.99). A reduced multilinear model based solely on explanatory variables identified in the DFA had fair to good results (Ceff=0.58; 0.38≤Ceff≤0.75) and may be used to assess the effects of restoration and management scenarios on shallow groundwater salinity in the Loxahatchee River floodplain. Copyright © 2014 Elsevier B.V. All rights reserved.
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2011-01-01
Objective Few studies have examined the link between health system strength and important public health outcomes across nations. We examined the association between health system indicators and mortality rates. Methods We used mixed effects linear regression models to investigate the strength of association between outcome and explanatory variables, while accounting for geographic clustering of countries. We modelled infant mortality rate (IMR), child mortality rate (CMR), and maternal mortality rate (MMR) using 13 explanatory variables as outlined by the World Health Organization. Results Significant protective health system determinants related to IMR included higher physician density (adjusted rate ratio [aRR] 0.81; 95% Confidence Interval [CI] 0.71-0.91), higher sustainable access to water and sanitation (aRR 0.85; 95% CI 0.78-0.93), and having a less corrupt government (aRR 0.57; 95% CI 0.40-0.80). Out-of-pocket expenditures on health (aRR 1.29; 95% CI 1.03-1.62) were a risk factor. The same four variables were significantly related to CMR after controlling for other variables. Protective determinants of MMR included access to water and sanitation (aRR 0.88; 95% CI 0.82-0.94), having a less corrupt government (aRR 0.49; 95%; CI 0.36-0.66), and higher total expenditures on health per capita (aRR 0.84; 95% CI 0.77-0.92). Higher fertility rates (aRR 2.85; 95% CI: 2.02-4.00) were found to be a significant risk factor for MMR. Conclusion Several key measures of a health system predict mortality in infants, children, and maternal mortality rates at the national level. Improving access to water and sanitation and reducing corruption within the health sector should become priorities. PMID:22023970
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NASA Astrophysics Data System (ADS)
Kirby, Nicola Frances; Dempster, Edith Roslyn
2014-11-01
The Foundation Programme of the Centre for Science Access at the University of KwaZulu-Natal, South Africa provides access to tertiary science studies to educationally disadvantaged students who do not meet formal faculty entrance requirements. The low number of students proceeding from the programme into mainstream is of concern, particularly given the national imperative to increase participation and levels of performance in tertiary-level science. An attempt was made to understand foundation student performance in a campus of this university, with the view to identifying challenges and opportunities for remediation in the curriculum and processes of selection into the programme. A classification and regression tree analysis was used to identify which variables best described student performance. The explanatory variables included biographical and school-history data, performance in selection tests, and socio-economic data pertaining to their year in the programme. The results illustrate the prognostic reliability of the model used to select students, raise concerns about the inefficiency of school performance indicators as a measure of students' academic potential in the Foundation Programme, and highlight the importance of accommodation arrangements and financial support for student success in their access year.
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Differences in medication adherence are associated with beliefs about medicines in asthma and COPD.
Brandstetter, Susanne; Finger, Tamara; Fischer, Wiebke; Brandl, Magdalena; Böhmer, Merle; Pfeifer, Michael; Apfelbacher, Christian
2017-01-01
Adherence to medication is crucial for achieving treatment control in chronic obstructive lung diseases. This study refers to the "necessity-concerns framework" and examines the associations between beliefs about medicines and self-reported medication adherence in people with chronic obstructive lung disease. 402 patients (196 with asthma, 206 with COPD) participated in the study and completed a questionnaire comprising the "Beliefs about Medicines-Questionnaire" (BMQ) and the "Medication Adherence Report Scale" (MARS). Multivariable logistic regression analyses with the BMQ-subscales as explanatory and the dichotomized MARS-score as dependent variable were computed for the asthma and the COPD sample, respectively, and adjusted for potentially confounding variables. 19% of asthma patients and 34% of COPD patients were completely adherent to their prescribed medication. While specific beliefs about the necessity of medicines were positively associated with medication adherence both in patients with asthma and with COPD, general beliefs about harm and overuse of medicines by doctors were negatively associated with medication adherence only among patients with asthma. The findings of this study suggest that patients' specific beliefs about the necessity of medicines represent an important modifiable target for improving patient-doctor consultations when prescribing medicines.
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Relationships between depression, anxiety, and pain in a group of university music students.
Wristen, Brenda W; Fountain, Sarah E
2013-09-01
There is emerging interest in studying the incidence of music-related injuries and problems among students. The current study drew on a data set collected from 287 music majors and minors at a large US midwestern university school of music in order to determine if correlations existed between anxiety and/or depression and the reported presence of physical pain, and to understand the nature of any such relationships. Physical pain symptoms were scored on a scale of 0 (none) to 10 (excruciating) and summed across 21 body regions. Depression and anxiety symptoms were scored as none (0), mild (1), moderate (2), or severe (3), and each summed across either 13 symptoms for depression or 8 symptoms for anxiety. The potential linear relationship among these variables was evaluated using F-tests (as part of ANOVAs) and linear regression parameter estimation techniques. The explanatory value of these relationships was evaluated using R² values. Results indicate a clear positive linear relationship between both depression and pain, and anxiety and pain. However, the presence of depression and/or anxiety symptoms was insufficient to explain variability in pain scores of these participants.
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Tsai, Pui-Jen; Yeh, Hsi-Chyi
2013-04-29
The Taiwan area comprises the main island of Taiwan and several small islands located off the coast of the Southern China. The eastern two-thirds of Taiwan are characterized by rugged mountains covered with tropical and subtropical vegetation. The western region of Taiwan is characterized by flat or gently rolling plains. Geographically, the Taiwan area is diverse in ecology and environment, although scrub typhus threatens local human populations. In this study, we investigate the effects of seasonal and meteorological factors on the incidence of scrub typhus infection among 10 local climate regions. The correlation between the spatial distribution of scrub typhus and cultivated forests in Taiwan, as well as the relationship between scrub typhus incidence and the population density of farm workers is examined. We applied Pearson's product moment correlation to calculate the correlation between the incidence of scrub typhus and meteorological factors among 10 local climate regions. We used the geographically weighted regression (GWR) method, a type of spatial regression that generates parameters disaggregated by the spatial units of analysis, to detail and map each regression point for the response variables of the standardized incidence ratio (SIR)-district scrub typhus. We also applied the GWR to examine the explanatory variables of types of forest-land use and farm worker density in Taiwan in 2005. In the Taiwan Area, scrub typhus endemic areas are located in the southeastern regions and mountainous townships of Taiwan, as well as the Pescadore, Kinmen, and Matou Islands. Among these islands and low-incidence areas in the central western and southwestern regions of Taiwan, we observed a significant correlation between scrub typhus incidence and surface temperature. No similar significant correlation was found in the endemic areas (e.g., the southeastern region and the mountainous area of Taiwan). Precipitation correlates positively with scrub typhus incidence in 3 local climate regions (i.e., Taiwan's central western and southwestern regions, and the Kinmen Islands). Relative humidity correlates positively with incidence in Southwestern Taiwan and the Kinmen Islands. The number of wet days correlates positively with incidence in Southwestern Taiwan. The duration of sunshine correlates positively with incidence in Central Western Taiwan, as well as the Kinmen and Matou Islands. In addition, the 10 local climatic regions can be classified into the following 3 groups, based on the warm-cold seasonal fluctuations in scrub typhus incidence: (a) Type 1, evident in 5 local climate regions (Taiwan's northern, northwestern, northeastern, and southeastern regions, as well as the mountainous area); (b) Type 2 (Taiwan's central western and southwestern regions, and the Pescadore Islands); and (c) Type 3 (the Kinmen and Matou Islands). In the GWR models, the response variable of the SIR-district scrub typhus has a statistically significantly positive association with 2 explanatory variables (farm worker population density and timber management). In addition, other explanatory variables (recreational forests, natural reserves, and "other purpose" areas) show positive or negative signs for parameter estimates in various locations in Taiwan. Negative signs of parameter estimates occurred only for the explanatory variables of national protectorates, plantations, and clear-cut areas. The results of this study show that scrub typhus in Taiwan can be classified into 3 types. Type 1 exhibits no climatic effect, whereas the incidence of Type 2 correlates positively with higher temperatures during the warm season, and the incidence of Type 3 correlates positively with higher surface temperatures and longer hours of sunshine. The results also show that in the mountainous township areas of Taiwan's central and southern regions, as well as in Southeastern Taiwan, higher SIR values for scrub typhus are associated with the following variables: farm worker population density, timber management, and area type (i.e., recreational forest, natural reserve, or other purpose).
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2013-01-01
Background The Taiwan area comprises the main island of Taiwan and several small islands located off the coast of the Southern China. The eastern two-thirds of Taiwan are characterized by rugged mountains covered with tropical and subtropical vegetation. The western region of Taiwan is characterized by flat or gently rolling plains. Geographically, the Taiwan area is diverse in ecology and environment, although scrub typhus threatens local human populations. In this study, we investigate the effects of seasonal and meteorological factors on the incidence of scrub typhus infection among 10 local climate regions. The correlation between the spatial distribution of scrub typhus and cultivated forests in Taiwan, as well as the relationship between scrub typhus incidence and the population density of farm workers is examined. Methods We applied Pearson’s product moment correlation to calculate the correlation between the incidence of scrub typhus and meteorological factors among 10 local climate regions. We used the geographically weighted regression (GWR) method, a type of spatial regression that generates parameters disaggregated by the spatial units of analysis, to detail and map each regression point for the response variables of the standardized incidence ratio (SIR)-district scrub typhus. We also applied the GWR to examine the explanatory variables of types of forest-land use and farm worker density in Taiwan in 2005. Results In the Taiwan Area, scrub typhus endemic areas are located in the southeastern regions and mountainous townships of Taiwan, as well as the Pescadore, Kinmen, and Matou Islands. Among these islands and low-incidence areas in the central western and southwestern regions of Taiwan, we observed a significant correlation between scrub typhus incidence and surface temperature. No similar significant correlation was found in the endemic areas (e.g., the southeastern region and the mountainous area of Taiwan). Precipitation correlates positively with scrub typhus incidence in 3 local climate regions (i.e., Taiwan’s central western and southwestern regions, and the Kinmen Islands). Relative humidity correlates positively with incidence in Southwestern Taiwan and the Kinmen Islands. The number of wet days correlates positively with incidence in Southwestern Taiwan. The duration of sunshine correlates positively with incidence in Central Western Taiwan, as well as the Kinmen and Matou Islands. In addition, the 10 local climatic regions can be classified into the following 3 groups, based on the warm-cold seasonal fluctuations in scrub typhus incidence: (a) Type 1, evident in 5 local climate regions (Taiwan’s northern, northwestern, northeastern, and southeastern regions, as well as the mountainous area); (b) Type 2 (Taiwan’s central western and southwestern regions, and the Pescadore Islands); and (c) Type 3 (the Kinmen and Matou Islands). In the GWR models, the response variable of the SIR-district scrub typhus has a statistically significantly positive association with 2 explanatory variables (farm worker population density and timber management). In addition, other explanatory variables (recreational forests, natural reserves, and “other purpose” areas) show positive or negative signs for parameter estimates in various locations in Taiwan. Negative signs of parameter estimates occurred only for the explanatory variables of national protectorates, plantations, and clear-cut areas. Conclusion The results of this study show that scrub typhus in Taiwan can be classified into 3 types. Type 1 exhibits no climatic effect, whereas the incidence of Type 2 correlates positively with higher temperatures during the warm season, and the incidence of Type 3 correlates positively with higher surface temperatures and longer hours of sunshine. The results also show that in the mountainous township areas of Taiwan’s central and southern regions, as well as in Southeastern Taiwan, higher SIR values for scrub typhus are associated with the following variables: farm worker population density, timber management, and area type (i.e., recreational forest, natural reserve, or other purpose). PMID:23627966
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Sentiment analysis in twitter data using data analytic techniques for predictive modelling
NASA Astrophysics Data System (ADS)
Razia Sulthana, A.; Jaithunbi, A. K.; Sai Ramesh, L.
