Assessing dose–response effects of national essential medicine policy in China: comparison of two methods for handling data with a stepped wedge-like design and hierarchical structure

PubMed Central

Ren, Yan; Yang, Min; Li, Qian; Pan, Jay; Chen, Fei; Li, Xiaosong; Meng, Qun

2017-01-01

Objectives To introduce multilevel repeated measures (RM) models and compare them with multilevel difference-in-differences (DID) models in assessing the linear relationship between the length of the policy intervention period and healthcare outcomes (dose–response effect) for data from a stepped-wedge design with a hierarchical structure. Design The implementation of national essential medicine policy (NEMP) in China was a stepped-wedge-like design of five time points with a hierarchical structure. Using one key healthcare outcome from the national NEMP surveillance data as an example, we illustrate how a series of multilevel DID models and one multilevel RM model can be fitted to answer some research questions on policy effects. Setting Routinely and annually collected national data on China from 2008 to 2012. Participants 34 506 primary healthcare facilities in 2675 counties of 31 provinces. Outcome measures Agreement and differences in estimates of dose–response effect and variation in such effect between the two methods on the logarithm-transformed total number of outpatient visits per facility per year (LG-OPV). Results The estimated dose–response effect was approximately 0.015 according to four multilevel DID models and precisely 0.012 from one multilevel RM model. Both types of model estimated an increase in LG-OPV by 2.55 times from 2009 to 2012, but 2–4.3 times larger SEs of those estimates were found by the multilevel DID models. Similar estimates of mean effects of covariates and random effects of the average LG-OPV among all levels in the example dataset were obtained by both types of model. Significant variances in the dose–response among provinces, counties and facilities were estimated, and the ‘lowest’ or ‘highest’ units by their dose–response effects were pinpointed only by the multilevel RM model. Conclusions For examining dose–response effect based on data from multiple time points with hierarchical structure and the stepped wedge-like designs, multilevel RM models are more efficient, convenient and informative than the multilevel DID models. PMID:28399510

Intermediate and advanced topics in multilevel logistic regression analysis

PubMed Central

Merlo, Juan

2017-01-01

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher‐level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within‐cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population‐average effect of covariates measured at the subject and cluster level, in contrast to the within‐cluster or cluster‐specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster‐level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:28543517

Intermediate and advanced topics in multilevel logistic regression analysis.

PubMed

Austin, Peter C; Merlo, Juan

2017-09-10

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Evaluating flow cytometer performance with weighted quadratic least squares analysis of LED and multi-level bead data.

PubMed

Parks, David R; El Khettabi, Faysal; Chase, Eric; Hoffman, Robert A; Perfetto, Stephen P; Spidlen, Josef; Wood, James C S; Moore, Wayne A; Brinkman, Ryan R

2017-03-01

We developed a fully automated procedure for analyzing data from LED pulses and multilevel bead sets to evaluate backgrounds and photoelectron scales of cytometer fluorescence channels. The method improves on previous formulations by fitting a full quadratic model with appropriate weighting and by providing standard errors and peak residuals as well as the fitted parameters themselves. Here we describe the details of the methods and procedures involved and present a set of illustrations and test cases that demonstrate the consistency and reliability of the results. The automated analysis and fitting procedure is generally quite successful in providing good estimates of the Spe (statistical photoelectron) scales and backgrounds for all the fluorescence channels on instruments with good linearity. The precision of the results obtained from LED data is almost always better than that from multilevel bead data, but the bead procedure is easy to carry out and provides results good enough for most purposes. Including standard errors on the fitted parameters is important for understanding the uncertainty in the values of interest. The weighted residuals give information about how well the data fits the model, and particularly high residuals indicate bad data points. Known photoelectron scales and measurement channel backgrounds make it possible to estimate the precision of measurements at different signal levels and the effects of compensated spectral overlap on measurement quality. Combining this information with measurements of standard samples carrying dyes of biological interest, we can make accurate comparisons of dye sensitivity among different instruments. Our method is freely available through the R/Bioconductor package flowQB. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

Fitting and Calibrating a Multilevel Mixed-Effects Stem Taper Model for Maritime Pine in NW Spain

PubMed Central

Arias-Rodil, Manuel; Castedo-Dorado, Fernando; Cámara-Obregón, Asunción; Diéguez-Aranda, Ulises

2015-01-01

Stem taper data are usually hierarchical (several measurements per tree, and several trees per plot), making application of a multilevel mixed-effects modelling approach essential. However, correlation between trees in the same plot/stand has often been ignored in previous studies. Fitting and calibration of a variable-exponent stem taper function were conducted using data from 420 trees felled in even-aged maritime pine (Pinus pinaster Ait.) stands in NW Spain. In the fitting step, the tree level explained much more variability than the plot level, and therefore calibration at plot level was omitted. Several stem heights were evaluated for measurement of the additional diameter needed for calibration at tree level. Calibration with an additional diameter measured at between 40 and 60% of total tree height showed the greatest improvement in volume and diameter predictions. If additional diameter measurement is not available, the fixed-effects model fitted by the ordinary least squares technique should be used. Finally, we also evaluated how the expansion of parameters with random effects affects the stem taper prediction, as we consider this a key question when applying the mixed-effects modelling approach to taper equations. The results showed that correlation between random effects should be taken into account when assessing the influence of random effects in stem taper prediction. PMID:26630156

The Moderating Effect of Health-Improving Workplace Environment on Promoting Physical Activity in White-Collar Employees: A Multi-Site Longitudinal Study Using Multi-Level Structural Equation Modeling.

PubMed

Watanabe, Kazuhiro; Otsuka, Yasumasa; Shimazu, Akihito; Kawakami, Norito

2016-02-01

This longitudinal study aimed to investigate the moderating effect of health-improving workplace environment on relationships between physical activity, self-efficacy, and psychological distress. Data were collected from 16 worksites and 129 employees at two time-points. Health-improving workplace environment was measured using the Japanese version of the Environmental Assessment Tool. Physical activity, self-efficacy, and psychological distress were also measured. Multi-level structural equation modeling was used to investigate the moderating effect of health-improving workplace environment on relationships between psychological distress, self-efficacy, and physical activity. Psychological distress was negatively associated with physical activity via low self-efficacy. Physical activity was negatively related to psychological distress. Physical activity/fitness facilities in the work environment exaggerated the positive relationship between self-efficacy and physical activity. Physical activity/fitness facilities in the workplace may promote employees' physical activity.

Estimating multilevel logistic regression models when the number of clusters is low: a comparison of different statistical software procedures.

PubMed

Austin, Peter C

2010-04-22

Multilevel logistic regression models are increasingly being used to analyze clustered data in medical, public health, epidemiological, and educational research. Procedures for estimating the parameters of such models are available in many statistical software packages. There is currently little evidence on the minimum number of clusters necessary to reliably fit multilevel regression models. We conducted a Monte Carlo study to compare the performance of different statistical software procedures for estimating multilevel logistic regression models when the number of clusters was low. We examined procedures available in BUGS, HLM, R, SAS, and Stata. We found that there were qualitative differences in the performance of different software procedures for estimating multilevel logistic models when the number of clusters was low. Among the likelihood-based procedures, estimation methods based on adaptive Gauss-Hermite approximations to the likelihood (glmer in R and xtlogit in Stata) or adaptive Gaussian quadrature (Proc NLMIXED in SAS) tended to have superior performance for estimating variance components when the number of clusters was small, compared to software procedures based on penalized quasi-likelihood. However, only Bayesian estimation with BUGS allowed for accurate estimation of variance components when there were fewer than 10 clusters. For all statistical software procedures, estimation of variance components tended to be poor when there were only five subjects per cluster, regardless of the number of clusters.

Assessing dose-response effects of national essential medicine policy in China: comparison of two methods for handling data with a stepped wedge-like design and hierarchical structure.

PubMed

Ren, Yan; Yang, Min; Li, Qian; Pan, Jay; Chen, Fei; Li, Xiaosong; Meng, Qun

2017-02-22

To introduce multilevel repeated measures (RM) models and compare them with multilevel difference-in-differences (DID) models in assessing the linear relationship between the length of the policy intervention period and healthcare outcomes (dose-response effect) for data from a stepped-wedge design with a hierarchical structure. The implementation of national essential medicine policy (NEMP) in China was a stepped-wedge-like design of five time points with a hierarchical structure. Using one key healthcare outcome from the national NEMP surveillance data as an example, we illustrate how a series of multilevel DID models and one multilevel RM model can be fitted to answer some research questions on policy effects. Routinely and annually collected national data on China from 2008 to 2012. 34 506 primary healthcare facilities in 2675 counties of 31 provinces. Agreement and differences in estimates of dose-response effect and variation in such effect between the two methods on the logarithm-transformed total number of outpatient visits per facility per year (LG-OPV). The estimated dose-response effect was approximately 0.015 according to four multilevel DID models and precisely 0.012 from one multilevel RM model. Both types of model estimated an increase in LG-OPV by 2.55 times from 2009 to 2012, but 2-4.3 times larger SEs of those estimates were found by the multilevel DID models. Similar estimates of mean effects of covariates and random effects of the average LG-OPV among all levels in the example dataset were obtained by both types of model. Significant variances in the dose-response among provinces, counties and facilities were estimated, and the 'lowest' or 'highest' units by their dose-response effects were pinpointed only by the multilevel RM model. For examining dose-response effect based on data from multiple time points with hierarchical structure and the stepped wedge-like designs, multilevel RM models are more efficient, convenient and informative than the multilevel DID models. 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/.

Analyzing longitudinal data with the linear mixed models procedure in SPSS.

PubMed

West, Brady T

2009-09-01

Many applied researchers analyzing longitudinal data share a common misconception: that specialized statistical software is necessary to fit hierarchical linear models (also known as linear mixed models [LMMs], or multilevel models) to longitudinal data sets. Although several specialized statistical software programs of high quality are available that allow researchers to fit these models to longitudinal data sets (e.g., HLM), rapid advances in general purpose statistical software packages have recently enabled analysts to fit these same models when using preferred packages that also enable other more common analyses. One of these general purpose statistical packages is SPSS, which includes a very flexible and powerful procedure for fitting LMMs to longitudinal data sets with continuous outcomes. This article aims to present readers with a practical discussion of how to analyze longitudinal data using the LMMs procedure in the SPSS statistical software package.

Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway.

PubMed

Shi, Qi; Abdel-Aty, Mohamed; Yu, Rongjie

2016-03-01

In traffic safety studies, crash frequency modeling of total crashes is the cornerstone before proceeding to more detailed safety evaluation. The relationship between crash occurrence and factors such as traffic flow and roadway geometric characteristics has been extensively explored for a better understanding of crash mechanisms. In this study, a multi-level Bayesian framework has been developed in an effort to identify the crash contributing factors on an urban expressway in the Central Florida area. Two types of traffic data from the Automatic Vehicle Identification system, which are the processed data capped at speed limit and the unprocessed data retaining the original speed were incorporated in the analysis along with road geometric information. The model framework was proposed to account for the hierarchical data structure and the heterogeneity among the traffic and roadway geometric data. Multi-level and random parameters models were constructed and compared with the Negative Binomial model under the Bayesian inference framework. Results showed that the unprocessed traffic data was superior. Both multi-level models and random parameters models outperformed the Negative Binomial model and the models with random parameters achieved the best model fitting. The contributing factors identified imply that on the urban expressway lower speed and higher speed variation could significantly increase the crash likelihood. Other geometric factors were significant including auxiliary lanes and horizontal curvature. Copyright © 2015 Elsevier Ltd. All rights reserved.

Healthcare access and mammography screening in Michigan: a multilevel cross-sectional study

PubMed Central

2012-01-01

Background Breast cancer screening rates have increased over time in the United States. However actual screening rates appear to be lower among black women compared with white women. Purpose To assess determinants of breast cancer screening among women in Michigan USA, focusing on individual and neighborhood socio-economic status and healthcare access. Methods Data from 1163 women ages 50-74 years who participated in the 2008 Michigan Special Cancer Behavioral Risk Factor Survey were analyzed. County-level SES and healthcare access were obtained from the Area Resource File. Multilevel logistic regression models were fit using SAS Proc Glimmix to account for clustering of individual observations by county. Separate models were fit for each of the two outcomes of interest; mammography screening and clinical breast examination. For each outcome, two sequential models were fit; a model including individual level covariates and a model including county level covariates. Results After adjusting for misclassification bias, overall cancer screening rates were lower than reported by survey respondents; black women had lower mammography screening rates but higher clinical breast examination rates than white women. However, after adjusting for other individual level variables, race was not a significant predictor of screening. Having health insurance or a usual healthcare provider were the most important predictors of cancer screening. Discussion Access to healthcare is important to ensuring appropriate cancer screening among women in Michigan. PMID:22436125

The role of perceived social support in loneliness and self-esteem among children affected by HIV/AIDS: a longitudinal multilevel analysis in rural China.

PubMed

Qiao, Shan; Li, Xiaoming; Zhao, Guoxiang; Zhao, Junfeng; Stanton, Bonita

2014-07-01

To delineate the trajectories of loneliness and self-esteem over time among children affected by parental HIV and AIDS, and to examine how their perceived social support (PSS) influenced initial scores and change rates of these two psychological outcomes. We collected longitudinal data from children affected by parental HIV/AIDS in rural central China. Children 6-18 years of age at baseline were eligible to participate in the study and were assessed annually for 3 years. Multilevel regression models for change were used to assess the effect of baseline PSS on the trajectories of loneliness and self-esteem over time. We employed maximum likelihood estimates to fit multilevel models and specified the between-individual covariance matrix as 'unstructured' to allow correlation among the different sources of variance. Statistics including -2 Log Likelihood, Akaike Information Criterion and Bayesian Information Criterion were used in evaluating the model fit. The results of multilevel analyses indicated that loneliness scores significantly declined over time. Controlling for demographic characteristics, children with higher PSS reported significantly lower baseline loneliness score and experienced a slower rate of decline in loneliness over time. Children with higher PSS were more likely to report higher self-esteem scores at baseline. However, the self-esteem scores remained stable over time controlling for baseline PSS and all the other variables. The positive effect of PSS on psychological adjustment may imply a promising approach for future intervention among children affected by HIV/AIDS, in which efforts to promote psychosocial well being could focus on children and families with lower social support. We also call for a greater understanding of children's psychological adjustment process in various contexts of social support and appropriate adaptations of evidence-based interventions to meet their diverse needs.

Lifting the lid on geographic complexity in the relationship between body mass index and education in China.

PubMed

Zhou, Maigeng; Feng, Xiaoqi; Yong, Jiang; Li, Yichong; Zhang, Mei; Page, Andrew; Astell-Burt, Thomas; Zhao, Wenhua

2017-07-01

In China, rising obesity has coincided with increasing affluence. Few studies have properly accounted for geographic variation, however, which may influence prior results. Using large data with biomarkers in China, we show body mass index (BMI) to be positively correlated with higher person-level education if estimated using ordinary least squares. In stark contrast, fitting the same data within a multilevel model gives the complete opposite result. We go on to show that the relationship between BMI and person-level education in China is dependent upon geography, underlining why multilevel modelling is crucial for revealing these types of people-place contingencies. Copyright © 2017. Published by Elsevier Ltd.

Student and School SES, Gender, Strategy Use, and Achievement

ERIC Educational Resources Information Center

Callan, Gregory L.; Marchant, Gregory J.; Finch, W. Holmes; Flegge, Lindsay

2017-01-01

A multilevel mediated regression model was fit to Programme for International Student Assessment achievement, strategy use, gender, and family- and school-level socioeconomic status (SES). Two metacognitive strategies (i.e., understanding and summarizing) and one learning strategy (i.e., control strategies) were found to relate significantly and…

Fully Bayesian Estimation of Data from Single Case Designs

ERIC Educational Resources Information Center

Rindskopf, David

2013-01-01

Single case designs (SCDs) generally consist of a small number of short time series in two or more phases. The analysis of SCDs statistically fits in the framework of a multilevel model, or hierarchical model. The usual analysis does not take into account the uncertainty in the estimation of the random effects. This not only has an effect on the…

A Tutorial on Multilevel Survival Analysis: Methods, Models and Applications

PubMed Central

Austin, Peter C.

2017-01-01

Summary Data that have a multilevel structure occur frequently across a range of disciplines, including epidemiology, health services research, public health, education and sociology. We describe three families of regression models for the analysis of multilevel survival data. First, Cox proportional hazards models with mixed effects incorporate cluster-specific random effects that modify the baseline hazard function. Second, piecewise exponential survival models partition the duration of follow-up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. This is equivalent to a Poisson regression model that incorporates the duration of exposure within each interval. By incorporating cluster-specific random effects, generalised linear mixed models can be used to analyse these data. Third, after partitioning the duration of follow-up into mutually exclusive intervals, one can use discrete time survival models that use a complementary log–log generalised linear model to model the occurrence of the outcome of interest within each interval. Random effects can be incorporated to account for within-cluster homogeneity in outcomes. We illustrate the application of these methods using data consisting of patients hospitalised with a heart attack. We illustrate the application of these methods using three statistical programming languages (R, SAS and Stata). PMID:29307954

A Tutorial on Multilevel Survival Analysis: Methods, Models and Applications.

PubMed

Austin, Peter C

2017-08-01

Data that have a multilevel structure occur frequently across a range of disciplines, including epidemiology, health services research, public health, education and sociology. We describe three families of regression models for the analysis of multilevel survival data. First, Cox proportional hazards models with mixed effects incorporate cluster-specific random effects that modify the baseline hazard function. Second, piecewise exponential survival models partition the duration of follow-up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. This is equivalent to a Poisson regression model that incorporates the duration of exposure within each interval. By incorporating cluster-specific random effects, generalised linear mixed models can be used to analyse these data. Third, after partitioning the duration of follow-up into mutually exclusive intervals, one can use discrete time survival models that use a complementary log-log generalised linear model to model the occurrence of the outcome of interest within each interval. Random effects can be incorporated to account for within-cluster homogeneity in outcomes. We illustrate the application of these methods using data consisting of patients hospitalised with a heart attack. We illustrate the application of these methods using three statistical programming languages (R, SAS and Stata).

Evolutionarily stable disequilibrium: endless dynamics of evolution in a stationary population.

PubMed

Takeuchi, Nobuto; Kaneko, Kunihiko; Hogeweg, Paulien

2016-05-11

Evolution is often conceived as changes in the properties of a population over generations. Does this notion exhaust the possible dynamics of evolution? Life is hierarchically organized, and evolution can operate at multiple levels with conflicting tendencies. Using a minimal model of such conflicting multilevel evolution, we demonstrate the possibility of a novel mode of evolution that challenges the above notion: individuals ceaselessly modify their genetically inherited phenotype and fitness along their lines of descent, without involving apparent changes in the properties of the population. The model assumes a population of primitive cells (protocells, for short), each containing a population of replicating catalytic molecules. Protocells are selected towards maximizing the catalytic activity of internal molecules, whereas molecules tend to evolve towards minimizing it in order to maximize their relative fitness within a protocell. These conflicting evolutionary tendencies at different levels and genetic drift drive the lineages of protocells to oscillate endlessly between high and low intracellular catalytic activity, i.e. high and low fitness, along their lines of descent. This oscillation, however, occurs independently in different lineages, so that the population as a whole appears stationary. Therefore, ongoing evolution can be hidden behind an apparently stationary population owing to conflicting multilevel evolution. © 2016 The Authors.

Many-level multilevel structural equation modeling: An efficient evaluation strategy.

PubMed

Pritikin, Joshua N; Hunter, Michael D; von Oertzen, Timo; Brick, Timothy R; Boker, Steven M

2017-01-01

Structural equation models are increasingly used for clustered or multilevel data in cases where mixed regression is too inflexible. However, when there are many levels of nesting, these models can become difficult to estimate. We introduce a novel evaluation strategy, Rampart, that applies an orthogonal rotation to the parts of a model that conform to commonly met requirements. This rotation dramatically simplifies fit evaluation in a way that becomes more potent as the size of the data set increases. We validate and evaluate the implementation using a 3-level latent regression simulation study. Then we analyze data from a state-wide child behavioral health measure administered by the Oklahoma Department of Human Services. We demonstrate the efficiency of Rampart compared to other similar software using a latent factor model with a 5-level decomposition of latent variance. Rampart is implemented in OpenMx, a free and open source software.

A Multilevel Shape Fit Analysis of Neutron Transmission Data

NASA Astrophysics Data System (ADS)

Naguib, K.; Sallam, O. H.; Adib, M.; Ashry, A.

A multilevel shape fit analysis of neutron transmission data is presented. A multilevel computer code SHAPE is used to analyse clean transmission data obtained from time-of-flight (TOF) measurements. The shape analysis deduces the parameters of the observed resonances in the energy region considered in the measurements. The shape code is based upon a least square fit of a multilevel Briet-Wigner formula and includes both instrumental resolution and Doppler broadenings. Operating the SHAPE code on a test example of a measured transmission data of 151Eu, 153Eu and natural Eu in the energy range 0.025-1 eV accquired a good result for the used technique of analysis.Translated AbstractAnalyse von Neutronentransmissionsdaten mittels einer VielniveauformanpassungNeutronentransmissionsdaten werden in einer Vielniveauformanpassung analysiert. Dazu werden bereinigte Daten aus Flugzeitmessungen mit dem Rechnerprogramm SHAPE bearbeitet. Man erhält die Parameter der beobachteten Resonanzen im gemessenen Energiebereich. Die Formanpassung benutzt eine Briet-Wignerformel und berücksichtigt Linienverbreiterungen infolge sowohl der Meßeinrichtung als auch des Dopplereffekts. Als praktisches Beispiel werden 151Eu, 153Eu und natürliches Eu im Energiebereich 0.025 bis 1 eV mit guter Übereinstimmung theoretischer und experimenteller Werte behandelt.

From least squares to multilevel modeling: A graphical introduction to Bayesian inference

NASA Astrophysics Data System (ADS)

Loredo, Thomas J.

2016-01-01

This tutorial presentation will introduce some of the key ideas and techniques involved in applying Bayesian methods to problems in astrostatistics. The focus will be on the big picture: understanding the foundations (interpreting probability, Bayes's theorem, the law of total probability and marginalization), making connections to traditional methods (propagation of errors, least squares, chi-squared, maximum likelihood, Monte Carlo simulation), and highlighting problems where a Bayesian approach can be particularly powerful (Poisson processes, density estimation and curve fitting with measurement error). The "graphical" component of the title reflects an emphasis on pictorial representations of some of the math, but also on the use of graphical models (multilevel or hierarchical models) for analyzing complex data. Code for some examples from the talk will be available to participants, in Python and in the Stan probabilistic programming language.

Multilevel models for cost-effectiveness analyses that use cluster randomised trial data: An approach to model choice.