2018-04-01
Sentiment analysis refers to the task of natural language processing to determine whether a piece of text contains subjective information and the kind of subjective information it expresses. The subjective information represents the attitude behind the text: positive, negative or neutral. Understanding the opinions behind user-generated content automatically is of great concern. We have made data analysis with huge amount of tweets taken as big data and thereby classifying the polarity of words, sentences or entire documents. We use linear regression for modelling the relationship between a scalar dependent variable Y and one or more explanatory variables (or independent variables) denoted X. We conduct a series of experiments to test the performance of the system.
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Birth Order and health: major issues.
Elliott, B A
1992-08-01
Birth Order has been described as a variable with a complex relationship to child and adult outcomes. A review of the medical literature over the past 5 years identified 20 studies that investigated the relationship between Birth Order and a health outcome. Only one of the studies established a relationship between Birth Order and a health outcome: third and fourth-born children have a higher incidence of accidents that result in hospitalization. The other demonstrated relationships are each explained by intervening variables or methodological limitations. Although Birth Order is not a strongly independent explanatory factor in understanding health outcomes, it is an important marker variable. Statistically significant relationships between Birth Order and health outcomes yield insights into the ways a family influences an individual's health.
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An instrumental variable random-coefficients model for binary outcomes
Chesher, Andrew; Rosen, Adam M
2014-01-01
In this paper, we study a random-coefficients model for a binary outcome. We allow for the possibility that some or even all of the explanatory variables are arbitrarily correlated with the random coefficients, thus permitting endogeneity. We assume the existence of observed instrumental variables Z that are jointly independent with the random coefficients, although we place no structure on the joint determination of the endogenous variable X and instruments Z, as would be required for a control function approach. The model fits within the spectrum of generalized instrumental variable models, and we thus apply identification results from our previous studies of such models to the present context, demonstrating their use. Specifically, we characterize the identified set for the distribution of random coefficients in the binary response model with endogeneity via a collection of conditional moment inequalities, and we investigate the structure of these sets by way of numerical illustration. PMID:25798048
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Sithisarankul, P; Weaver, V M; Diener-West, M; Strickland, P T
1997-06-01
Collinearity is the situation which arises in multiple regression when some or all of the explanatory variables are so highly correlated with one another that it becomes very difficult, if not impossible, to disentangle their influences and obtain a reasonably precise estimate of their effects. Suppressor variable is one of the extreme situations of collinearity that one variable can substantially increase the multiple correlation when combined with a variable that is only modestly correlated with the response variable. In this study, we describe the process by which we disentangled and discovered multicollinearity and its consequences, namely artificial interaction, using the data from cross-sectional quantification of several biomarkers. We showed how the collinearity between one biomarker (blood lead level) and another (urinary trans, trans-muconic acid) and their interaction (blood lead level* urinary trans, trans-muconic acid) can lead to the observed artificial interaction on the third biomarker (urinary 5-aminolevulinic acid).
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Akachi, Yoko; Zumla, Alimuddin; Atun, Rifat
2012-05-15
To assess the impact of investment in national tuberculosis programs (NTPs) on NTP performance and tuberculosis burden in 22 high-burden countries, as determined by the World Health Organization (WHO). Estimates of annual tuberculosis burden and NTP performance indicators and control variables during 2002-2009 were obtained from the Organization for Economic Cooperation and Development, the WHO, the World Bank, and the Penn World Table for the 22 high-burden countries. Panel data analysis was performed using the outcome variables tuberculosis incidence, prevalence, and mortality and the key explanatory variables Partnership case detection rate and treatment success rate, controlling for gross domestic product per capita, population structure, and human immunodeficiency virus (HIV) prevalence. A $1 per capita (general population) higher NTP budget (including domestic and external sources) was associated with a 1.9% (95% confidence interval, .12%-3.6%) higher estimated case detection rate the following year for the 22 high-burden countries between 2002 and 2009. In the final models, which corrected for autocorrelation and heteroskedasticity, achieving the STOP TB Partnership case detection rate target of >70% was associated with significantly (P < .01) lower tuberculosis incidence, prevalence, and mortality the following year, even when controlling for general economic development and HIV prevalence as potential confounding variables. Increased investment in NTPs was significantly associated with improved performance and with a downward trend in the tuberculosis burden in the 22 high-burden countries during 2002-2009.
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Parent Predictors of Adolescents' Explanatory Style
ERIC Educational Resources Information Center
Vélez, Clorinda E.; Krause, Elizabeth D.; Brunwasser, Steven M.; Freres, Derek R.; Abenavoli, Rachel M.; Gillham, Jane E.
2015-01-01
The current study tested the prospective relations (6-month lag) between three aspects of the parent-child relationship at Time 1 (T1) and adolescents' explanatory styles at Time 2 (T2): caregiving behaviors, parents' explanatory style for their own negative events, and parents' explanatory style for their children's negative events. The sample…
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Fall in homicides in the City of São Paulo: an exploratory analysis of possible determinants
Peres, Maria Fernanda Tourinho; de Almeida, Juliana Feliciano; Vicentin, Diego; Cerda, Magdalena; Cardia, Nancy; Adorno, Sérgio
2012-01-01
Throughout the first decade of the 2000s the homicide mortality rate (HMR) showed a significant reduction in the state and the city of São Paulo (MSP). The aim of this study is to describe the trend of HMR, socio-demographic indicators, and the investment in social and public security, and to analyze the correlation between HMR and independent variables in the MSP between 1996 and 2008. An exploratory time series ecological study was conducted. The following variables were included: HMR per 100,000 inhabitants, socio-demographic indicators, and investments in social and public security. The moving-averages for all variables were calculated and trends were analyzed through Simple Linear Regression models. Annual percentage changes, the average annual change and periodic percentage changes were calculated for all variables, and the associations between annual percentage changes were tested by Spearman’s correlation analysis. Correlations were found for the proportion of youth in the population (r = 0.69), unemployment rate (r = 0.60), State budget for education and culture (r = 0.87) and health and sanitation (r = 0.56), municipal (r = 0.68) and State (r = 0.53) budget for Public Security, firearms seized (r = 0.69) and the incarceration rate (r = 0.71). The results allow us to support the hypothesis that demographic changes, acceleration of the economy, in particular the fall in unemployment, investment in social policies and changes in public security policies act synergistically to reduce HMR in São Paulo. Complex models of analysis, incorporating the joint action of different potential explanatory variables, should be developed. PMID:22218669
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Explanatory style across the life span: evidence for stability over 52 years.
Burns, M O; Seligman, M E
1989-03-01
Analyzed explanatory style across the life span. 30 Ss whose average age was 72 responded to questions about their current life and provided diaries or letters written in their youth, an average of 52 years earlier. A blind content analysis of explanatory style derived from these 2 sources revealed that explanatory style for negative events was stable throughout adult life (r = .54, p less than .002). In contrast, there appeared to be no stability of explanatory style for positive events between the same 2 time periods. These results suggest that explanatory style for negative events may persist across the life span and may constitute an enduring risk factor for depression, low achievement, and physical illness.
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ERIC Educational Resources Information Center
Leniz, Ane; Zuza, Kristina; Guiasola, Jenaro
2017-01-01
This study examines the causal reasoning that university students use to explain how dc circuits work. We analyze how students use the concepts of electric field and potential difference in their explanatory models of dc circuits, and what kinds of reasoning they use at the macroscopic and microscopic levels in their explanations. This knowledge…
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Tack, Jason D.; Fedy, Bradley C.
2015-01-01
Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development. PMID:26262876
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Tack, Jason D.; Fedy, Bradley C.
2015-01-01
Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.
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Tack, Jason D; Fedy, Bradley C
2015-01-01
Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.
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Tiruneh, Gizachew Tadele; Karim, Ali Mehryar; Avan, Bilal Iqbal; Zemichael, Nebreed Fesseha; Wereta, Tewabech Gebrekiristos; Wickremasinghe, Deepthi; Keweti, Zinar Nebi; Kebede, Zewditu; Betemariam, Wuleta Aklilu
2018-05-02
Basic emergency obstetric and newborn care (BEmONC) is a primary health care level initiative promoted in low- and middle-income countries to reduce maternal and newborn mortality. Tailored support, including BEmONC training to providers, mentoring and monitoring through supportive supervision, provision of equipment and supplies, strengthening referral linkages, and improving infection-prevention practice, was provided in a package of interventions to 134 health centers, covering 91 rural districts of Ethiopia to ensure timely BEmONC care. In recent years, there has been a growing interest in measuring program implementation strength to evaluate public health gains. To assess the effectiveness of the BEmONC initiative, this study measures its implementation strength and examines the effect of its variability across intervention health centers on the rate of facility deliveries and the met need for BEmONC. Before and after data from 134 intervention health centers were collected in April 2013 and July 2015. A BEmONC implementation strength index was constructed from seven input and five process indicators measured through observation, record review, and provider interview; while facility delivery rate and the met need for expected obstetric complications were measured from service statistics and patient records. We estimated the dose-response relationships between outcome and explanatory variables of interest using regression methods. The BEmONC implementation strength index score, which ranged between zero and 10, increased statistically significantly from 4.3 at baseline to 6.7 at follow-up (p < .05). Correspondingly, the health center delivery rate significantly increased from 24% to 56% (p < .05). There was a dose-response relationship between the explanatory and outcome variables. For every unit increase in BEmONC implementation strength score there was a corresponding average of 4.5 percentage points (95% confidence interval: 2.1-6.9) increase in facility-based deliveries; while a higher score for BEmONC implementation strength of a health facility at follow-up was associated with a higher met need. The BEmONC initiative was effective in improving institutional deliveries and may have also improved the met need for BEmONC services. The BEmONC implementation strength index can be potentially used to monitor the implementation of BEmONC interventions.
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Thrasher, Robert G; Andrew, Damon P S; Mahony, Daniel F
2011-09-01
Recently, Thrasher et al. (College Student Affairs Journal 27(1): 57-75, 2007) explored the efficacy of the Theory of Reasoned Action (TRA; Ajzen and Fishbein, Attitudes, personality, and behavior, 1980) in explaining gambling behavior of college students. However, their study found the TRA only predicted small amounts of variance in gambling intentions. Heeding their call to enhance the efficacy of the TRA through the addition of explanatory variables to the model, the present study incorporated gambling motivations and locus of control as moderating variables within the TRA to test the potential of a modified TRA in explaining gambling behavior of college students. A total of 345 students at a major metropolitan research university in the Midwest volunteered to participate in the study. A series of hierarchical linear regressions indicated intrinsic motivation to accomplish (p = .002) significantly moderated the relationship between gambling attitudes and gambling intentions. Further, internal locus of control (p < .001), chance locus of control (p < .001), and powerful others locus of control (p < .001) also significantly moderated the relationship between gambling attitudes and gambling intentions. The significant impact of the moderating variables on the relationship between gambling attitudes and intentions suggests intrinsic motivation and locus of control can alter the impact of the relationship between gambling attitudes and gambling intentions.
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Souza, Ricardo Alexandre de; Oliveira, Cláudia Di Lorenzo; Lima-Costa, Maria Fernanda; Proietti, Fernando Augusto
2014-01-01
The objective of this study was to examine the association between individual satisfaction with social and physical surroundings and the habit of smoking cigarettes. Data from the Health Survey of Adults from the metropolitan area of Belo Horizonte, Minas Gerais, Brazil, were used. Based on a probability sample, participants (n = 12,299) were selected among residents aged 20 years old or more. The response variable was the smoking habit and the explanatory variable of interest was the neighborhood perception. Potential confounding variables included demographic characteristics, health behaviors and other indicators of socioeconomic position. The prevalence of current smokers, former smokers and never smokers were 20.8, 14.1 and 65.1%, respectively; 74.4 and 25.5% of the participants were categorized as being more satisfied and less satisfied with the neighborhood, respectively. Compared to those who never smoked, former smokers (adjusted odds ratio = 1.40, 95% confidence interval 1.20 - 1.62) and current smokers (adjusted odds ratio = 1.17, 95% confidence interval 1.03 - 1.34) were less satisfied with the neighborhood compared to those who never smoked. The results of this study indicate there is an independent association between the smoking habit and a less satisfying neighborhood perception in the metropolitan region of Belo Horizonte, which does not depend on individual characteristics, traditionally reported as being associated with smoking.
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Wheelock, Ana; Miraldo, Marisa; Thomson, Angus; Vincent, Charles; Sevdalis, Nick
2017-01-01
Objectives Despite continuous efforts to improve influenza vaccination coverage, uptake among high-risk groups remains suboptimal. We aimed to identify policy amenable factors associated with vaccination and to measure their importance in order to assist in the monitoring of vaccination sentiment and the design of communication strategies and interventions to improve vaccination rates. Setting The USA, the UK and France. Participants A total of 2412 participants were surveyed across the three countries. Outcome measures Self-reported influenza vaccination. Methods Between March and April 2014, a stratified random sampling strategy was employed with the aim of obtaining nationally representative samples in the USA, the UK and France through online databases and random-digit dialling. Participants were asked about vaccination practices, perceptions and feelings. Multivariable logistic regression was used to identify factors associated with past influenza vaccination. Results The models were able to explain 64%–80% of the variance in vaccination behaviour. Overall, sociopsychological variables, which are inherently amenable to policy, were better at explaining past vaccination behaviour than demographic, socioeconomic and health variables. Explanatory variables included social influence (physician), influenza and vaccine risk perceptions and traumatic childhood experiences. Conclusions Our results indicate that evidence-based sociopsychological items should be considered for inclusion into national immunisation surveys to gauge the public’s views, identify emerging concerns and thus proactively and opportunely address potential barriers and harness vaccination drivers. PMID:28706088
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Determinants of virtual water flows in the Mediterranean.
Fracasso, Andrea; Sartori, Martina; Schiavo, Stefano
2016-02-01
The aim of the paper is to investigate the main determinants of the bilateral virtual water (water used in the production of a commodity or service) flows associated with international trade in agricultural goods across the Mediterranean basin. We consider the bilateral gross flows of virtual water in the area and study what export-specific and import-specific factors are significantly associated with virtual water flows. We follow a sequential approach. Through a gravity model of trade, we obtain a "refined" version of the variable we aim to explain, one that is free of the amount of flows due to pair-specific factors affecting bilateral trade flows and that fully reflects the impact of country-specific determinants of virtual water trade. A number of country-specific potential explanatory variables, ranging from water endowments to trade barriers, from per capita GDP to irrigation prices, is presented and tested. To identify the variables that help to explain the bilateral flows of virtual water, we adopt a model selection procedure based on model averaging. Our findings confirm one of the main controversial results in the literature: larger water endowments do not necessarily lead to a larger 'export' of virtual water, as one could expect. We also find some evidence that higher water irrigation prices reduce (increase) virtual water 'exports' ('imports'). Copyright © 2015 Elsevier B.V. All rights reserved.
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Kitsao-Wekulo, Patricia; Holding, Penny; Taylor, H. Gerry; Abubakar, Amina; Kvalsvig, Jane; Connolly, Kevin
2013-01-01
Adequate nutrition is fundamental to the development of a child's full potential. However, the extent to which malnutrition affects developmental and cognitive outcomes in the midst of co-occurring risk factors remains largely understudied. We sought to establish if the effects of nutritional status varied according to diverse background characteristics as well as to compare the relative strength of the effects of poor nutritional status on language skills, motor abilities, and cognitive functioning at school age. This cross-sectional study was conducted among school-age boys and girls resident in Kilifi District in Kenya. We hypothesized that the effects of area of residence, school attendance, household wealth, age and gender on child outcomes are experienced directly and indirectly through child nutritional status. The use of structural equation modeling (SEM) allowed the disaggregation of the total effect of the explanatory variables into direct effects (effects that go directly from one variable to another) and indirect effects. Each of the models tested for the four child outcomes had a good fit. However, the effects on verbal memory apart from being weaker than for the other outcomes, were not mediated through nutritional status. School attendance was the most influential predictor of nutritional status and child outcomes. The estimated models demonstrated the continued importance of child nutritional status at school-age. PMID:24298246
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McClurg, Doreen; Bugge, Carol; Elders, Andrew; Irshad, Tasneem; Hagen, Suzanne; Moore, Katherine N; Buckley, Brian; Fader, Mandy
2018-04-01
Clean intermittent catheterisation (CIC) is often recommended for people with multiple sclerosis (MS). To determine the variables that affect continuation or discontinuation of the use of CIC. A three-part mixed-method study (prospective longitudinal cohort ( n = 56), longitudinal qualitative interviews ( n = 20) and retrospective survey ( n = 456)) was undertaken, which identified the variables that influenced CIC continuation/discontinuation. The potential explanatory variables investigated in each study were the individual's age, gender, social circumstances, number of urinary tract infections, bladder symptoms, presence of co-morbidity, stage of multiple sclerosis and years since diagnosis, as well as CIC teaching method and intensity. For some people with MS the prospect of undertaking CIC is difficult and may take a period of time to accept before beginning the process of using CIC. Ongoing support from clinicians, support at home and a perceived improvement in symptoms such as nocturia were positive predictors of continuation. In many cases, the development of a urinary tract infection during the early stages of CIC use had a significant detrimental impact on continuation. Procedures for reducing the incidence of urinary tract infection during the learning period (i.e. when being taught and becoming competent) should be considered, as well as the development of a tool to aid identification of a person's readiness to try CIC.
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Estimating integrated variance in the presence of microstructure noise using linear regression
NASA Astrophysics Data System (ADS)
Holý, Vladimír
2017-07-01
Using financial high-frequency data for estimation of integrated variance of asset prices is beneficial but with increasing number of observations so-called microstructure noise occurs. This noise can significantly bias the realized variance estimator. We propose a method for estimation of the integrated variance robust to microstructure noise as well as for testing the presence of the noise. Our method utilizes linear regression in which realized variances estimated from different data subsamples act as dependent variable while the number of observations act as explanatory variable. We compare proposed estimator with other methods on simulated data for several microstructure noise structures.
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Mroz, T A
1999-10-01
This paper contains a Monte Carlo evaluation of estimators used to control for endogeneity of dummy explanatory variables in continuous outcome regression models. When the true model has bivariate normal disturbances, estimators using discrete factor approximations compare favorably to efficient estimators in terms of precision and bias; these approximation estimators dominate all the other estimators examined when the disturbances are non-normal. The experiments also indicate that one should liberally add points of support to the discrete factor distribution. The paper concludes with an application of the discrete factor approximation to the estimation of the impact of marriage on wages.
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Cooper, K M; Barry, J
2017-09-29
In this study we produce a standardised dataset for benthic macrofauna and sediments through integration of data (33,198 samples) from 777 grab surveys. The resulting dataset is used to identify spatial and temporal patterns in faunal distribution around the UK, and the role of sediment composition and other explanatory variables in determining such patterns. We show how insight into natural variability afforded by the dataset can be used to improve the sustainability of activities which affect sediment composition, by identifying conditions which should remain favourable for faunal recolonisation. Other big data applications and uses of the dataset are discussed.
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Rates and predictors of depression in adoptive mothers: moving toward theory.
Foli, Karen J; South, Susan C; Lim, Eunjung
2012-01-01
There are approximately 1.8 million adopted children living in the United States. Adoptive parents may experience depressive symptoms and put their children at risk for negative outcomes. The results of this study describe the rates of depression in 300 adoptive mothers and associations with hypothesized explanatory variables, which predict approximately half of the variance in maternal depressive symptoms: expectations of themselves as mothers, the child, and family and friends; feeling of rest; past and present psychiatric difficulties (self-esteem, history of depression); and interpersonal variables (bonding, marital satisfaction, perceived support). These findings are useful in planning effective interventions to mitigate depressive symptoms.
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Learning investment indicators through data extension
NASA Astrophysics Data System (ADS)
Dvořák, Marek
2017-07-01
Stock prices in the form of time series were analysed using single and multivariate statistical methods. After simple data preprocessing in the form of logarithmic differences, we augmented this single variate time series to a multivariate representation. This method makes use of sliding windows to calculate several dozen of new variables using simple statistic tools like first and second moments as well as more complicated statistic, like auto-regression coefficients and residual analysis, followed by an optional quadratic transformation that was further used for data extension. These were used as a explanatory variables in a regularized logistic LASSO regression which tried to estimate Buy-Sell Index (BSI) from real stock market data.