PubMed

Ng, Edmond S-W; Diaz-Ordaz, Karla; Grieve, Richard; Nixon, Richard M; Thompson, Simon G; Carpenter, James R

2016-10-01

Multilevel models provide a flexible modelling framework for cost-effectiveness analyses that use cluster randomised trial data. However, there is a lack of guidance on how to choose the most appropriate multilevel models. This paper illustrates an approach for deciding what level of model complexity is warranted; in particular how best to accommodate complex variance-covariance structures, right-skewed costs and missing data. Our proposed models differ according to whether or not they allow individual-level variances and correlations to differ across treatment arms or clusters and by the assumed cost distribution (Normal, Gamma, Inverse Gaussian). The models are fitted by Markov chain Monte Carlo methods. Our approach to model choice is based on four main criteria: the characteristics of the data, model pre-specification informed by the previous literature, diagnostic plots and assessment of model appropriateness. This is illustrated by re-analysing a previous cost-effectiveness analysis that uses data from a cluster randomised trial. We find that the most useful criterion for model choice was the deviance information criterion, which distinguishes amongst models with alternative variance-covariance structures, as well as between those with different cost distributions. This strategy for model choice can help cost-effectiveness analyses provide reliable inferences for policy-making when using cluster trials, including those with missing data. © The Author(s) 2013.

Agent-based model with multi-level herding for complex financial systems

NASA Astrophysics Data System (ADS)

Chen, Jun-Jie; Tan, Lei; Zheng, Bo

2015-02-01

In complex financial systems, the sector structure and volatility clustering are respectively important features of the spatial and temporal correlations. However, the microscopic generation mechanism of the sector structure is not yet understood. Especially, how to produce these two features in one model remains challenging. We introduce a novel interaction mechanism, i.e., the multi-level herding, in constructing an agent-based model to investigate the sector structure combined with volatility clustering. According to the previous market performance, agents trade in groups, and their herding behavior comprises the herding at stock, sector and market levels. Further, we propose methods to determine the key model parameters from historical market data, rather than from statistical fitting of the results. From the simulation, we obtain the sector structure and volatility clustering, as well as the eigenvalue distribution of the cross-correlation matrix, for the New York and Hong Kong stock exchanges. These properties are in agreement with the empirical ones. Our results quantitatively reveal that the multi-level herding is the microscopic generation mechanism of the sector structure, and provide new insight into the spatio-temporal interactions in financial systems at the microscopic level.

Agent-based model with multi-level herding for complex financial systems

PubMed Central

Chen, Jun-Jie; Tan, Lei; Zheng, Bo

2015-01-01

In complex financial systems, the sector structure and volatility clustering are respectively important features of the spatial and temporal correlations. However, the microscopic generation mechanism of the sector structure is not yet understood. Especially, how to produce these two features in one model remains challenging. We introduce a novel interaction mechanism, i.e., the multi-level herding, in constructing an agent-based model to investigate the sector structure combined with volatility clustering. According to the previous market performance, agents trade in groups, and their herding behavior comprises the herding at stock, sector and market levels. Further, we propose methods to determine the key model parameters from historical market data, rather than from statistical fitting of the results. From the simulation, we obtain the sector structure and volatility clustering, as well as the eigenvalue distribution of the cross-correlation matrix, for the New York and Hong Kong stock exchanges. These properties are in agreement with the empirical ones. Our results quantitatively reveal that the multi-level herding is the microscopic generation mechanism of the sector structure, and provide new insight into the spatio-temporal interactions in financial systems at the microscopic level. PMID:25669427

Can we get some cooperation around here? The mediating role of group norms on the relationship between team personality and individual helping behaviors.

PubMed

Gonzalez-Mulé, Erik; DeGeest, David S; McCormick, Brian W; Seong, Jee Young; Brown, Kenneth G

2014-09-01

Drawing on the group-norms theory of organizational citizenship behaviors and person-environment fit theory, we introduce and test a multilevel model of the effects of additive and dispersion composition models of team members' personality characteristics on group norms and individual helping behaviors. Our model was tested using regression and random coefficients modeling on 102 research and development teams. Results indicated that high mean levels of extraversion are positively related to individual helping behaviors through the mediating effect of cooperative group norms. Further, low variance on agreeableness (supplementary fit) and high variance on extraversion (complementary fit) promote the enactment of individual helping behaviors, but only the effects of extraversion were mediated by cooperative group norms. Implications of these findings for theories of helping behaviors in teams are discussed. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Multilevel Hierarchical Modeling of Benthic Macroinvertebrate Responses to Urbanization in Nine Metropolitan Regions across the Conterminous United States

USGS Publications Warehouse

Kashuba, Roxolana; Cha, YoonKyung; Alameddine, Ibrahim; Lee, Boknam; Cuffney, Thomas F.

2010-01-01

Multilevel hierarchical modeling methodology has been developed for use in ecological data analysis. The effect of urbanization on stream macroinvertebrate communities was measured across a gradient of basins in each of nine metropolitan regions across the conterminous United States. The hierarchical nature of this dataset was harnessed in a multi-tiered model structure, predicting both invertebrate response at the basin scale and differences in invertebrate response at the region scale. Ordination site scores, total taxa richness, Ephemeroptera, Plecoptera, Trichoptera (EPT) taxa richness, and richness-weighted mean tolerance of organisms at a site were used to describe invertebrate responses. Percentage of urban land cover was used as a basin-level predictor variable. Regional mean precipitation, air temperature, and antecedent agriculture were used as region-level predictor variables. Multilevel hierarchical models were fit to both levels of data simultaneously, borrowing statistical strength from the complete dataset to reduce uncertainty in regional coefficient estimates. Additionally, whereas non-hierarchical regressions were only able to show differing relations between invertebrate responses and urban intensity separately for each region, the multilevel hierarchical regressions were able to explain and quantify those differences within a single model. In this way, this modeling approach directly establishes the importance of antecedent agricultural conditions in masking the response of invertebrates to urbanization in metropolitan regions such as Milwaukee-Green Bay, Wisconsin; Denver, Colorado; and Dallas-Fort Worth, Texas. Also, these models show that regions with high precipitation, such as Atlanta, Georgia; Birmingham, Alabama; and Portland, Oregon, start out with better regional background conditions of invertebrates prior to urbanization but experience faster negative rates of change with urbanization. Ultimately, this urbanization-invertebrate response example is used to detail the multilevel hierarchical construction methodology, showing how the result is a set of models that are both statistically more rigorous and ecologically more interpretable than simple linear regression models.

Placing Families in Context: Challenges for Cross-National Family Research

PubMed Central

Yu, Wei-hsin

2015-01-01

Cross-national comparisons constitute a valuable strategy to assess how broader cultural, political, and institutional contexts shape family outcomes. One typical approach of cross-national family research is to use comparable data from a limited number of countries, fit similar regression models for each country, and compare results across country-specific models. Increasingly, researchers are adopting a second approach, which requires merging data from many more societies and testing multilevel models using the pooled sample. Although the second approach has the advantage of allowing direct estimates of the effects of nation-level characteristics, it is more likely to suffer from the problems of omitted-variable bias, influential cases, and measurement and construct nonequivalence. I discuss ways to improve the first approach's ability to infer macrolevel influences, as well as how to deal with challenges associated with the second one. I also suggest choosing analytical strategies according to whether the data meet multilevel models’ assumptions. PMID:25999603

Multilevel models for estimating incremental net benefits in multinational studies.

PubMed

Grieve, Richard; Nixon, Richard; Thompson, Simon G; Cairns, John

2007-08-01

Multilevel models (MLMs) have been recommended for estimating incremental net benefits (INBs) in multicentre cost-effectiveness analysis (CEA). However, these models have assumed that the INBs are exchangeable and that there is a common variance across all centres. This paper examines the plausibility of these assumptions by comparing various MLMs for estimating the mean INB in a multinational CEA. The results showed that the MLMs that assumed the INBs were exchangeable and had a common variance led to incorrect inferences. The MLMs that included covariates to allow for systematic differences across the centres, and estimated different variances in each centre, made more plausible assumptions, fitted the data better and led to more appropriate inferences. We conclude that the validity of assumptions underlying MLMs used in CEA need to be critically evaluated before reliable conclusions can be drawn. Copyright 2006 John Wiley & Sons, Ltd.

Ethnic and Gender Differences in Science Graduation at Selective Colleges with Implications for Admission Policy and College Choice

ERIC Educational Resources Information Center

Smyth, Frederick L.; McArdle, John J.

2004-01-01

Using Bowen and Bok's data from 23 selective colleges, we fit multilevel logit models to test two hypotheses with implications for affirmative action and group differences in attainment of science, math, or engineering (SME) degrees. Hypothesis 1, that differences in precollege academic preparation will explain later SME graduation disparities,…

Empirical valence bond models for reactive potential energy surfaces: a parallel multilevel genetic program approach.

PubMed

Bellucci, Michael A; Coker, David F

2011-07-28

We describe a new method for constructing empirical valence bond potential energy surfaces using a parallel multilevel genetic program (PMLGP). Genetic programs can be used to perform an efficient search through function space and parameter space to find the best functions and sets of parameters that fit energies obtained by ab initio electronic structure calculations. Building on the traditional genetic program approach, the PMLGP utilizes a hierarchy of genetic programming on two different levels. The lower level genetic programs are used to optimize coevolving populations in parallel while the higher level genetic program (HLGP) is used to optimize the genetic operator probabilities of the lower level genetic programs. The HLGP allows the algorithm to dynamically learn the mutation or combination of mutations that most effectively increase the fitness of the populations, causing a significant increase in the algorithm's accuracy and efficiency. The algorithm's accuracy and efficiency is tested against a standard parallel genetic program with a variety of one-dimensional test cases. Subsequently, the PMLGP is utilized to obtain an accurate empirical valence bond model for proton transfer in 3-hydroxy-gamma-pyrone in gas phase and protic solvent. © 2011 American Institute of Physics

Multilevel joint competing risk models

NASA Astrophysics Data System (ADS)

Karunarathna, G. H. S.; Sooriyarachchi, M. R.

2017-09-01

Joint modeling approaches are often encountered for different outcomes of competing risk time to event and count in many biomedical and epidemiology studies in the presence of cluster effect. Hospital length of stay (LOS) has been the widely used outcome measure in hospital utilization due to the benchmark measurement for measuring multiple terminations such as discharge, transferred, dead and patients who have not completed the event of interest at the follow up period (censored) during hospitalizations. Competing risk models provide a method of addressing such multiple destinations since classical time to event models yield biased results when there are multiple events. In this study, the concept of joint modeling has been applied to the dengue epidemiology in Sri Lanka, 2006-2008 to assess the relationship between different outcomes of LOS and platelet count of dengue patients with the district cluster effect. Two key approaches have been applied to build up the joint scenario. In the first approach, modeling each competing risk separately using the binary logistic model, treating all other events as censored under the multilevel discrete time to event model, while the platelet counts are assumed to follow a lognormal regression model. The second approach is based on the endogeneity effect in the multilevel competing risks and count model. Model parameters were estimated using maximum likelihood based on the Laplace approximation. Moreover, the study reveals that joint modeling approach yield more precise results compared to fitting two separate univariate models, in terms of AIC (Akaike Information Criterion).

A methodological approach to short-term tracking of youth physical fitness: the Oporto Growth, Health and Performance Study.

PubMed

Souza, Michele; Eisenmann, Joey; Chaves, Raquel; Santos, Daniel; Pereira, Sara; Forjaz, Cláudia; Maia, José

2016-10-01

In this paper, three different statistical approaches were used to investigate short-term tracking of cardiorespiratory and performance-related physical fitness among adolescents. Data were obtained from the Oporto Growth, Health and Performance Study and comprised 1203 adolescents (549 girls) divided into two age cohorts (10-12 and 12-14 years) followed for three consecutive years, with annual assessment. Cardiorespiratory fitness was assessed with 1-mile run/walk test; 50-yard dash, standing long jump, handgrip, and shuttle run test were used to rate performance-related physical fitness. Tracking was expressed in three different ways: auto-correlations, multilevel modelling with crude and adjusted model (for biological maturation, body mass index, and physical activity), and Cohen's Kappa (κ) computed in IBM SPSS 20.0, HLM 7.01 and Longitudinal Data Analysis software, respectively. Tracking of physical fitness components was (1) moderate-to-high when described by auto-correlations; (2) low-to-moderate when crude and adjusted models were used; and (3) low according to Cohen's Kappa (κ). These results demonstrate that when describing tracking, different methods should be considered since they provide distinct and more comprehensive views about physical fitness stability patterns.