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Morgan, Wayne J; VanDevanter, Donald R; Pasta, David J; Foreman, Aimee J; Wagener, Jeffrey S; Konstan, Michael W
2016-02-01
To evaluate several alternative measures of forced expiratory volume in 1 second percent predicted (FEV1 %pred) variability as potential predictors of future FEV1 %pred decline in patients with cystic fibrosis. We included 13,827 patients age ≥6 years from the Epidemiologic Study of Cystic Fibrosis 1994-2002 with ≥4 FEV1 %pred measurements spanning ≥366 days in both a 2-year baseline period and a 2-year follow-up period. We predicted change from best baseline FEV1 %pred to best follow-up FEV1 %pred and change from baseline to best in the second follow-up year by using multivariable regression stratified by 4 lung-disease stages. We assessed 5 measures of variability (some as deviations from the best and some as deviations from the trend line) both alone and after controlling for demographic and clinical factors and for the slope and level of FEV1 %pred. All 5 measures of FEV1 %pred variability were predictive, but the strongest predictor was median deviation from the best FEV1 %pred in the baseline period. The contribution to explanatory power (R(2)) was substantial and exceeded the total contribution of all other factors excluding the FEV1 %pred rate of decline. Adding the other variability measures provided minimal additional value. Median deviation from the best FEV1 %pred is a simple metric that markedly improves prediction of FEV1 %pred decline even after the inclusion of demographic and clinical characteristics and the FEV1 %pred rate of decline. The routine calculation of this variability measure could allow clinicians to better identify patients at risk and therefore in need of increased intervention. Copyright © 2016 Elsevier Inc. All rights reserved.
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Rule, Michael E.; Vargas-Irwin, Carlos; Donoghue, John P.; Truccolo, Wilson
2015-01-01
Understanding the sources of variability in single-neuron spiking responses is an important open problem for the theory of neural coding. This variability is thought to result primarily from spontaneous collective dynamics in neuronal networks. Here, we investigate how well collective dynamics reflected in motor cortex local field potentials (LFPs) can account for spiking variability during motor behavior. Neural activity was recorded via microelectrode arrays implanted in ventral and dorsal premotor and primary motor cortices of non-human primates performing naturalistic 3-D reaching and grasping actions. Point process models were used to quantify how well LFP features accounted for spiking variability not explained by the measured 3-D reach and grasp kinematics. LFP features included the instantaneous magnitude, phase and analytic-signal components of narrow band-pass filtered (δ,θ,α,β) LFPs, and analytic signal and amplitude envelope features in higher-frequency bands. Multiband LFP features predicted single-neuron spiking (1ms resolution) with substantial accuracy as assessed via ROC analysis. Notably, however, models including both LFP and kinematics features displayed marginal improvement over kinematics-only models. Furthermore, the small predictive information added by LFP features to kinematic models was redundant to information available in fast-timescale (<100 ms) spiking history. Overall, information in multiband LFP features, although predictive of single-neuron spiking during movement execution, was redundant to information available in movement parameters and spiking history. Our findings suggest that, during movement execution, collective dynamics reflected in motor cortex LFPs primarily relate to sensorimotor processes directly controlling movement output, adding little explanatory power to variability not accounted by movement parameters. PMID:26157365
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Pichetti, S; Penneau, A; Lengagne, P; Sermet, C
2016-04-01
Using data from the 2008 French health and disabilities households surveys, this study examines the use of three types of routine medical care (dental, ophthalmological and gynecological care) and four preventive services (cervical cancer screening, breast cancer screening, colon cancer screening and vaccination against hepatitis B) both for people with disabilities and for those without. Two definitions of disability were retained: (1) functional limitations (motor, cognitive, visual or hearing limitations) and (2) administrative recognition of disability. For each type of care, binary logistic regression was used to test whether access to care is influenced by any of the disability indicators as well as by other explanatory variables. Two set of explanatory variables were included successively: (1) sociodemographic variables such as age, gender as well as a proxy variable representing medical needs and (2) socioeconomic variables such as level of education, household income per consumption unit, supplementary health insurance coverage, co-payment exemption and geographic variables. Persons reporting functional limitations are less likely to access to all types of care, in a proportion that varies between 5 to 27 points, compared to persons without functional limitations, except for eye care for which no gap is observed. The same results are obtained for persons reporting an administrative recognition of disability, and more precisely for those who benefit from the Disability allowance for adults (Allocation adulte handicapé [AAH]). After adding the social variables to the model, problems of access to health care decrease significantly, showing that disabled persons' social situation tends to reduce their access to care. This study reveals, for a broad range of care, a negative differential access to care for persons reporting functional limitations compared to those without limitations which is confirmed when identifying disability through administrative recognition. Furthermore, it also discusses factors explaining these differentials. It highlights the role of the social situation of disabled people as an additional barrier to already limited access to healthcare. Copyright © 2016 Elsevier Masson SAS. All rights reserved.
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1981-01-01
explanatory variable has been ommitted. Ramsey (1974) has developed a rather interesting test for detecting specification errors using estimates of the...Peter. (1979) A Guide to Econometrics , Cambridge, MA: The MIT Press. Ramsey , J.B. (1974), "Classical Model Selection Through Specification Error... Tests ," in P. Zarembka, Ed. Frontiers in Econometrics , New York: Academia Press. Theil, Henri. (1971), Principles of Econometrics , New York: John Wiley
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12 CFR Appendix A to Subpart A of... - Method to Derive Pricing Multipliers and Uniform Amount
Code of Federal Regulations, 2012 CFR
2012-01-01
... explanatory variables (regressors) in the model are six financial ratios and a weighted average of the “C,” “A,” “M,” “E” and “L” component ratings. The six financial ratios included in the model are: • Tier 1... downgraded to 3 or worse within 3 to 12 months from time T. The risk measures are financial ratios as defined...
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12 CFR Appendix A to Subpart A of... - Method to Derive Pricing Multipliers and Uniform Amount
Code of Federal Regulations, 2014 CFR
2014-01-01
... explanatory variables (regressors) in the model are six financial ratios and a weighted average of the “C,” “A,” “M,” “E” and “L” component ratings. The six financial ratios included in the model are: • Tier 1... downgraded to 3 or worse within 3 to 12 months from time T. The risk measures are financial ratios as defined...
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12 CFR Appendix A to Subpart A of... - Method to Derive Pricing Multipliers and Uniform Amount
Code of Federal Regulations, 2013 CFR
2013-01-01
... explanatory variables (regressors) in the model are six financial ratios and a weighted average of the “C,” “A,” “M,” “E” and “L” component ratings. The six financial ratios included in the model are: • Tier 1... downgraded to 3 or worse within 3 to 12 months from time T. The risk measures are financial ratios as defined...
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The Impact of Pay on Navy Physician Retention in a Health Care Reform Environment.
1998-03-01
Analyses 33 6. Generalist Physician Supply Under Alternative Specialty Mix 33 7. Specialist Physician Supply Under Alternative Specialty Mix 34 8...Fiscal Years 1992 to 1997 40 5. Comparison of proposed alternative pay plans 54 6. Evaluation of expected outcomes of alternative pay plans 55 7...Explanatory variables with the expected sign of regression coefficient 76 8. Comparison of Navy physicians’ pay to alternative civilian pay for selected
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2004-03-01
predicting future events ( Heizer and Render , 1999). Forecasting techniques fall into two major categories, qualitative and quantitative methods...Globemaster III.” Excerpt from website. www.globalsecurity.org/military /systems/ aircraft/c-17-history.htm. 2003. Heizer , Jay, and Barry Render ...of the past data used to make the forecast ( Heizer , et. al., 1999). Explanatory forecasting models assume that the variable being forecasted
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Psychosocial Factors Influencing Smokeless Tobacco Use by Teenage Military Dependents
1994-01-01
age and grade Demographics both show strong bivariate relationships with the outcome MALE-male gender measures, we selected to use only one of these...a positive sign indicating a direct relation- no impact for either gender . Attitude and other tobacco influ- ship and a negative sign an inverse...by both genders . nlficant. However, if the interval includes one but is highly For both genders , the strongest explanatory variable for trial skewed
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Brooks, Mollie E; Mugabo, Marianne; Rodgers, Gwendolen M; Benton, Timothy G; Ozgul, Arpat
2016-03-01
Demographic rates are shaped by the interaction of past and current environments that individuals in a population experience. Past environments shape individual states via selection and plasticity, and fitness-related traits (e.g. individual size) are commonly used in demographic analyses to represent the effect of past environments on demographic rates. We quantified how well the size of individuals captures the effects of a population's past and current environments on demographic rates in a well-studied experimental system of soil mites. We decomposed these interrelated sources of variation with a novel method of multiple regression that is useful for understanding nonlinear relationships between responses and multicollinear explanatory variables. We graphically present the results using area-proportional Venn diagrams. Our novel method was developed by combining existing methods and expanding upon them. We showed that the strength of size as a proxy for the past environment varied widely among vital rates. For instance, in this organism with an income breeding life history, the environment had more effect on reproduction than individual size, but with substantial overlap indicating that size encompassed some of the effects of the past environment on fecundity. This demonstrates that the strength of size as a proxy for the past environment can vary widely among life-history processes within a species, and this variation should be taken into consideration in trait-based demographic or individual-based approaches that focus on phenotypic traits as state variables. Furthermore, the strength of a proxy will depend on what state variable(s) and what demographic rate is being examined; that is, different measures of body size (e.g. length, volume, mass, fat stores) will be better or worse proxies for various life-history processes. © 2016 The Authors. Journal of Animal Ecology © 2016 British Ecological Society.
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Logistic regression modeling to assess groundwater vulnerability to contamination in Hawaii, USA.
Mair, Alan; El-Kadi, Aly I
2013-10-01
Capture zone analysis combined with a subjective susceptibility index is currently used in Hawaii to assess vulnerability to contamination of drinking water sources derived from groundwater. In this study, we developed an alternative objective approach that combines well capture zones with multiple-variable logistic regression (LR) modeling and applied it to the highly-utilized Pearl Harbor and Honolulu aquifers on the island of Oahu, Hawaii. Input for the LR models utilized explanatory variables based on hydrogeology, land use, and well geometry/location. A suite of 11 target contaminants detected in the region, including elevated nitrate (>1 mg/L), four chlorinated solvents, four agricultural fumigants, and two pesticides, was used to develop the models. We then tested the ability of the new approach to accurately separate groups of wells with low and high vulnerability, and the suitability of nitrate as an indicator of other types of contamination. Our results produced contaminant-specific LR models that accurately identified groups of wells with the lowest/highest reported detections and the lowest/highest nitrate concentrations. Current and former agricultural land uses were identified as significant explanatory variables for eight of the 11 target contaminants, while elevated nitrate was a significant variable for five contaminants. The utility of the combined approach is contingent on the availability of hydrologic and chemical monitoring data for calibrating groundwater and LR models. Application of the approach using a reference site with sufficient data could help identify key variables in areas with similar hydrogeology and land use but limited data. In addition, elevated nitrate may also be a suitable indicator of groundwater contamination in areas with limited data. The objective LR modeling approach developed in this study is flexible enough to address a wide range of contaminants and represents a suitable addition to the current subjective approach. © 2013 Elsevier B.V. All rights reserved.