Using multiple group modeling to test moderators in meta-analysis.

PubMed

Schoemann, Alexander M

2016-12-01

Meta-analysis is a popular and flexible analysis that can be fit in many modeling frameworks. Two methods of fitting meta-analyses that are growing in popularity are structural equation modeling (SEM) and multilevel modeling (MLM). By using SEM or MLM to fit a meta-analysis researchers have access to powerful techniques associated with SEM and MLM. This paper details how to use one such technique, multiple group analysis, to test categorical moderators in meta-analysis. In a multiple group meta-analysis a model is fit to each level of the moderator simultaneously. By constraining parameters across groups any model parameter can be tested for equality. Using multiple groups to test for moderators is especially relevant in random-effects meta-analysis where both the mean and the between studies variance of the effect size may be compared across groups. A simulation study and the analysis of a real data set are used to illustrate multiple group modeling with both SEM and MLM. Issues related to multiple group meta-analysis and future directions for research are discussed. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Evaluation of using R-SCHA to simultaneously model main field and secular variation multilevel geomagnetic data for the North Atlantic

NASA Astrophysics Data System (ADS)

Talarn, Àngela; Pavón-Carrasco, F. Javier; Torta, J. Miquel; Catalán, Manuel

2017-02-01

One efficient approach to modelling the Earth's core magnetic field involves the inclusion of crossover marine data which cover areas lacking in observatory and repeat station data for epochs when precise three-component satellite magnetic field measurements were not common. In this study, we show how the Revised Spherical Cap Harmonic Analysis (R-SCHA) can appropriately provide a continuous-time field model for the North Atlantic region by using multilevel sets of geomagnetic data such as marine, repeat station, observatory, and satellite data. Taking advantage of the properties of the R-SCHA basis functions we can model the radial and horizontal variations of the main field and its secular variation with the most suitable spatial and temporal wavelengths. To assess the best compromise between the data fit and the model roughness, temporal and spatial regularization matrices were implemented in the modelling approach. Two additional strategies were also used to obtain a satisfactory regional model: the opportunity to fit the anomaly bias at each observatory location, and constraining the regional model to the CHAOS-6 model at the end of its period of validity, i.e. 1999-2000, allowing a smooth transition with the predictions of this recent model. In terms of the root mean square error, the degree of success was limited partly because of the high uncertainties associated with some of the datasets (especially the marine ones), but we have produced a model that performs comparably to the global models for the period 1960-2000, thus showing the benefits of using this regional technique.

Exploring Person Fit with an Approach Based on Multilevel Logistic Regression

ERIC Educational Resources Information Center

Walker, A. Adrienne; Engelhard, George, Jr.

2015-01-01

The idea that test scores may not be valid representations of what students know, can do, and should learn next is well known. Person fit provides an important aspect of validity evidence. Person fit analyses at the individual student level are not typically conducted and person fit information is not communicated to educational stakeholders. In…

Assessing the multidimensional and hierarchical structure of SERVQUAL.

PubMed

Ma, Jun; Harvey, Milton E; Hu, Michael Y

2007-10-01

Parasuraman, Zeithaml, and Berry introduced SERVQUAL in 1998 as a scale to measure service quality. Since then, researchers have proposed several variations. This study examines the development of the tool. Marketing researchers have first challenged the conceptualization of a perceptions-expectations gap and have concluded that the performance-based measures are adequate to capture consumers' perception of service quality. Some researchers have argued that the five dimensions of the SERVQUAL scale only focus on the process of service delivery and have extended the SERVQUAL scale into six dimensions by including the service outcome dimension. Others have proposed that service quality is a multilevel construct and should be measured accordingly. From a sample of 467 undergraduate students data on service quality toward up-scale restaurants were collected. Using the structural equation approach, two measurement models of service quality were compared, the extended SERVQUAL model and the restructured multilevel SERVQUAL model. Analysis suggested that the latter model fits the data better than the extended one.

Friendship Dissolution Within Social Networks Modeled Through Multilevel Event History Analysis

PubMed Central

Dean, Danielle O.; Bauer, Daniel J.; Prinstein, Mitchell J.

2018-01-01

A social network perspective can bring important insight into the processes that shape human behavior. Longitudinal social network data, measuring relations between individuals over time, has become increasingly common—as have the methods available to analyze such data. A friendship duration model utilizing discrete-time multilevel survival analysis with a multiple membership random effect structure is developed and applied here to study the processes leading to undirected friendship dissolution within a larger social network. While the modeling framework is introduced in terms of understanding friendship dissolution, it can be used to understand microlevel dynamics of a social network more generally. These models can be fit with standard generalized linear mixed-model software, after transforming the data to a pair-period data set. An empirical example highlights how the model can be applied to understand the processes leading to friendship dissolution between high school students, and a simulation study is used to test the use of the modeling framework under representative conditions that would be found in social network data. Advantages of the modeling framework are highlighted, and potential limitations and future directions are discussed. PMID:28463022

Individual and institutional factors associated with functional disability in nursing home residents: An observational study with multilevel analysis.

PubMed

Serrano-Urrea, Ramón; Gómez-Rubio, Virgilio; Palacios-Ceña, Domingo; Fernández-de-Las-Peñas, César; García-Meseguer, María José

2017-01-01

High prevalence of functional limitations has been previously observed in nursing homes. Disability may depend not only on the characteristics of the residents but also on the facility characteristics. The aims of this study were: 1, to describe the prevalence of functional disability in older people living in Spanish nursing homes; and 2, to analyze the relationships between individual and nursing home characteristics and residents' functional disability. A cross-sectional study with data collected from 895 residents in 34 nursing homes in the province of Albacete (Spain) was conducted. Functional status was assessed by the Barthel Index. Taking into account both levels of data (individual and institutional characteristics) we resorted to a multilevel analysis in order to take different sources of variability in the data. The prevalence of functional disability of the total sample was 79.8%. The best fitting multilevel model showed that female gender, older age, negative self-perception of health, and living in private nursing homes were factors significantly associated with functional disability. After separating individual and institutional effects, the institutions showed significant differences. In line with previous findings, our study found high levels of functional dependence among institutionalized elders. Gender, age, self-perception of health, and institution ownership were associated with functional status. Disentangling individual and institutional effects by means of multilevel models can help evaluate the quality of the residences.

Longitudinal Study of Repeated Sprint Performance in Youth Soccer Players of Contrasting Skeletal Maturity Status

PubMed Central

Valente-dos-Santos, João; Coelho-e-Silva, Manuel J.; Severino, Vítor; Duarte, João; Martins, Raúl S.; Figueiredo, António J.; Seabra, André T.; Philippaerts, Renaat M.; Cumming, Sean P; Elferink-Gemser, Marije; Malina, Robert M.

2012-01-01

The purpose of the study was to evaluate the developmental changes in performance in a repeated-sprint ability (RSA) test in young soccer players of contrasting maturity status. A total of 83 regional level Portuguese youth soccer players, aged 11-13 years at baseline was assessed annually. Stature, body mass, 7x34.2-m sprint protocol (25-s active recovery), 20-m multi-stage continuous shuttle endurance run and counter-movement jump (CMJ) without the use of the arms were measured. Fat-free mass (FFM) was determined by age and gender-specific formulas. Developmental changes in total sprint time across ages were predicted using multilevel modeling. Corresponding measurements were performed on an independent cross-sectional subsample of 52 youth soccer players 11-17 years to evaluate the predictive model. CA, CA2, maturational status (SA-CA), body size (mass and stature), FFM, aerobic endurance, lower limb explosive strength and annual volume training significantly improved the statistical fit of the RSA multilevel model. In ‘late’ maturing athletes, the best model for predicting change in RSA was expressed by the following equation: 86.54 - 2.87 x CA + 0.05 x CA2 - 0.25 x FFM + 0.15 x body mass + 0.05 x stature - 0.05 x aerobic endurance - 0.09 x lower limb explosive strength - 0.01 x annual volume training. The best fitting models for players who were ‘on time’ and ‘early’ maturing were identical to the best model for late maturing players, less 0.64 seconds and 1.74 seconds, respectively. Multilevel modeling provided performance curves that permitted the prediction of individual RSA performance across adolescent years in regional level soccer players. Key pointsRepeated-sprint ability tests are a valuable sport-specific field test of sprint performance in youth soccer players. Here, the test had reasonable reliability and can be useful to trainers and coaches in the assessment of young athletes and in monitoring changes over time.The total sprint time of youth soccer players advanced in biological maturation improves more, on average, than that of players who are on time (average) and late in maturation. The performance difference between early and late maturing players is consistent after about 13 years of age.Multilevel modeling is a promising statistical technique for analyzing the development of functional capacity in a sport. It has the potential to provide useful information to assist trainers and coaches in evaluating and facilitating the development of individual players. PMID:24149342

The hitchhiker's guide to altruism: gene-culture coevolution, and the internalization of norms.

PubMed

Gintis, Herbert

2003-02-21

An internal norm is a pattern of behavior enforced in part by internal sanctions, such as shame, guilt and loss of self-esteem, as opposed to purely external sanctions, such as material rewards and punishment. The ability to internalize norms is widespread among humans, although in some so-called "sociopaths", this capacity is diminished or lacking. Suppose there is one genetic locus that controls the capacity to internalize norms. This model shows that if an internal norm is fitness enhancing, then for plausible patterns of socialization, the allele for internalization of norms is evolutionarily stable. This framework can be used to model Herbert Simon's (1990) explanation of altruism, showing that altruistic norms can "hitchhike" on the general tendency of internal norms to be personally fitness-enhancing. A multi-level selection, gene-culture coevolution argument then explains why individually fitness-reducing internal norms are likely to be prosocial as opposed to socially harmful.

Does inequality erode social trust? Results from multilevel models of US states and counties.

PubMed

Fairbrother, Malcolm; Martin, Isaac W

2013-03-01

Previous research has argued that income inequality reduces people's trust in other people, and that declining social trust in the United States in recent decades has been due to rising levels of income inequality. Using multilevel models fitted to data from the General Social Survey, this paper substantially qualifies these arguments. We show that while people are less trusting in US states with higher income inequality, this association holds only cross-sectionally, not longitudinally; since the 1970s, states experiencing larger increases in inequality have not suffered systematically larger declines in trust. For counties, there is no statistically significant relationship either cross-sectionally or longitudinally. There is therefore only limited empirical support for the argument that inequality influences generalized social trust; and the declining trust of recent decades certainly cannot be attributed to rising inequality. Copyright © 2012 Elsevier Inc. All rights reserved.

Use of multilevel logistic regression to identify the causes of differential item functioning.

PubMed

Balluerka, Nekane; Gorostiaga, Arantxa; Gómez-Benito, Juana; Hidalgo, María Dolores

2010-11-01

Given that a key function of tests is to serve as evaluation instruments and for decision making in the fields of psychology and education, the possibility that some of their items may show differential behaviour is a major concern for psychometricians. In recent decades, important progress has been made as regards the efficacy of techniques designed to detect this differential item functioning (DIF). However, the findings are scant when it comes to explaining its causes. The present study addresses this problem from the perspective of multilevel analysis. Starting from a case study in the area of transcultural comparisons, multilevel logistic regression is used: 1) to identify the item characteristics associated with the presence of DIF; 2) to estimate the proportion of variation in the DIF coefficients that is explained by these characteristics; and 3) to evaluate alternative explanations of the DIF by comparing the explanatory power or fit of different sequential models. The comparison of these models confirmed one of the two alternatives (familiarity with the stimulus) and rejected the other (the topic area) as being a cause of differential functioning with respect to the compared groups.