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NASA Astrophysics Data System (ADS)
Houborg, Rasmus; McCabe, Matthew F.
2018-01-01
With an increasing volume and dimensionality of Earth observation data, enhanced integration of machine-learning methodologies is needed to effectively analyze and utilize these information rich datasets. In machine-learning, a training dataset is required to establish explicit associations between a suite of explanatory 'predictor' variables and the target property. The specifics of this learning process can significantly influence model validity and portability, with a higher generalization level expected with an increasing number of observable conditions being reflected in the training dataset. Here we propose a hybrid training approach for leaf area index (LAI) estimation, which harnesses synergistic attributes of scattered in-situ measurements and systematically distributed physically based model inversion results to enhance the information content and spatial representativeness of the training data. To do this, a complimentary training dataset of independent LAI was derived from a regularized model inversion of RapidEye surface reflectances and subsequently used to guide the development of LAI regression models via Cubist and random forests (RF) decision tree methods. The application of the hybrid training approach to a broad set of Landsat 8 vegetation index (VI) predictor variables resulted in significantly improved LAI prediction accuracies and spatial consistencies, relative to results relying on in-situ measurements alone for model training. In comparing the prediction capacity and portability of the two machine-learning algorithms, a pair of relatively simple multi-variate regression models established by Cubist performed best, with an overall relative mean absolute deviation (rMAD) of ∼11%, determined based on a stringent scene-specific cross-validation approach. In comparison, the portability of RF regression models was less effective (i.e., an overall rMAD of ∼15%), which was attributed partly to model saturation at high LAI in association with inherent extrapolation and transferability limitations. Explanatory VIs formed from bands in the near-infrared (NIR) and shortwave infrared domains (e.g., NDWI) were associated with the highest predictive ability, whereas Cubist models relying entirely on VIs based on NIR and red band combinations (e.g., NDVI) were associated with comparatively high uncertainties (i.e., rMAD ∼ 21%). The most transferable and best performing models were based on combinations of several predictor variables, which included both NDWI- and NDVI-like variables. In this process, prior screening of input VIs based on an assessment of variable relevance served as an effective mechanism for optimizing prediction accuracies from both Cubist and RF. While this study demonstrated benefit in combining data mining operations with physically based constraints via a hybrid training approach, the concept of transferability and portability warrants further investigations in order to realize the full potential of emerging machine-learning techniques for regression purposes.
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A Mixed-Method Analysis of Reports on 100 Cases of Improper Prescribing of Controlled Substances
DuBois, James M.; Chibnall, John T.; Anderson, Emily E.; Eggers, Michelle; Baldwin, Kari; Vasher, Meghan
2017-01-01
Improper prescribing of controlled substances contributes to opioid addictions and deaths by overdose. Studies conducted to-date have largely lacked a theoretical framework and ignored the interaction of individual with environmental factors. We conducted a mixed-method analysis of published reports on 100 cases that occurred in the United States. An average of 17 reports (e.g., from medical boards) per case were coded for 38 dichotomous variables describing the physician, setting, patients, and investigation. A theory on how the case occurred was developed for each case. Explanatory typologies were developed and then validated through hierarchical cluster analysis. Most cases involved physicians who were male (88%), >40 years old (90%), non-board certified (63%), and in small private practices (97%); 54% of cases reported facts about the physician indicative of self-centered personality traits. Three explanatory typologies were validated. Increasing oversight provided by peers and trainees may help prevent improper prescribing of controlled substances. PMID:28663601
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The Importance of Field in Understanding Persistence among Science and Engineering Majors
NASA Astrophysics Data System (ADS)
Wyer, Mary
Many contemporary studies of persistence in science and engineering majors find that students' gender is an important explanatory variable in understanding why students leave and stay in their majors. This study revisits an earlier report by this author to explore the relative impact of gender on persistence when field of major is held constant, using the same data as the earlier work, drawing on survey responses from 285 students in required biology and engineering courses. The author argues two points: that effects of field on students' persistence are often misinterpreted as gender effects and that gender is too often understood in the narrow sense of students' sex. The author finds that when field is taken into account, students' sex has little explanatory power for understanding persistence rates. In contrast, students' positive experiences with and positive attitudes about gender equity significantly enhance commitments to science and engineering majors, advanced degrees, and careers. The results suggest the need for field-specific interventions to promote the advancement of undergraduate women in science and engineering.
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Why the Difference Between Explanation and Argument Matters to Science Education
NASA Astrophysics Data System (ADS)
Brigandt, Ingo
2016-05-01
Contributing to the recent debate on whether or not explanations ought to be differentiated from arguments, this article argues that the distinction matters to science education. I articulate the distinction in terms of explanations and arguments having to meet different standards of adequacy. Standards of explanatory adequacy are important because they correspond to what counts as a good explanation in a science classroom, whereas a focus on evidence-based argumentation can obscure such standards of what makes an explanation explanatory. I provide further reasons for the relevance of not conflating explanations with arguments (and having standards of explanatory adequacy in view). First, what guides the adoption of the particular standards of explanatory adequacy that are relevant in a scientific case is the explanatory aim pursued in this context. Apart from explanatory aims being an important aspect of the nature of science, including explanatory aims in classroom instruction also promotes students seeing explanations as more than facts, and engages them in developing explanations as responses to interesting explanatory problems. Second, it is of relevance to science curricula that science aims at intervening in natural processes, not only for technological applications, but also as part of experimental discovery. Not any argument enables intervention in nature, as successful intervention specifically presupposes causal explanations. Students can fruitfully explore in the classroom how an explanatory account suggests different options for intervention.
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pH regulates ammonia-oxidizing bacteria and archaea in paddy soils in Southern China.
Li, Hu; Weng, Bo-Sen; Huang, Fu-Yi; Su, Jian-Qiang; Yang, Xiao-Ru
2015-07-01
Ammonia-oxidizing archaea (AOA) and bacteria (AOB) play important roles in nitrogen cycling. However, the effects of environmental factors on the activity, abundance, and diversity of AOA and AOB and the relative contributions of these two groups to nitrification in paddy soils are not well explained. In this study, potential nitrification activity (PNA), abundance, and diversity of amoA genes from 12 paddy soils in Southern China were determined by potential nitrification assay, quantitative PCR, and cloning. The results showed that PNA was highly variable between paddy soils, ranging from 4.05 ± 0.21 to 9.81 ± 1.09 mg NOx-N kg(-1) dry soil day(-1), and no significant correlation with soil parameters was found. The abundance of AOA was predominant over AOB, indicating that AOA may be the major members in aerobic ammonia oxidation in these paddy soils. Community compositions of AOA and AOB were highly variable among samples, but the variations were best explained by pH. AOA sequences were affiliated to the Nitrosopumilus cluster and Nitrososphaera cluster, and AOB were classified into the lineages of Nitrosospira and Nitrosomonas, with Nitrosospira being predominant over Nitrosomonas, accounting for 83.6 % of the AOB community. Moreover, the majority of Nitrosomonas was determined in neutral soils. Canonical correspondence analysis (CCA) analysis further demonstrated that AOA and AOB community structures were significantly affected by pH, soil total organic carbon, total nitrogen, and C/N ratio, suggesting that these factors exert strong effects on the distribution of AOB and AOA in paddy soils in Southern China. In conclusion, our results imply that soil pH was a key explanatory variable for both AOA and AOB community structure and nitrification activity.
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Benthic algae of benchmark streams in agricultural areas of eastern Wisconsin
Scudder, Barbara C.; Stewart, Jana S.
2001-01-01
Multivariate analyses indicated multiple scales of environmental factors affect algae. Although two-way indicator species analysis (TWINSPAN), detrended correspondence analysis (DCA), and canonical correspondence analysis (CCA) generally separated sites according to RHU, only DCA ordination indicated a separation of sites according to ecoregion. Environmental variables con-elated with DCA axes 1 and 2 and therefore indicated as important explanatory factors for algal distribution and abundance were factors related to stream size, basin land use/cover, geomorphology, hydrogeology, and riparian disturbance. CCA analyses with a more limited set of environmental variables indicated that pH, average width of natural riparian vegetation (segment scale), basin land use/cover and Q/Q2 were the most important variables affecting the distribution and relative abundance of benthic algae at the 20 benchmark streams,
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Williams, Chyvette T; Grier, Sonya A; Marks, Amy Seidel
2008-07-01
This study was conducted to examine the effect of urban living on smoking attitudes among black African women in South Africa. We examine how urbanicity affects attitudes toward smoking and how it moderates the relationship between both advertising exposure and network norms on black women's smoking attitudes. Respondents were 975 black women currently living in Cape Town townships, some of which were raised in rural villages or small towns. Respondents completed a cross-sectional survey, which included data on smoking attitudes, norms, and exposure to cigarette advertising. Multiple linear regression analysis was performed with smoking attitudes as the response variable, and urbanicity, cigarette advertising exposure, and network smoking norms as primary explanatory variables. Interactions were tested to determine whether urbanicity modified the effect of advertising exposure and network norms on smoking attitudes. Independent effects of urbanicity, exposure to cigarette advertising, and greater smoking prevalence within women's networks were associated with more favorable smoking attitudes. In addition, urbanicity moderated the relationship between network smoking norms and smoking attitudes, but not cigarette advertising exposure and smoking attitudes. Urbanicity, cigarette advertising, and networks play important roles in women's attitudes toward smoking, and potentially, smoking behavior. Overall, our results suggest that strong and creative anti-smoking efforts are needed to combat the potential for a smoking epidemic among an increasingly urbanized population of black women in South Africa and similar emerging markets. Additional research is warranted.
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The upper bound of Pier Scour defined by selected laboratory and field data
Benedict, Stephen; Caldwell, Andral W.
2015-01-01
The U.S. Geological Survey, in cooperation with the South Carolina Department of Transportation, conducted several field investigations of pier scour in South Carolina (Benedict and Caldwell, 2006; Benedict and Caldwell, 2009) and used that data to develop envelope curves defining the upper bound of pier scour. To expand upon this previous work, an additional cooperative investigation was initiated to combine the South Carolina data with pier-scour data from other sources and evaluate the upper bound of pier scour with this larger data set. To facilitate this analysis, a literature review was made to identify potential sources of published pier-scour data, and selected data were compiled into a digital spreadsheet consisting of approximately 570 laboratory and 1,880 field measurements. These data encompass a wide range of laboratory and field conditions and represent field data from 24 states within the United States and six other countries. This extensive database was used to define the upper bound of pier-scour depth with respect to pier width encompassing the laboratory and field data. Pier width is a primary variable that influences pier-scour depth (Laursen and Toch, 1956; Melville and Coleman, 2000; Mueller and Wagner, 2005, Ettema et al. 2011, Arneson et al. 2012) and therefore, was used as the primary explanatory variable in developing the upper-bound envelope curve. The envelope curve provides a simple but useful tool for assessing the potential maximum pier-scour depth for pier widths of about 30 feet or less.
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Williams, Chyvette T.; Marks, Amy Seidel
2008-01-01
This study was conducted to examine the effect of urban living on smoking attitudes among black African women in South Africa. We examine how urbanicity affects attitudes toward smoking and how it moderates the relationship between both advertising exposure and network norms on black women’s smoking attitudes. Respondents were 975 black women currently living in Cape Town townships, some of which were raised in rural villages or small towns. Respondents completed a cross-sectional survey, which included data on smoking attitudes, norms, and exposure to cigarette advertising. Multiple linear regression analysis was performed with smoking attitudes as the response variable, and urbanicity, cigarette advertising exposure, and network smoking norms as primary explanatory variables. Interactions were tested to determine whether urbanicity modified the effect of advertising exposure and network norms on smoking attitudes. Independent effects of urbanicity, exposure to cigarette advertising, and greater smoking prevalence within women’s networks were associated with more favorable smoking attitudes. In addition, urbanicity moderated the relationship between network smoking norms and smoking attitudes, but not cigarette advertising exposure and smoking attitudes. Urbanicity, cigarette advertising, and networks play important roles in women’s attitudes toward smoking, and potentially, smoking behavior. Overall, our results suggest that strong and creative anti-smoking efforts are needed to combat the potential for a smoking epidemic among an increasingly urbanized population of black women in South Africa and similar emerging markets. Additional research is warranted. PMID:18563573
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A Social Domain Approach to Informant Discrepancies in Parental Solicitation and Family Rules.
Metzger, Aaron; Babskie, Elizabeth; Olson, Rebecca; Romm, Katelyn
2016-10-01
An extensive body of research has explored the effects of parental monitoring on adolescent outcomes, but studies consistently find substantial discrepancies between parent and adolescent reports of different monitoring behaviors. Little research has examined whether parents and adolescents are more or less discrepant when reporting on parents' rules or solicitation for different adolescent problem and health risk behaviors and few studies have explored potential explanatory variables to explicate individual variability in parent-adolescent discrepant reporting. To address this gap in the literature, the current study examined discrepancies in mother-adolescent reports of family rules and solicitation across five distinct adolescent behaviors: personal behaviors and four different risk behaviors (alcohol-related, cyber, over- and under-eating). Participants were 143 mother-adolescent dyads (Adolescent M age = 14.42, SD = 1.73, range = 12-18, 81 % white, 60 % female). Mean-level discrepancies between maternal and adolescent reports significantly differed by category of adolescent behavior and also varied as a function of reported parental monitoring behavior (rules vs. solicitation). Discrepancies in mother-adolescent reports of behavior-specific rules and solicitation were positively associated with discrepancies in mother and adolescent judgments of the harmfulness of the activities. The results demonstrate that discrepancies in mother-adolescent reports of family process differ by category of adolescent behavior and may be undergirded by differences in mother and adolescent informational assumptions about the potential harm involved with different activities.
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Zocalo, Yanina; Castro, Juan M; Garcia-Espinosa, Victoria; Curcio, Santiago; Chiesa, Pedro; Giachetto, Gustavo; Cabrera-Fischer, Edmundo I; Bia, Daniel
2018-04-12
High blood pressure states (HBP) would differ in wave components and reflections indexes, which could associate clinical and prognostic implications. 1) to characterize the association of aortic wave components and reflection parameters (backward [Pb], forward [Pf], Pb/Pf ratio and augmentation index [AIx]) with demographic, anthropometric, hemodynamic and arterial parameters in healthy children and adolescents; 2) to generate multivariate prediction models for the associations, to contribute to understand main determinants of Pf, Pb, Pb/Pf and AIx; 3) to identify if differences in wave reflection indexes observed in HBP could be explained by differences in the analyzed parameters. Healthy children and adolescents (n=816, females: 386; Age: 3-20 years) were studied. central aortic pressure and wave components (Pb, Pf, Pb/Pf and AIx determination with SphygmoCor [SCOR] and Mobil-o-Graph [MOG]); anthropometric assessment; regional arterial stiffness (carotid-femoral, carotid-radial pulse wave velocity [PWV] and PWV ratio); carotid intima-media thickness; carotid and femoral distensbility; cardiac output; systemic vascular resistances (SVR). Simple and multiple regression models were constructed to determine aortic wave parameters main explanatory variables. Normotensive and HBP groups were compared. Differences in wave reflection indexes were analyzed before and after controlling for explanatory variables. Equivalences between SphygmoCor and Mobil-O-Graph data were assessed (correlation and Bland-Altman analyses). There were systematic and proportional differences between data obtained with SphygmoCor and Mobil-O-Graph devices. Heart rate (HR), peripheral pulse pressure, height and weight were the variables that isolated (simple associations) or combined (multiple associations) showed the major capability to explain interindividual differences in Pf, Pb, Pb/Pf and AIx. Arterial stiffness also showed explanatory capacity, being the carotid the artery with major contribution. HBP associated higher Pf, Pb, AIx and lower Pb/Pf ratio. Those findings were observed together with higher weight, arterial stiffness and HR. After adjusting for anthropometric characteristics, HR, cardiac output and SVR, the HBP group showed greater Pf and Pb. Then, Pf and Pb characteristics associated with HBP would not be explained by anthropometric or hemodynamic factors. To evaluate wave components and reflection parameters could contribute to improve comprehension and management of HBP states. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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Improved algorithms for estimating Total Alkalinity in Northern Gulf of Mexico
NASA Astrophysics Data System (ADS)
Devkota, M.; Dash, P.
2017-12-01
Ocean Acidification (OA) is one of the serious challenges that have significant impacts on ocean. About 25% of anthropologically generated CO2 is absorbed by the oceans which decreases average ocean pH. This change has critical impacts on marine species, ocean ecology, and associated economics. 35 years of observation concluded that the rate of alteration in OA parameters varies geographically with higher variations in the northern Gulf of Mexico (N-GoM). Several studies have suggested that the Mississippi River affects the carbon dynamics of the N-GoM coastal ecosystem significantly. Total Alkalinity (TA) algorithms developed for major ocean basins produce inaccurate estimations in this region. Hence, a local algorithm to estimate TA is the need for this region, which would incorporate the local effects of oceanographic processes and complex spatial influences. In situ data collected in N-GoM region during the GOMECC-I and II cruises, and GISR Cruises (G-1, 3, 5) from 2007 to 2013 were assimilated and used to calculate the efficiency of the existing TA algorithm that uses Sea Surface Temperature (SST) and Sea Surface Salinity (SSS) as explanatory variables. To improve this algorithm, firstly, statistical analyses were performed to improve the coefficients and the functional form of this algorithm. Then, chlorophyll a (Chl-a) was included as an additional explanatory variable in the multiple linear regression approach in addition to SST and SSS. Based on the average concentration of Chl-a for last 15 years, the N-GoM was divided into two regions, and two separate algorithms were developed for each region. Finally, to address spatial non-stationarity, a Geographically Weighted Regression (GWR) algorithm was developed. The existing TA algorithm resulted considerable algorithm bias with a larger bias in the coastal waters. Chl-a as an additional explanatory variable reduced the bias in the residuals and improved the algorithm efficiency. Chl-a worked as a proxy for addressing the organic pump's pronounced effects in the coastal waters. The GWR algorithm provided a raster surface of the coefficients with even more reliable algorithms to estimate TA with least error. The GWR algorithm addressed the spatial non-stationarity of OA in N-GoM, which apparently was not addressed in the previously developed algorithms.
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Hinkley, Trina; Salmon, Jo; Okely, Anthony D; Crawford, David
2013-09-01
To investigate the individual, social and physical environment correlates of preschool children's compliance with Australian/Canadian and American Academy of Pediatrics (AAP) screen recommendations. An Ecological Model (EM) was used to identify constructs potentially associated with children's screen time. In 2008-2009, parents in Melbourne, Australia, reported their child's screen time and on a range of potential correlates. Children (n = 935; 54% boys, mean age 4.54 ± 0.70 years) were assessed as meeting or not meeting each of the screen recommendations. Logistic regression assessed bivariable and multivariable associations. In total, 15 explanatory variables, across the three domains of the EM were associated with boys' and/or girls' compliance with either Australian/Canadian or AAP recommendations. Correlates varied by sex and recommendation. Maternal television viewing time was the only consistent correlate for both boys' and girls' compliance with both recommendations. No demographic groups were identified as being less likely to comply with screen recommendations. Public health programs should take account of the sex-specific nature of correlates of preschool children's screen time. Preschool children across all demographic groups need support to engage in less screen use. Parents may benefit from education and parenting skills to minimize potentially harmful effects of excessive screen time for their child. Copyright © 2013 Elsevier Inc. All rights reserved.
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Psychosocial work dimensions, personality, and body mass index: sex differences.
Blanch, Angel; Aluja, Anton
2013-08-01
The association between psychosocial work dimensions (i.e. demand and control) and obesity has been found to be inconclusive, indicating that individual differences factors might also contribute to explain the variability in BMI. The interaction between work dimensions and personality variables in a group of male and female workers (N = 506), and its associations with BMI were analyzed with a cross-sectional study with self-report data. Hierarchical regression analyses were used to predict the BMI levels from work and individual differences variables and their interactions for males and females. The main effects of personality variables were not significant, physical workload interacted with neuroticism for males, whereas control interacted with activity for females. Psychosocial work dimensions and personality traits were related to BMI for men and women. These outcomes reinforce the notion that different models might account for the explanatory mechanisms of BMI in regard to sex.
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Nonparametric instrumental regression with non-convex constraints
NASA Astrophysics Data System (ADS)
Grasmair, M.; Scherzer, O.; Vanhems, A.
2013-03-01
This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, such as integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition.
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Variables affecting learning in a simulation experience: a mixed methods study.