Multilevel context of depression in two American Indian tribes.

PubMed

Kaufman, Carol E; Beals, Janette; Croy, Calvin; Jiang, Luohua; Novins, Douglas K

2013-12-01

Depression is a major debilitating disease. For American Indians living in tribal reservations, who endure disproportionately high levels of stress and poverty often associated with depression, determining the patterns and correlates is key to appropriate clinical assessment and intervention development. Yet little attention has been given to the cultural context of correlates for depression, including the influence of family, cultural traditions or practices, or community conditions. We used data from a large representative psychiatric epidemiological study among American Indians in 2 reservation communities to estimate nested individual and multilevel models of past-year major depressive episode (MDE) accounting for family, cultural, and community conditions. We found that models including culturally informed individual-level measures significantly improved the model fit over demographics alone. We found significant community-level variation in the probability of past-year MDE diagnosis in 1 tribe even after accounting for individual-level characteristics. Accounting for culture, family, and community context will facilitate research, clinician assessment, and treatment of depression in diverse settings.

Multilevel Context of Depression in Two American Indian Tribes

PubMed Central

Kaufman, Carol E.; Beals, Janette; Croy, Calvin; Jiang, Luohua; Novins, Douglas K.

2015-01-01

Objective Depression is a major debilitating disease. For American Indians living in tribal reservations, who endure disproportionately high levels of stress and poverty often associated with depression, determining the patterns and correlates is key to appropriate clinical assessment and intervention development. Yet, little attention has been given to the cultural context of correlates for depression, including the influence of family, cultural traditions or practices, or community conditions. Method We used data from a large representative psychiatric epidemiological study among American Indians in two reservation communities to estimate nested individual and multilevel models of past-year Major Depressive Episode (MDE) accounting for family, cultural, and community conditions. Results We found that models including culturally informed individual-level measures significantly improved the model fit over demographics alone. We found significant community-level variation in the probability of past-year MDE diagnosis in one tribe even after accounting for individual-level characteristics. Conclusions Accounting for culture, family, and community context will facilitate research, clinician assessment, and treatment of depression in diverse settings. PMID:24016293

[A multilevel model analysis of correlation between population characteristics and work ability of employees].

PubMed

Zhang, Lei; Huang, Chunping; Lan, Yajia; Wang, Mianzhen

2015-12-01

To analyze the correlation between population characteristics and work ability of employees with a multilevel model, to investigate the important influencing factors for work ability, and to provide a basis for improvement in work ability. Work ability index (WAI)was applied to measure the work ability of 1686 subjects from different companies (n=6). MLwi N2.0 software was applied for two-level variance component model fitting. The WAI of employees showed differences between various companies (χ2=3.378 6, P=0.0660); working years was negatively correlated with WAI (χ2=38.229 2, P=0.0001), and the WAI of the employees with 20 or more working years was 1.63 lower than that of the employees with less than 20 working years; the work ability of manual workers was lower than that of mental-manual workers (χ2=8.2726, P=0.0040), and the work ability showed no significant difference between mental workers and mental-manual workers (χ2=2.086 0, P=0.148 7). From the perspective of probability, the multilevel model analysis reveals the differences in work ability of employees between different companies, and suggests that company, work type, and working years are the important influencing factors for work ability of employees. These factors should be improved and adjusted to protect or enhance the work ability of employees.

Culture Matters in Successful Curriculum Change: An International Study of the Influence of National and Organizational Culture Tested With Multilevel Structural Equation Modeling.

PubMed

Jippes, Mariëlle; Driessen, Erik W; Broers, Nick J; Majoor, Gerard D; Gijselaers, Wim H; van der Vleuten, Cees P M

2015-07-01

National culture has been shown to play a role in curriculum change in medical schools, and business literature has described a similar influence of organizational culture on change processes in organizations. This study investigated the impact of both national and organizational culture on successful curriculum change in medical schools internationally. The authors tested a literature-based conceptual model using multilevel structural equation modeling. For the operationalization of national and organizational culture, the authors used Hofstede's dimensions of culture and Quinn and Spreitzer's competing values framework, respectively. To operationalize successful curriculum change, the authors used two derivates: medical schools' organizational readiness for curriculum change developed by Jippes and colleagues, and change-related behavior developed by Herscovitch and Meyer. The authors administered a questionnaire in 2012 measuring the described operationalizations to medical schools in the process of changing their curriculum. Nine hundred ninety-one of 1,073 invited staff members from 131 of 345 medical schools in 56 of 80 countries completed the questionnaire. An initial poor fit of the model improved to a reasonable fit by two suggested modifications which seemed theoretically plausible. In sum, characteristics of national culture and organizational culture, such as a certain level of risk taking, flexible policies and procedures, and strong leadership, affected successful curriculum change. National and organizational culture influence readiness for change in medical schools. Therefore, medical schools considering curriculum reform should anticipate the potential impact of national and organizational culture.

"Generality of mis-fit"? The real-life difficulty of matching scales in an interconnected world.

PubMed

Keskitalo, E Carina H; Horstkotte, Tim; Kivinen, Sonja; Forbes, Bruce; Käyhkö, Jukka

2016-10-01

A clear understanding of processes at multiple scales and levels is of special significance when conceiving strategies for human-environment interactions. However, understanding and application of the scale concept often differ between administrative-political and ecological disciplines. These mirror major differences in potential solutions whether and how scales can, at all, be made congruent. As a result, opportunities of seeking "goodness-of-fit" between different concepts of governance should perhaps be reconsidered in the light of a potential "generality of mis-fit." This article reviews the interdisciplinary considerations inherent in the concept of scale in its ecological, as well as administrative-political, significance and argues that issues of how to manage "mis-fit" should be awarded more emphasis in social-ecological research and management practices. These considerations are exemplified by the case of reindeer husbandry in Fennoscandia. Whilst an indigenous small-scale practice, reindeer husbandry involves multi-level ecological and administrative-political complexities-complexities that we argue may arise in any multi-level system.

The effects of changes in physical fitness on academic performance among New York City youth.

PubMed

Bezold, Carla P; Konty, Kevin J; Day, Sophia E; Berger, Magdalena; Harr, Lindsey; Larkin, Michael; Napier, Melanie D; Nonas, Cathy; Saha, Subir; Harris, Tiffany G; Stark, James H

2014-12-01

To evaluate whether a change in fitness is associated with academic outcomes in New York City (NYC) middle-school students using longitudinal data and to evaluate whether this relationship is modified by student household poverty. This was a longitudinal study of 83,111 New York City middle-school students enrolled between 2006-2007 and 2011-2012. Fitness was measured as a composite percentile based on three fitness tests and categorized based on change from the previous year. The effect of the fitness change level on academic outcomes, measured as a composite percentile based on state standardized mathematics and English Language Arts test scores, was estimated using a multilevel growth model. Models were stratified by sex, and additional models were tested stratified by student household poverty. For both girls and boys, a substantial increase in fitness from the previous year resulted in a greater improvement in academic ranking than was seen in the reference group (girls: .36 greater percentile point improvement, 95% confidence interval: .09-.63; boys: .38 greater percentile point improvement, 95% confidence interval: .09-.66). A substantial decrease in fitness was associated with a decrease in academics in both boys and girls. Effects of fitness on academics were stronger in high-poverty boys and girls than in low-poverty boys and girls. Academic rankings improved for boys and girls who increased their fitness level by >20 percentile points compared to other students. Opportunities for increased physical fitness may be important to support academic performance. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

The JOINT model of nurse absenteeism and turnover: a systematic review.

PubMed

Daouk-Öyry, Lina; Anouze, Abdel-Latef; Otaki, Farah; Dumit, Nuhad Yazbik; Osman, Ibrahim

2014-01-01

Absenteeism and turnover among healthcare workers have a significant impact on overall healthcare system performance. The literature captures variables from different levels of measurement and analysis as being associated with attendance behavior among nurses. Yet, it remains unclear how variables from different contextual levels interact to impact nurses' attendance behaviors. The purpose of this review is to develop an integrative multilevel framework that optimizes our understanding of absenteeism and turnover among nurses in hospital settings. We therefore systematically examine English-only studies retrieved from two major databases, PubMed and CINAHL Plus and published between January, 2007 and January, 2013 (inclusive). Our review led to the identification of 7619 articles out of which 41 matched the inclusion criteria. The analysis yielded a total of 91 antecedent variables and 12 outcome variables for turnover, and 29 antecedent variables and 9 outcome variables for absenteeism. The various manifested variables were analyzed using content analysis and grouped into 11 categories, and further into five main factors: Job, Organization, Individual, National and inTerpersonal (JOINT). Thus, we propose the JOINT multilevel conceptual model for investigating absenteeism and turnover among nurses. The JOINT model can be adapted by researchers for fitting their hypothesized multilevel relationships. It can also be used by nursing managers as a lens for holistically managing nurses' attendance behaviors. Copyright © 2013 Elsevier Ltd. All rights reserved.

Under multilevel selection: "when shall you be neither spiteful nor envious?".

PubMed

Garay, József; Csiszár, Villő; Móri, Tamás F

2014-01-07

In this paper, we study the egalitarianism-game in multilevel selection situation. The individuals form reproductive groups. In each group, an egalitarianism-game determines the number of juveniles of different phenotypes (spiteful, envious, neutral and donator). Before the juveniles form the next reproductive group, they have to survive either predators' attacks or a fight between two groups. We adopt the ESS definition of Maynard Smith to multilevel selection. Based on the "group size advantage" assumption (which claims that each juvenile's survival rate depends on the size of his own group, supposing that either the survival rate under predators' attacks is higher in larger groups, or in inter-group aggression usually the larger group wins) we found that when the survival probability has a massive effect on the average fitness, then "group fitness maximizing behavior" (in our case, either neutral or donator) has evolutionary advantage over "competitive behavior" (in our case, either spiteful or envious). © 2013 Elsevier Ltd. All rights reserved.

Multilevel analyses of school and children's characteristics associated with physical activity.

PubMed

Gomes, Thayse Natacha; dos Santos, Fernanda K; Zhu, Weimo; Eisenmann, Joey; Maia, José A R

2014-10-01

Children spend most of their awake time at school, and it is important to identify individual and school-level correlates of their physical activity (PA) levels. This study aimed to identify the between-school variability in Portuguese children PA and to investigate student and school PA correlates using multilevel modeling. The sample included 1075 Portuguese children of both sexes, aged 6-10 years, from 24 schools. Height and weight were measured and body mass index (BMI) was estimated. Physical activity was estimated using the Godin and Shephard questionnaire (total PA score was used); cardiorespiratory fitness was estimated with the 1-mile run/walk test. A structured inventory was used to access information about the school environment. A multilevel analysis (level-1: student-level; level-2: school-level) was used. Student-level variables (age, sex, 1-mile run/walk test) explained 7% of the 64% variance fraction of the individual-level PA; however, school context explained approximately 36% of the total PA variance. Variables included in the model (school size, school setting, playground area, frequency and duration of physical education class, and qualification of physical education teacher) are responsible for 80% of the context variance. School environment is an important correlate of PA among children, enhancing children's opportunities for being active and healthy. © 2014, American School Health Association.

Daily Fluctuation in Negative Affect for Family Caregivers of Individuals With Dementia

PubMed Central

Liu, Yin; Kim, Kyungmin; Almeida, David M.; Zarit, Steven H.

2017-01-01

Objective The study examined associations of intrinsic fluctuation in daily negative affect (i.e., depression and anger) with adult day service (ADS) use, daily experiences, and other caregiving characteristics. Methods This was an 8-day diary of 173 family caregivers of individuals with dementia. Multilevel models with common within-person variance were fit first to show average associations between daily stressors and mean level of daily affect. Then multilevel models with heterogeneous within-person variance were fit to test the hypotheses on associations between ADS use, daily experiences, and intrinsic fluctuation in daily affect. Results The study showed that, when the sum of ADS days was greater than average, there was a stabilizing effect of ADS use on caregivers’ within-person fluctuation in negative affect. Moreover, fewer daily stressors and greater-than-average daily care-related stressors, more positive events, not being a spouse, greater-than-average duration of caregiving, and less-than-average dependency of individuals with dementia on activities of daily living were associated with less fluctuation. Better sleep quality was associated with less intrinsic fluctuation in anger; and younger age and more years of education were associated with less intrinsic fluctuation in daily depression. Conclusions Because emotional stability has been argued as an aspect of emotional well-being in the general populations, intrinsic fluctuation of emotional experience was suggested as an outcome of evidence-based interventions for family caregivers. PMID:25365414

Multilevel Model Prediction

ERIC Educational Resources Information Center

Frees, Edward W.; Kim, Jee-Seon

2006-01-01

Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling--model disturbances, random coefficients, and future response…

Multi-level analysis in information systems research: the case of enterprise resource planning system usage in China

NASA Astrophysics Data System (ADS)

Sun, Yuan; Bhattacherjee, Anol

2011-11-01

Information technology (IT) usage within organisations is a multi-level phenomenon that is influenced by individual-level and organisational-level variables. Yet, current theories, such as the unified theory of acceptance and use of technology, describe IT usage as solely an individual-level phenomenon. This article postulates a model of organisational IT usage that integrates salient organisational-level variables such as user training, top management support and technical support within an individual-level model to postulate a multi-level model of IT usage. The multi-level model was then empirically validated using multi-level data collected from 128 end users and 26 managers in 26 firms in China regarding their use of enterprise resource planning systems and analysed using the multi-level structural equation modelling (MSEM) technique. We demonstrate the utility of MSEM analysis of multi-level data relative to the more common structural equation modelling analysis of single-level data and show how single-level data can be aggregated to approximate multi-level analysis when multi-level data collection is not possible. We hope that this article will motivate future scholars to employ multi-level data and multi-level analysis for understanding organisational phenomena that are truly multi-level in nature.

Multilevel principal component analysis (mPCA) in shape analysis: A feasibility study in medical and dental imaging.

PubMed

Farnell, D J J; Popat, H; Richmond, S

2016-06-01

Methods used in image processing should reflect any multilevel structures inherent in the image dataset or they run the risk of functioning inadequately. We wish to test the feasibility of multilevel principal components analysis (PCA) to build active shape models (ASMs) for cases relevant to medical and dental imaging. Multilevel PCA was used to carry out model fitting to sets of landmark points and it was compared to the results of "standard" (single-level) PCA. Proof of principle was tested by applying mPCA to model basic peri-oral expressions (happy, neutral, sad) approximated to the junction between the mouth/lips. Monte Carlo simulations were used to create this data which allowed exploration of practical implementation issues such as the number of landmark points, number of images, and number of groups (i.e., "expressions" for this example). To further test the robustness of the method, mPCA was subsequently applied to a dental imaging dataset utilising landmark points (placed by different clinicians) along the boundary of mandibular cortical bone in panoramic radiographs of the face. Changes of expression that varied between groups were modelled correctly at one level of the model and changes in lip width that varied within groups at another for the Monte Carlo dataset. Extreme cases in the test dataset were modelled adequately by mPCA but not by standard PCA. Similarly, variations in the shape of the cortical bone were modelled by one level of mPCA and variations between the experts at another for the panoramic radiographs dataset. Results for mPCA were found to be comparable to those of standard PCA for point-to-point errors via miss-one-out testing for this dataset. These errors reduce with increasing number of eigenvectors/values retained, as expected. We have shown that mPCA can be used in shape models for dental and medical image processing. mPCA was found to provide more control and flexibility when compared to standard "single-level" PCA. Specifically, mPCA is preferable to "standard" PCA when multiple levels occur naturally in the dataset. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Formulation and Application of the Generalized Multilevel Facets Model

ERIC Educational Resources Information Center

Wang, Wen-Chung; Liu, Chih-Yu

2007-01-01

In this study, the authors develop a generalized multilevel facets model, which is not only a multilevel and two-parameter generalization of the facets model, but also a multilevel and facet generalization of the generalized partial credit model. Because the new model is formulated within a framework of nonlinear mixed models, no efforts are…

The Effects of Educational Diversity in a National Sample of Law Students: Fitting Multilevel Latent Variable Models in Data With Categorical Indicators.

PubMed

Gottfredson, Nisha C; Panter, A T; Daye, Charles E; Allen, Walter F; Wightman, Linda F

2009-01-01

Controversy surrounding the use of race-conscious admissions can be partially resolved with improved empirical knowledge of the effects of racial diversity in educational settings. We use a national sample of law students nested in 64 law schools to test the complex and largely untested theory regarding the effects of educational diversity on student outcomes. Social scientists who study these outcomes frequently encounter both latent variables and nested data within a single analysis. Yet, until recently, an appropriate modeling technique has been computationally infeasible, and consequently few applied researchers have estimated appropriate models to test their theories, sometimes limiting the scope of their research question. Our results, based on disaggregated multilevel structural equation models, show that racial diversity is related to a reduction in prejudiced attitudes and increased perceived exposure to diverse ideas and that these effects are mediated by more frequent interpersonal contact with diverse peers. These findings provide support for the idea that administrative manipulation of educational diversity may lead to improved student outcomes. Admitting a racially/ethnically diverse student body provides an educational experience that encourages increased exposure to diverse ideas and belief systems.

Health-related quality of life among adults 65 years and older in the United States, 2011-2012: a multilevel small area estimation approach.

PubMed

Lin, Yu-Hsiu; McLain, Alexander C; Probst, Janice C; Bennett, Kevin J; Qureshi, Zaina P; Eberth, Jan M

2017-01-01

The purpose of this study was to develop county-level estimates of poor health-related quality of life (HRQOL) among aged 65 years and older U.S. adults and to identify spatial clusters of poor HRQOL using a multilevel, poststratification approach. Multilevel, random-intercept models were fit to HRQOL data (two domains: physical health and mental health) from the 2011-2012 Behavioral Risk Factor Surveillance System. Using a poststratification, small area estimation approach, we generated county-level probabilities of having poor HRQOL for each domain in U.S. adults aged 65 and older, and validated our model-based estimates against state and county direct estimates. County-level estimates of poor HRQOL in the United States ranged from 18.07% to 44.81% for physical health and 14.77% to 37.86% for mental health. Correlations between model-based and direct estimates were higher for physical than mental HRQOL. Counties located in the Arkansas, Kentucky, and Mississippi exhibited the worst physical HRQOL scores, but this pattern did not hold for mental HRQOL, which had the highest probability of mentally unhealthy days in Illinois, Indiana, and Vermont. Substantial geographic variation in physical and mental HRQOL scores exists among older U.S. adults. State and local policy makers should consider these local conditions in targeting interventions and policies to counties with high levels of poor HRQOL scores. Copyright © 2016 Elsevier Inc. All rights reserved.

Mössbauer and X-ray study of biodegradation of 57Fe3 O 4 magnetic nanoparticles in rat brain

NASA Astrophysics Data System (ADS)

Gabbasov, R. R.; Cherepanov, V. M.; Chuev, M. A.; Lomov, A. A.; Mischenko, I. N.; Nikitin, M. P.; Polikarpov, M. A.; Panchenko, V. Y.

2016-12-01

Biodegradation of a 57Fe3 O 4 - based dextran - stabilized ferrofluid in the ventricular cavities of the rat brain was studied by X-ray diffraction and Mössbauer spectroscopy. A two-step process of biodegradation, consisting of fast disintegration of the initial composite magnetic beads into separate superparamagnetic nanoparticles and subsequent slow dissolution of the nanoparticles has been found. Joint fitting of the couples of Mössbauer spectra measured at different temperatures in the formalism of multi-level relaxation model with one set of fitting parameters, allowed us to measure concentration of exogenous iron in the rat brain as a function of time after the injection of nanoparticles.

Income inequality is associated with adolescent fertility in Brazil: a longitudinal multilevel analysis of 5,565 municipalities.

PubMed

Chiavegatto Filho, Alexandre D P; Kawachi, Ichiro

2015-02-07

Brazil has one of the highest adolescent fertility rates in the world. Income inequality has been frequently linked to overall adolescent health, but studies that analyzed its association with adolescent fertility have been performed only in developed countries. Brazil, in the past decade, has presented a rare combination of increasing per capita income and decreasing income inequality, which could influence future desirable pathways for other countries. We analyzed every live birth from 2000 and from 2010 in each of the 5,565 municipalities of Brazil, a total of 6,049,864 births, which included 1,247,145 (20.6%) births from women aged 15 to 19. Income inequality was assessed by the Gini Coefficient and adolescent fertility by the ratio between the number of live births from women aged 15 to 19 and the number of women aged 15 to 19, calculated for each municipality. We first applied multilevel models separately for 2000 and 2010 to test the cross-sectional association between income inequality and adolescent fertility. We then fitted longitudinal first-differences multilevel models to control for time-invariant effects. We also performed a sensitivity analysis to include only municipality with satisfactory birth record coverage. Our results indicate a consistent and positive association between income inequality and adolescent fertility. After controlling for per capita income, college access, youth homicide rate and adult fertility, higher income inequality was significantly associated with higher adolescent fertility for both 2000 and 2010. The longitudinal multilevel models found similar results. The sensitivity analysis indicated that the results for the association between income inequality and adolescent fertility were robust. Adult fertility was also significantly associated with adolescent fertility in the cross-sectional and longitudinal models. Income inequality is expected to be a leading concern for most countries in the near future. Our results suggest that changes in income inequality are positively and consistently associated with changes in adolescent fertility.

Validation of a pre-existing safety climate scale for the Turkish furniture manufacturing industry.

PubMed

Akyuz, Kadri Cemil; Yildirim, Ibrahim; Gungor, Celal

2018-03-22

Understanding the safety climate level is essential to implement a proactive safety program. The objective of this study is to explore the possibility of having a safety climate scale for the Turkish furniture manufacturing industry since there has not been any scale available. The questionnaire recruited 783 subjects. Confirmatory factor analysis (CFA) tested a pre-existing safety scale's fit to the industry. The CFA indicated that the structures of the model present a non-satisfactory fit with the data (χ 2  = 2033.4, df = 314, p ≤ 0.001; root mean square error of approximation = 0.08, normed fit index = 0.65, Tucker-Lewis index = 0.65, comparative fit index = 0.69, parsimony goodness-of-fit index = 0.68). The results suggest that a new scale should be developed and validated to measure the safety climate level in the Turkish furniture manufacturing industry. Due to the hierarchical structure of organizations, future studies should consider a multilevel approach in their exploratory factor analyses while developing a new scale.

Multilevel algorithms for nonlinear optimization

NASA Technical Reports Server (NTRS)

Alexandrov, Natalia; Dennis, J. E., Jr.

1994-01-01

Multidisciplinary design optimization (MDO) gives rise to nonlinear optimization problems characterized by a large number of constraints that naturally occur in blocks. We propose a class of multilevel optimization methods motivated by the structure and number of constraints and by the expense of the derivative computations for MDO. The algorithms are an extension to the nonlinear programming problem of the successful class of local Brown-Brent algorithms for nonlinear equations. Our extensions allow the user to partition constraints into arbitrary blocks to fit the application, and they separately process each block and the objective function, restricted to certain subspaces. The methods use trust regions as a globalization strategy, and they have been shown to be globally convergent under reasonable assumptions. The multilevel algorithms can be applied to all classes of MDO formulations. Multilevel algorithms for solving nonlinear systems of equations are a special case of the multilevel optimization methods. In this case, they can be viewed as a trust-region globalization of the Brown-Brent class.