Beischel, Kelly P
2013-02-01
The primary purpose of this study was to test a hypothesized model describing the direct effects of learning variables on anxiety and cognitive learning outcomes in a high-fidelity simulation (HFS) experience. The secondary purpose was to explain and explore student perceptions concerning the qualities and context of HFS affecting anxiety and learning. This study used a mixed methods quantitative-dominant explanatory design with concurrent qualitative data collection to examine variables affecting learning in undergraduate, beginning nursing students (N = 124). Being ready to learn, having a strong auditory-verbal learning style, and being prepared for simulation directly affected anxiety, whereas learning outcomes were directly affected by having strong auditory-verbal and hands-on learning styles. Anxiety did not quantitatively mediate cognitive learning outcomes as theorized, although students qualitatively reported debilitating levels of anxiety. This study advances nursing education science by providing evidence concerning variables affecting learning outcomes in HFS.
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Determining Directional Dependency in Causal Associations
Pornprasertmanit, Sunthud; Little, Todd D.
2014-01-01
Directional dependency is a method to determine the likely causal direction of effect between two variables. This article aims to critique and improve upon the use of directional dependency as a technique to infer causal associations. We comment on several issues raised by von Eye and DeShon (2012), including: encouraging the use of the signs of skewness and excessive kurtosis of both variables, discouraging the use of D’Agostino’s K2, and encouraging the use of directional dependency to compare variables only within time points. We offer improved steps for determining directional dependency that fix the problems we note. Next, we discuss how to integrate directional dependency into longitudinal data analysis with two variables. We also examine the accuracy of directional dependency evaluations when several regression assumptions are violated. Directional dependency can suggest the direction of a relation if (a) the regression error in population is normal, (b) an unobserved explanatory variable correlates with any variables equal to or less than .2, (c) a curvilinear relation between both variables is not strong (standardized regression coefficient ≤ .2), (d) there are no bivariate outliers, and (e) both variables are continuous. PMID:24683282
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Takahashi, Masazumi; Terashima, Masanori; Kawahira, Hiroshi; Nagai, Eishi; Uenosono, Yoshikazu; Kinami, Shinichi; Nagata, Yasuhiro; Yoshida, Masashi; Aoyagi, Keishiro; Kodera, Yasuhiro; Nakada, Koji
2017-01-01
AIM To investigate the detrimental impact of loss of reservoir capacity by comparing total gastrectomy (TGRY) and distal gastrectomy with the same Roux-en-Y (DGRY) reconstruction. The study was conducted using an integrated questionnaire, the Postgastrectomy Syndrome Assessment Scale (PGSAS)-45, recently developed by the Japan Postgastrectomy Syndrome Working Party. METHODS The PGSAS-45 comprises 8 items from the Short Form-8, 15 from the Gastrointestinal Symptom Rating Scale, and 22 newly selected items. Uni- and multivariate analysis was performed on 868 questionnaires completed by patients who underwent either TGRY (n = 393) or DGRY (n = 475) for stage I gastric cancer (52 institutions). Multivariate analysis weighed of six explanatory variables, including the type of gastrectomy (TGRY/DGRY), interval after surgery, age, gender, surgical approach (laparoscopic/open), and whether the celiac branch of the vagus nerve was preserved/divided on the quality of life (QOL). RESULTS The patients who underwent TGRY experienced the poorer QOL compared to DGRY in the 15 of 19 main outcome measures of PGSAS-45. Moreover, multiple regression analysis indicated that the type of gastrectomy, TGRY, most strongly and broadly impaired the postoperative QOL among six explanatory variables. CONCLUSION The results of the present study suggested that TGRY had a certain detrimental impact on the postoperative QOL, and the loss of reservoir capacity could be a major cause. PMID:28373774
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Physical activity and sedentary behaviour among inpatient adults with mental illness.
Fraser, Sarah J; Chapman, Justin J; Brown, Wendy J; Whiteford, Harvey A; Burton, Nicola W
2016-08-01
The aim of this study was to assess levels and patterns of physical activity and sedentary behaviour among inpatient adults with mental illness. Cross-sectional. 101 participants completed questionnaires on time spent in walking, moderate- and vigorous-intensity activity in the past week and domain specific sitting time on a usual weekday and weekend day. 36 participants also provided valid accelerometry data. Regression analyses were used to explore associations between MVPA and sedentary behaviour and explanatory variables of gender, age, education, body mass index and psychological distress. Self-report data indicated median of 32min/day (IQR: 14.46-85.71) in weighted MVPA and a median of 761min/day (12.7h) (IQR: 552.43-917.14) in sedentary behaviour. Accelerometry data indicated an average of 115min/day in light activity, 37min/day in MVPA and 664min/day (11.1h) in sedentary behaviour. Bivariate analyses indicated no significant associations between explanatory variables and MVPA and sedentary behaviour. Inpatient adults with mental illness can be physically active, with walking comprising the major component of MVPA time. Inpatient adults with mental illness spend a significant amount of time sitting; intervention strategies could focus on reducing the time spent sitting in general relaxation and doing nothing. Copyright © 2015 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
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Gámez-Guadix, Manuel; Borrajo, Erika; Almendros, Carmen
2016-03-01
Background and aims This study aims to analyze the cross-sectional and longitudinal relationship between three major risky online behaviors during adolescence: problematic Internet use, cyberbullying perpetration, and meeting strangers online. An additional objective was to study the role of impulsivity-irresponsibility as a possible explanatory variable of the relationships between these risky online behaviors. Methods The study sample was 888 adolescents that completed self-report measures at time 1 and time 2 with an interval of 6 months. Results The findings showed a significant cross-sectional relationship between the risky online behaviors analyzed. At the longitudinal level, problematic Internet use at time 1 predicted an increase in the perpetration of cyberbullying and meeting strangers online at time 2. Furthermore, meeting strangers online increased the likelihood of cyberbullying perpetration at time 2. Finally, when impulsivity-irresponsibility was included in the model as an explanatory variable, the relationships previously found remained significant. Discussion These results extend traditional problem behavior theory during adolescence, also supporting a relationship between different risky behaviors in cyberspace. In addition, findings highlighted the role of problematic Internet use, which increased the chances of developing cyberbullying perpetration and meeting strangers online over time. However, the results suggest a limited role of impulsivity-irresponsibility as an explicative mechanism. Conclusions The findings suggest that various online risk activities ought to be addressed together when planning assessment, prevention and intervention efforts.
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Dowling, N Maritza; Bolt, Daniel M; Deng, Sien; Li, Chenxi
2016-05-26
Patient-reported outcome (PRO) measures play a key role in the advancement of patient-centered care research. The accuracy of inferences, relevance of predictions, and the true nature of the associations made with PRO data depend on the validity of these measures. Errors inherent to self-report measures can seriously bias the estimation of constructs assessed by the scale. A well-documented disadvantage of self-report measures is their sensitivity to response style (RS) effects such as the respondent's tendency to select the extremes of a rating scale. Although the biasing effect of extreme responding on constructs measured by self-reported tools has been widely acknowledged and studied across disciplines, little attention has been given to the development and systematic application of methodologies to assess and control for this effect in PRO measures. We review the methodological approaches that have been proposed to study extreme RS effects (ERS). We applied a multidimensional item response theory model to simultaneously estimate and correct for the impact of ERS on trait estimation in a PRO instrument. Model estimates were used to study the biasing effects of ERS on sum scores for individuals with the same amount of the targeted trait but different levels of ERS. We evaluated the effect of joint estimation of multiple scales and ERS on trait estimates and demonstrated the biasing effects of ERS on these trait estimates when used as explanatory variables. A four-dimensional model accounting for ERS bias provided a better fit to the response data. Increasing levels of ERS showed bias in total scores as a function of trait estimates. The effect of ERS was greater when the pattern of extreme responding was the same across multiple scales modeled jointly. The estimated item category intercepts provided evidence of content independent category selection. Uncorrected trait estimates used as explanatory variables in prediction models showed downward bias. A comprehensive evaluation of the psychometric quality and soundness of PRO assessment measures should incorporate the study of ERS as a potential nuisance dimension affecting the accuracy and validity of scores and the impact of PRO data in clinical research and decision making.
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NASA Astrophysics Data System (ADS)
Smith, R. A.; Moore, R. B.; Shanley, J. B.; Miller, E. K.; Kamman, N. C.; Nacci, D.
2009-12-01
Mercury (Hg) concentrations in fish and aquatic wildlife are complex functions of atmospheric Hg deposition rate, terrestrial and aquatic watershed characteristics that influence Hg methylation and export, and food chain characteristics determining Hg bioaccumulation. Because of the complexity and incomplete understanding of these processes, regional-scale models of fish tissue Hg concentration are necessarily empirical in nature, typically constructed through regression analysis of fish tissue Hg concentration data from many sampling locations on a set of potential explanatory variables. Unless the data sets are unusually long and show clear time trends, the empirical basis for model building must be based solely on spatial correlation. Predictive regional scale models are highly useful for improving understanding of the relevant biogeochemical processes, as well as for practical fish and wildlife management and human health protection. Mechanistically, the logical arrangement of explanatory variables is to multiply each of the individual Hg source terms (e.g. dry, wet, and gaseous deposition rates, and residual watershed Hg) for a given fish sampling location by source-specific terms pertaining to methylation, watershed transport, and biological uptake for that location (e.g. SO4 availability, hill slope, lake size). This mathematical form has the desirable property that predicted tissue concentration will approach zero as all individual source terms approach zero. One complication with this form, however, is that it is inconsistent with the standard linear multiple regression equation in which all terms (including those for sources and physical conditions) are additive. An important practical disadvantage of a model in which the Hg source terms are additive (rather than multiplicative) with their modifying factors is that predicted concentration is not zero when all sources are zero, making it unreliable for predicting the effects of large future reductions in Hg deposition. In this paper we compare the results of using several different linear and non-linear models in an analysis of watershed and fish Hg data for 450 New England lakes. The differences in model results pertain to both their utility in interpreting methylation and export processes as well as in fisheries management.
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Tucker, Bram; Tsiazonera; Tombo, Jaovola; Hajasoa, Patricia; Nagnisaha, Charlotte
2015-01-01
A fact of life for farmers, hunter-gatherers, and fishermen in the rural parts of the world are that crops fail, wild resources become scarce, and winds discourage fishing. In this article we approach subsistence risk from the perspective of “coexistence thinking,” the simultaneous application of natural and supernatural causal models to explain subsistence success and failure. In southwestern Madagascar, the ecological world is characterized by extreme variability and unpredictability, and the cosmological world is characterized by anxiety about supernatural dangers. Ecological and cosmological causes seem to point to different risk minimizing strategies: to avoid losses from drought, flood, or heavy winds, one should diversify activities and be flexible; but to avoid losses caused by disrespected spirits one should narrow one’s range of behaviors to follow the code of taboos and offerings. We address this paradox by investigating whether southwestern Malagasy understand natural and supernatural causes as occupying separate, contradictory explanatory systems (target dependence), whether they make no categorical distinction between natural and supernatural forces and combine them within a single explanatory system (synthetic thinking), or whether they have separate natural and supernatural categories of causes that are integrated into one explanatory system so that supernatural forces drive natural forces (integrative thinking). Results from three field studies suggest that (a) informants explain why crops, prey, and market activities succeed or fail with reference to natural causal forces like rainfall and pests, (b) they explain why individual persons experience success or failure primarily with supernatural factors like God and ancestors, and (c) they understand supernatural forces as driving natural forces, so that ecology and cosmology represent distinct sets of causes within a single explanatory framework. We expect that future cross-cultural analyses may find that this form of “integrative thinking” is common in unpredictable environments and is a cognitive strategy that accompanies economic diversification. PMID:26528205
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Tucker, Bram; Tsiazonera; Tombo, Jaovola; Hajasoa, Patricia; Nagnisaha, Charlotte
2015-01-01
A fact of life for farmers, hunter-gatherers, and fishermen in the rural parts of the world are that crops fail, wild resources become scarce, and winds discourage fishing. In this article we approach subsistence risk from the perspective of "coexistence thinking," the simultaneous application of natural and supernatural causal models to explain subsistence success and failure. In southwestern Madagascar, the ecological world is characterized by extreme variability and unpredictability, and the cosmological world is characterized by anxiety about supernatural dangers. Ecological and cosmological causes seem to point to different risk minimizing strategies: to avoid losses from drought, flood, or heavy winds, one should diversify activities and be flexible; but to avoid losses caused by disrespected spirits one should narrow one's range of behaviors to follow the code of taboos and offerings. We address this paradox by investigating whether southwestern Malagasy understand natural and supernatural causes as occupying separate, contradictory explanatory systems (target dependence), whether they make no categorical distinction between natural and supernatural forces and combine them within a single explanatory system (synthetic thinking), or whether they have separate natural and supernatural categories of causes that are integrated into one explanatory system so that supernatural forces drive natural forces (integrative thinking). Results from three field studies suggest that (a) informants explain why crops, prey, and market activities succeed or fail with reference to natural causal forces like rainfall and pests, (b) they explain why individual persons experience success or failure primarily with supernatural factors like God and ancestors, and (c) they understand supernatural forces as driving natural forces, so that ecology and cosmology represent distinct sets of causes within a single explanatory framework. We expect that future cross-cultural analyses may find that this form of "integrative thinking" is common in unpredictable environments and is a cognitive strategy that accompanies economic diversification.
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Predicting the risk of groundwater arsenic contamination in drinking water wells
NASA Astrophysics Data System (ADS)
Cao, Hailong; Xie, Xianjun; Wang, Yanxin; Pi, Kunfu; Li, Junxia; Zhan, Hongbin; Liu, Peng
2018-05-01
Arsenic (As)-contaminated groundwater is a global concern with potential detrimental effects on the health of hundreds of millions of people worldwide. However, the extent of this problem may be more severe than anticipated, as many wells have not been tested and may contain unsafe-level of As. An optimized statistical regression model was developed to predict the probability of geogenic high As groundwater (As > 10 μg/L) in this study. Easily obtained hydrogeochemical and geological parameters that are significantly related to As geochemical behaviors were selected as explanatory variables in the model. The results indicate that pH, Cl-, HCO3-, SO42-, and NO3- concentrations, stratigraphic information, and well depth are excellent predictors of As exposure in the Datong Basin, China. Predicted unsafe wells correspond well with the known distribution of high As groundwater in the Datong Basin. The successful application of a data set from Bangladesh also demonstrated the applicability and credibility of this proposed method.
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Robles, Zuzuky; Anjum, Sahar; Garey, Lorra; Kauffman, Brooke Y; Rodríguez-Cano, Rubén; Langdon, Kirsten J; Neighbors, Clayton; Reitzel, Lorraine R; Zvolensky, Michael J
2017-07-01
Little work has focused on the underlying mechanisms that may link financial strain and smoking processes. The current study tested the hypothesis that financial strain would exert an indirect effect on cognitive-based smoking processes via depressive symptoms. Three clinically significant dependent variables linked to the maintenance of smoking were evaluated: negative affect reduction motives, negative mood abstinence expectancies, and perceived barriers for quitting. Participants included 102 adult daily smokers (M age =33.0years, SD=13.60; 35.3% female) recruited from the community to participate in a self-guided (unaided; no psychological or pharmacological intervention) smoking cessation study. Results indicated that depressive symptoms explain, in part, the relation between financial strain and smoking motives for negative affect reduction, negative mood abstinence expectancies, and perceived barriers for quitting. Results indicate that smoking interventions for individuals with high levels of financial strain may potentially benefit from the addition of therapeutic tactics aimed at reducing depression. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Cusack, K; Spates, C R
1999-01-01
Twenty-seven subjects were exposed to standard Eye Movement Desensitization and Reprocessing (EMDR) treatment or a similar treatment without the explicit cognitive elements found in EMDR. Standardized psychometric assessments were administered (Structured Interview for Post Traumatic Stress Disorder, Impact of Event Scale, Revised Symptom Checklist-90) by independent assessors at pretest, posttest and two separate follow-up periods. Potential subjects met specific inclusion/exclusion criteria. Subjective measures including Subjective Units of Disturbance and Validity of Cognition assessments were also conducted. A two-factor repeated measures analysis of variance revealed that both treatments produced significant symptom reductions and were comparable on all dependent measures across assessment phases. The present findings are discussed in light of previous dismantling research that converges to suggest that several elements in the EMDR protocol may be superfluous in terms of the contribution to treatment outcome. These same elements have nevertheless entered unparsimoniously into consideration as possible explanatory variables.
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Hein, Irma M; Troost, Pieter W; de Vries, Martine C; Knibbe, Catherijne A J; van Goudoever, Johannes B; Lindauer, Ramón J L
2015-07-01
More pediatric drug trials are needed, but although specific pediatric regulations warrant safety, recruitment of children for these trials remains one of the main difficulties. Therefore, we investigated potential determining factors of nonparticipation in clinical research, in order to optimize research participation of children by recommending improved recruitment strategies. Between 1 January 2012 and 1 January 2014, we performed a prospective study among161 pediatric patients, aged 6 to 18 y, who were eligible for clinical research. We quantitatively analyzed the association of potential explanatory variables (e.g., age, cognitive development, experience, ethnicity) with nonparticipation and qualitatively analyzed interviews on reasons for nonparticipation. Sixty percent of the children did not participate in the research project on offer (39% decided not to participate, 21% were indecisive). Lower age, less disease experience, and less complex research with lower risk were predictive for not participating. Time constraint and extra burden were expressed as decisive reasons for not participating. Strategies to optimize research participation should be aimed at younger children and their families, who are logistically challenged and unfamiliar with health care and research. Recommendations include informing pediatric patients and their families of the value of research; minimizing logistic burdens; and improving accessibility.
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Human influence on California fire regimes.
Syphard, Alexandra D; Radeloff, Volker C; Keeley, Jon E; Hawbaker, Todd J; Clayton, Murray K; Stewart, Susan I; Hammer, Roger B
2007-07-01
Periodic wildfire maintains the integrity and species composition of many ecosystems, including the mediterranean-climate shrublands of California. However, human activities alter natural fire regimes, which can lead to cascading ecological effects. Increased human ignitions at the wildland-urban interface (WUI) have recently gained attention, but fire activity and risk are typically estimated using only biophysical variables. Our goal was to determine how humans influence fire in California and to examine whether this influence was linear, by relating contemporary (2000) and historic (1960-2000) fire data to both human and biophysical variables. Data for the human variables included fine-resolution maps of the WUI produced using housing density and land cover data. Interface WUI, where development abuts wildland vegetation, was differentiated from intermix WUI, where development intermingles with wildland vegetation. Additional explanatory variables included distance to WUI, population density, road density, vegetation type, and ecoregion. All data were summarized at the county level and analyzed using bivariate and multiple regression methods. We found highly significant relationships between humans and fire on the contemporary landscape, and our models explained fire frequency (R2 = 0.72) better than area burned (R2 = 0.50). Population density, intermix WUI, and distance to WUI explained the most variability in fire frequency, suggesting that the spatial pattern of development may be an important variable to consider when estimating fire risk. We found nonlinear effects such that fire frequency and area burned were highest at intermediate levels of human activity, but declined beyond certain thresholds. Human activities also explained change in fire frequency and area burned (1960-2000), but our models had greater explanatory power during the years 1960-1980, when there was more dramatic change in fire frequency. Understanding wildfire as a function of the spatial arrangement of ignitions and fuels on the landscape, in addition to nonlinear relationships, will be important to fire managers and conservation planners because fire risk may be related to specific levels of housing density that can be accounted for in land use planning. With more fires occurring in close proximity to human infrastructure, there may also be devastating ecological impacts if development continues to grow farther into wildland vegetation.
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Human influence on California fire regimes
Syphard, A.D.; Radeloff, V.C.; Keeley, J.E.; Hawbaker, T.J.; Clayton, M.K.; Stewart, S.I.; Hammer, R.B.
2007-01-01
Periodic wildfire maintains the integrity and species composition of many ecosystems, including the mediterranean-climate shrublands of California. However, human activities alter natural fire regimes, which can lead to cascading ecological effects. Increased human ignitions at the wildland-urban interface (WUI) have recently gained attention, but fire activity and risk are typically estimated using only biophysical variables. Our goal was to determine how humans influence fire in California and to examine whether this influence was linear, by relating contemporary (2000) and historic (1960-2000) fire data to both human and biophysical variables. Data for the human variables included fine-resolution maps of the WUI produced using housing density and land cover data. Interface WUI, where development abuts wildland vegetation, was differentiated from intermix WUI, where development intermingles with wildland vegetation. Additional explanatory variables included distance to WUI, population density, road density, vegetation type, and ecoregion. All data were summarized at the county level and analyzed using bivariate and multiple regression methods. We found highly significant relationships between humans and fire on the contemporary landscape, and our models explained fire frequency (R2 = 0.72) better than area burned (R2 = 0.50). Population density, intermix WUI, and distance to WUI explained the most variability in fire frequency, suggesting that the spatial pattern of development may be an important variable to consider when estimating fire risk. We found nonlinear effects such that fire frequency and area burned were highest at intermediate levels of human activity, but declined beyond certain thresholds. Human activities also explained change in fire frequency and area burned (1960-2000), but our models had greater explanatory power during the years 1960-1980, when there was more dramatic change in fire frequency. Understanding wildfire as a function of the spatial arrangement of ignitions and fuels on the landscape, in addition to nonlinear relationships, will be important to fire managers and conservation planners because fire risk may be related to specific levels of housing density that can be accounted for in land use planning. With more fires occurring in close proximity to human infrastructure, there may also be devastating ecological impacts if development continues to grow farther into wildland vegetation. ?? 2007 by the Ecological Society of America.