Mapping Regional Impervious Surface Distribution from Night Time Light: The Variability across Global Cities

NASA Astrophysics Data System (ADS)

Lin, M.; Yang, Z.; Park, H.; Qian, S.; Chen, J.; Fan, P.

2017-12-01

Impervious surface area (ISA) has become an important indicator for studying urban environments, but mapping ISA at the regional or global scale is still challenging due to the complexity of impervious surface features. The Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime light data is (NTL) and Resolution Imaging Spectroradiometer (MODIS) are the major remote sensing data source for regional ISA mapping. A single regression relationship between fractional ISA and NTL or various index derived based on NTL and MODIS vegetation index (NDVI) data was established in many previous studies for regional ISA mapping. However, due to the varying geographical, climatic, and socio-economic characteristics of different cities, the same regression relationship may vary significantly across different cities in the same region in terms of both fitting performance (i.e. R2) and the rate of change (Slope). In this study, we examined the regression relationship between fractional ISA and Vegetation Adjusted Nighttime light Urban Index (VANUI) for 120 randomly selected cities around the world with a multilevel regression model. We found that indeed there is substantial variability of both the R2 (0.68±0.29) and slopes (0.64±0.40) among individual regressions, which suggests that multilevel/hierarchical models are needed for accuracy improvement of future regional ISA mapping .Further analysis also let us find the this substantial variability are affected by climate conditions, socio-economic status, and urban spatial structures. However, all these effects are nonlinear rather than linear, thus could not modeled explicitly in multilevel linear regression models.

Methodological quality and reporting of generalized linear mixed models in clinical medicine (2000-2012): a systematic review.

PubMed

Casals, Martí; Girabent-Farrés, Montserrat; Carrasco, Josep L

2014-01-01

Modeling count and binary data collected in hierarchical designs have increased the use of Generalized Linear Mixed Models (GLMMs) in medicine. This article presents a systematic review of the application and quality of results and information reported from GLMMs in the field of clinical medicine. A search using the Web of Science database was performed for published original articles in medical journals from 2000 to 2012. The search strategy included the topic "generalized linear mixed models","hierarchical generalized linear models", "multilevel generalized linear model" and as a research domain we refined by science technology. Papers reporting methodological considerations without application, and those that were not involved in clinical medicine or written in English were excluded. A total of 443 articles were detected, with an increase over time in the number of articles. In total, 108 articles fit the inclusion criteria. Of these, 54.6% were declared to be longitudinal studies, whereas 58.3% and 26.9% were defined as repeated measurements and multilevel design, respectively. Twenty-two articles belonged to environmental and occupational public health, 10 articles to clinical neurology, 8 to oncology, and 7 to infectious diseases and pediatrics. The distribution of the response variable was reported in 88% of the articles, predominantly Binomial (n = 64) or Poisson (n = 22). Most of the useful information about GLMMs was not reported in most cases. Variance estimates of random effects were described in only 8 articles (9.2%). The model validation, the method of covariate selection and the method of goodness of fit were only reported in 8.0%, 36.8% and 14.9% of the articles, respectively. During recent years, the use of GLMMs in medical literature has increased to take into account the correlation of data when modeling qualitative data or counts. According to the current recommendations, the quality of reporting has room for improvement regarding the characteristics of the analysis, estimation method, validation, and selection of the model.

Overweight and obesity in India: policy issues from an exploratory multi-level analysis.

PubMed

Siddiqui, Md Zakaria; Donato, Ronald

2016-06-01

This article analyses a nationally representative household dataset-the National Family Health Survey (NFHS-3) conducted in 2005 to 2006-to examine factors influencing the prevalence of overweight/obesity in India. The dataset was disaggregated into four sub-population groups-urban and rural females and males-and multi-level logit regression models were used to estimate the impact of particular covariates on the likelihood of overweight/obesity. The multi-level modelling approach aimed to identify individual and macro-level contextual factors influencing this health outcome. In contrast to most studies on low-income developing countries, the findings reveal that education for females beyond a particular level of educational attainment exhibits a negative relationship with the likelihood of overweight/obesity. This relationship was not observed for males. Muslim females and all Sikh sub-populations have a higher likelihood of overweight/obesity suggesting the importance of socio-cultural influences. The results also show that the relationship between wealth and the probability of overweight/obesity is stronger for males than females highlighting the differential impact of increasing socio-economic status on gender. Multi-level analysis reveals that states exerted an independent influence on the likelihood of overweight/obesity beyond individual-level covariates, reflecting the importance of spatially related contextual factors on overweight/obesity. While this study does not disentangle macro-level 'obesogenic' environmental factors from socio-cultural network influences, the results highlight the need to refrain from adopting a 'one size fits all' policy approach in addressing the overweight/obesity epidemic facing India. Instead, policy implementation requires a more nuanced and targeted approach to incorporate the growing recognition of socio-cultural and spatial contextual factors impacting on healthy behaviours. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Data Mining of Web-Based Documents on Social Networking Sites That Included Suicide-Related Words Among Korean Adolescents.

PubMed

Song, Juyoung; Song, Tae Min; Seo, Dong-Chul; Jin, Jae Hyun

2016-12-01

To investigate online search activity of suicide-related words in South Korean adolescents through data mining of social media Web sites as the suicide rate in South Korea is one of the highest in the world. Out of more than 2.35 billion posts for 2 years from January 1, 2011 to December 31, 2012 on 163 social media Web sites in South Korea, 99,693 suicide-related documents were retrieved by Crawler and analyzed using text mining and opinion mining. These data were further combined with monthly employment rate, monthly rental prices index, monthly youth suicide rate, and monthly number of reported bully victims to fit multilevel models as well as structural equation models. The link from grade pressure to suicide risk showed the largest standardized path coefficient (beta = .357, p < .001) in structural models and a significant random effect (p < .01) in multilevel models. Depression was a partial mediator between suicide risk and grade pressure, low body image, victims of bullying, and concerns about disease. The largest total effect was observed in the grade pressure to depression to suicide risk. The multilevel models indicate about 27% of the variance in the daily suicide-related word search activity is explained by month-to-month variations. A lower employment rate, a higher rental prices index, and more bullying were associated with an increased suicide-related word search activity. Academic pressure appears to be the biggest contributor to Korean adolescents' suicide risk. Real-time suicide-related word search activity monitoring and response system needs to be developed. Copyright © 2016 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

The dynamics of sex ratio evolution: from the gene perspective to multilevel selection.

PubMed

Argasinski, Krzysztof

2013-01-01

The new dynamical game theoretic model of sex ratio evolution emphasizes the role of males as passive carriers of sex ratio genes. This shows inconsistency between population genetic models of sex ratio evolution and classical strategic models. In this work a novel technique of change of coordinates will be applied to the new model. This will reveal new aspects of the modelled phenomenon which cannot be shown or proven in the original formulation. The underlying goal is to describe the dynamics of selection of particular genes in the entire population, instead of in the same sex subpopulation, as in the previous paper and earlier population genetics approaches. This allows for analytical derivation of the unbiased strategic model from the model with rigorous non-simplified genetics. In effect, an alternative system of replicator equations is derived. It contains two subsystems: the first describes changes in gene frequencies (this is an alternative unbiased formalization of the Fisher-Dusing argument), whereas the second describes changes in the sex ratios in subpopulations of carriers of genes for each strategy. An intriguing analytical result of this work is that the fitness of a gene depends on the current sex ratio in the subpopulation of its carriers, not on the encoded individual strategy. Thus, the argument of the gene fitness function is not constant but is determined by the trajectory of the sex ratio among carriers of that gene. This aspect of the modelled phenomenon cannot be revealed by the static analysis. Dynamics of the sex ratio among gene carriers is driven by a dynamic "tug of war" between female carriers expressing the encoded strategic trait value and random partners of male carriers expressing the average population strategy (a primary sex ratio). This mechanism can be called "double-level selection". Therefore, gene interest perspective leads to multi-level selection.

Addictive internet use among Korean adolescents: a national survey.

PubMed

Heo, Jongho; Oh, Juhwan; Subramanian, S V; Kim, Yoon; Kawachi, Ichiro

2014-01-01

A psychological disorder called 'Internet addiction' has newly emerged along with a dramatic increase of worldwide Internet use. However, few studies have used population-level samples nor taken into account contextual factors on Internet addiction. We identified 57,857 middle and high school students (13-18 year olds) from a Korean nationally representative survey, which was surveyed in 2009. To identify associated factors with addictive Internet use, two-level multilevel regression models were fitted with individual-level responses (1st level) nested within schools (2nd level) to estimate associations of individual and school characteristics simultaneously. Gender differences of addictive Internet use were estimated with the regression model stratified by gender. Significant associations were found between addictive Internet use and school grade, parental education, alcohol use, tobacco use, and substance use. Female students in girls' schools were more likely to use Internet addictively than those in coeducational schools. Our results also revealed significant gender differences of addictive Internet use in its associated individual- and school-level factors. Our results suggest that multilevel risk factors along with gender differences should be considered to protect adolescents from addictive Internet use.

Multilevel SEM Strategies for Evaluating Mediation in Three-Level Data

ERIC Educational Resources Information Center

Preacher, Kristopher J.

2011-01-01

Strategies for modeling mediation effects in multilevel data have proliferated over the past decade, keeping pace with the demands of applied research. Approaches for testing mediation hypotheses with 2-level clustered data were first proposed using multilevel modeling (MLM) and subsequently using multilevel structural equation modeling (MSEM) to…

The socio-genetics of a complex society: female gelada relatedness patterns mirror association patterns in a multilevel society.

PubMed

Snyder-Mackler, Noah; Alberts, Susan C; Bergman, Thore J

2014-12-01

Multilevel societies with fission-fusion dynamics--arguably the most complex animal societies--are defined by two or more nested levels of organization. The core of these societies are modular social units that regularly fission and fuse with one another. Despite convergent evolution in disparate taxa, we know strikingly little about how such societies form and how fitness benefits operate. Understanding the kinship structure of complex societies could inform us about the origins of the social structure as well as about the potential for individuals in these societies to accrue indirect fitness benefits. Here, we combined genetic and behavioural data on geladas (Theropithecus gelada), an Old World Monkey, to complete the most comprehensive socio-genetic analysis of a multilevel society to date. In geladas, individuals in the core social 'units', associate at different frequencies to form 'teams', 'bands' and, the largest aggregations, 'communities'. Units were composed of closely related females, and females remained with their close kin during permanent fissions of units. Interestingly, female-female relatedness also significantly predicted between-unit, between-team and between-band association patterns, while male-male relatedness did not. Thus, it is likely that the socio-genetic structure of gelada society results from females maintaining associations with their female relatives during successive unit fissions--possibly in an attempt to balance the direct and indirect fitness benefits of group living. Overall, the persistence of associations among related females across generations appears to drive the formation of higher levels of gelada society, suggesting that females seek kin for inclusive fitness benefits at multiple levels of gelada society. © 2014 John Wiley & Sons Ltd.

Objectively measured sedentary time and academic achievement in schoolchildren.

PubMed

Lopes, Luís; Santos, Rute; Mota, Jorge; Pereira, Beatriz; Lopes, Vítor

2017-03-01

This study aimed to evaluate the relationship between objectively measured total sedentary time and academic achievement (AA) in Portuguese children. The sample comprised of 213 children (51.6% girls) aged 9.46 ± 0.43 years, from the north of Portugal. Sedentary time was measured with accelerometry, and AA was assessed using the Portuguese Language and Mathematics National Exams results. Multilevel linear regression models were fitted to assess regression coefficients predicting AA. The results showed that objectively measured total sedentary time was not associated with AA, after adjusting for potential confounders.

Regional Cultures and the Psychological Geography of Switzerland: Person–Environment–Fit in Personality Predicts Subjective Wellbeing

PubMed Central

Götz, Friedrich M.; Ebert, Tobias; Rentfrow, Peter J.

2018-01-01

The present study extended traditional nation-based research on person–culture–fit to the regional level. First, we examined the geographical distribution of Big Five personality traits in Switzerland. Across the 26 Swiss cantons, unique patterns were observed for all traits. For Extraversion and Neuroticism clear language divides emerged between the French- and Italian-speaking South-West vs. the German-speaking North-East. Second, multilevel modeling demonstrated that person–environment–fit in Big Five, composed of elevation (i.e., mean differences between individual profile and cantonal profile), scatter (differences in mean variances) and shape (Pearson correlations between individual and cantonal profiles across all traits; Furr, 2008, 2010), predicted the development of subjective wellbeing (i.e., life satisfaction, satisfaction with personal relationships, positive affect, negative affect) over a period of 4 years. Unexpectedly, while the effects of shape were in line with the person–environment–fit hypothesis (better fit predicted higher subjective wellbeing), the effects of scatter showed the opposite pattern, while null findings were observed for elevation. Across a series of robustness checks, the patterns for shape and elevation were consistently replicated. While that was mostly the case for scatter as well, the effects of scatter appeared to be somewhat less robust and more sensitive to the specific way fit was modeled when predicting certain outcomes (negative affect, positive affect). Distinguishing between supplementary and complementary fit may help to reconcile these findings and future research should explore whether and if so under which conditions these concepts may be applicable to the respective facets of person–culture–fit. PMID:29713299

Regional Cultures and the Psychological Geography of Switzerland: Person-Environment-Fit in Personality Predicts Subjective Wellbeing.

PubMed

Götz, Friedrich M; Ebert, Tobias; Rentfrow, Peter J

2018-01-01

The present study extended traditional nation-based research on person-culture-fit to the regional level. First, we examined the geographical distribution of Big Five personality traits in Switzerland. Across the 26 Swiss cantons, unique patterns were observed for all traits. For Extraversion and Neuroticism clear language divides emerged between the French- and Italian-speaking South-West vs. the German-speaking North-East. Second, multilevel modeling demonstrated that person-environment-fit in Big Five, composed of elevation (i.e., mean differences between individual profile and cantonal profile), scatter (differences in mean variances) and shape (Pearson correlations between individual and cantonal profiles across all traits; Furr, 2008, 2010), predicted the development of subjective wellbeing (i.e., life satisfaction, satisfaction with personal relationships, positive affect, negative affect) over a period of 4 years. Unexpectedly, while the effects of shape were in line with the person-environment-fit hypothesis (better fit predicted higher subjective wellbeing), the effects of scatter showed the opposite pattern, while null findings were observed for elevation. Across a series of robustness checks, the patterns for shape and elevation were consistently replicated. While that was mostly the case for scatter as well, the effects of scatter appeared to be somewhat less robust and more sensitive to the specific way fit was modeled when predicting certain outcomes (negative affect, positive affect). Distinguishing between supplementary and complementary fit may help to reconcile these findings and future research should explore whether and if so under which conditions these concepts may be applicable to the respective facets of person-culture-fit.

A spatially filtered multilevel model to account for spatial dependency: application to self-rated health status in South Korea

PubMed Central

2014-01-01

Background This study aims to suggest an approach that integrates multilevel models and eigenvector spatial filtering methods and apply it to a case study of self-rated health status in South Korea. In many previous health-related studies, multilevel models and single-level spatial regression are used separately. However, the two methods should be used in conjunction because the objectives of both approaches are important in health-related analyses. The multilevel model enables the simultaneous analysis of both individual and neighborhood factors influencing health outcomes. However, the results of conventional multilevel models are potentially misleading when spatial dependency across neighborhoods exists. Spatial dependency in health-related data indicates that health outcomes in nearby neighborhoods are more similar to each other than those in distant neighborhoods. Spatial regression models can address this problem by modeling spatial dependency. This study explores the possibility of integrating a multilevel model and eigenvector spatial filtering, an advanced spatial regression for addressing spatial dependency in datasets. Methods In this spatially filtered multilevel model, eigenvectors function as additional explanatory variables accounting for unexplained spatial dependency within the neighborhood-level error. The specification addresses the inability of conventional multilevel models to account for spatial dependency, and thereby, generates more robust outputs. Results The findings show that sex, employment status, monthly household income, and perceived levels of stress are significantly associated with self-rated health status. Residents living in neighborhoods with low deprivation and a high doctor-to-resident ratio tend to report higher health status. The spatially filtered multilevel model provides unbiased estimations and improves the explanatory power of the model compared to conventional multilevel models although there are no changes in the signs of parameters and the significance levels between the two models in this case study. Conclusions The integrated approach proposed in this paper is a useful tool for understanding the geographical distribution of self-rated health status within a multilevel framework. In future research, it would be useful to apply the spatially filtered multilevel model to other datasets in order to clarify the differences between the two models. It is anticipated that this integrated method will also out-perform conventional models when it is used in other contexts. PMID:24571639

Multilevel Modeling of Social Segregation

ERIC Educational Resources Information Center

Leckie, George; Pillinger, Rebecca; Jones, Kelvyn; Goldstein, Harvey

2012-01-01

The traditional approach to measuring segregation is based upon descriptive, non-model-based indices. A recently proposed alternative is multilevel modeling. The authors further develop the argument for a multilevel modeling approach by first describing and expanding upon its notable advantages, which include an ability to model segregation at a…

Examining the Multi-level Fit between Work and Technology in a Secure Messaging Implementation.

PubMed

Ozkaynak, Mustafa; Johnson, Sharon; Shimada, Stephanie; Petrakis, Beth Ann; Tulu, Bengisu; Archambeault, Cliona; Fix, Gemmae; Schwartz, Erin; Woods, Susan

2014-01-01

Secure messaging (SM) allows patients to communicate with their providers for non-urgent health issues. Like other health information technologies, the design and implementation of SM should account for workflow to avoid suboptimal outcomes. SM may present unique workflow challenges because patients add a layer of complexity, as they are also direct users of the system. This study explores SM implementation at two Veterans Health Administration facilities. We interviewed twenty-nine members of eight primary care teams using semi-structured interviews. Questions addressed staff opinions about the integration of SM with daily practice, and team members' attitudes and experiences with SM. We describe the clinical workflow for SM, examining complexity and variability. We identified eight workflow issues directly related to efficiency and patient satisfaction, based on an exploration of the technology fit with multilevel factors. These findings inform organizational interventions that will accommodate SM implementation and lead to more patient-centered care.

Application of Hierarchical Linear Models/Linear Mixed-Effects Models in School Effectiveness Research

ERIC Educational Resources Information Center

Ker, H. W.

2014-01-01

Multilevel data are very common in educational research. Hierarchical linear models/linear mixed-effects models (HLMs/LMEs) are often utilized to analyze multilevel data nowadays. This paper discusses the problems of utilizing ordinary regressions for modeling multilevel educational data, compare the data analytic results from three regression…

Testing Group Mean Differences of Latent Variables in Multilevel Data Using Multiple-Group Multilevel CFA and Multilevel MIMIC Modeling.

PubMed

Kim, Eun Sook; Cao, Chunhua

2015-01-01

Considering that group comparisons are common in social science, we examined two latent group mean testing methods when groups of interest were either at the between or within level of multilevel data: multiple-group multilevel confirmatory factor analysis (MG ML CFA) and multilevel multiple-indicators multiple-causes modeling (ML MIMIC). The performance of these methods were investigated through three Monte Carlo studies. In Studies 1 and 2, either factor variances or residual variances were manipulated to be heterogeneous between groups. In Study 3, which focused on within-level multiple-group analysis, six different model specifications were considered depending on how to model the intra-class group correlation (i.e., correlation between random effect factors for groups within cluster). The results of simulations generally supported the adequacy of MG ML CFA and ML MIMIC for multiple-group analysis with multilevel data. The two methods did not show any notable difference in the latent group mean testing across three studies. Finally, a demonstration with real data and guidelines in selecting an appropriate approach to multilevel multiple-group analysis are provided.

Multilevel Modeling: A Review of Methodological Issues and Applications

ERIC Educational Resources Information Center

Dedrick, Robert F.; Ferron, John M.; Hess, Melinda R.; Hogarty, Kristine Y.; Kromrey, Jeffrey D.; Lang, Thomas R.; Niles, John D.; Lee, Reginald S.

2009-01-01

This study analyzed the reporting of multilevel modeling applications of a sample of 99 articles from 13 peer-reviewed journals in education and the social sciences. A checklist, derived from the methodological literature on multilevel modeling and focusing on the issues of model development and specification, data considerations, estimation, and…

Building Path Diagrams for Multilevel Models

ERIC Educational Resources Information Center

Curran, Patrick J.; Bauer, Daniel J.

2007-01-01

Multilevel models have come to play an increasingly important role in many areas of social science research. However, in contrast to other modeling strategies, there is currently no widely used approach for graphically diagramming multilevel models. Ideally, such diagrams would serve two functions: to provide a formal structure for deriving the…

Modeling Intraindividual Dynamics Using Stochastic Differential Equations: Age Differences in Affect Regulation.

PubMed

Wood, Julie; Oravecz, Zita; Vogel, Nina; Benson, Lizbeth; Chow, Sy-Miin; Cole, Pamela; Conroy, David E; Pincus, Aaron L; Ram, Nilam

2017-12-15

Life-span theories of aging suggest improvements and decrements in individuals' ability to regulate affect. Dynamic process models, with intensive longitudinal data, provide new opportunities to articulate specific theories about individual differences in intraindividual dynamics. This paper illustrates a method for operationalizing affect dynamics using a multilevel stochastic differential equation (SDE) model, and examines how those dynamics differ with age and trait-level tendencies to deploy emotion regulation strategies (reappraisal and suppression). Univariate multilevel SDE models, estimated in a Bayesian framework, were fit to 21 days of ecological momentary assessments of affect valence and arousal (average 6.93/day, SD = 1.89) obtained from 150 adults (age 18-89 years)-specifically capturing temporal dynamics of individuals' core affect in terms of attractor point, reactivity to biopsychosocial (BPS) inputs, and attractor strength. Older age was associated with higher arousal attractor point and less BPS-related reactivity. Greater use of reappraisal was associated with lower valence attractor point. Intraindividual variability in regulation strategy use was associated with greater BPS-related reactivity and attractor strength, but in different ways for valence and arousal. The results highlight the utility of SDE models for studying affect dynamics and informing theoretical predictions about how intraindividual dynamics change over the life course. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Hurdle models for multilevel zero-inflated data via h-likelihood.

PubMed

Molas, Marek; Lesaffre, Emmanuel

2010-12-30

Count data often exhibit overdispersion. One type of overdispersion arises when there is an excess of zeros in comparison with the standard Poisson distribution. Zero-inflated Poisson and hurdle models have been proposed to perform a valid likelihood-based analysis to account for the surplus of zeros. Further, data often arise in clustered, longitudinal or multiple-membership settings. The proper analysis needs to reflect the design of a study. Typically random effects are used to account for dependencies in the data. We examine the h-likelihood estimation and inference framework for hurdle models with random effects for complex designs. We extend the h-likelihood procedures to fit hurdle models, thereby extending h-likelihood to truncated distributions. Two applications of the methodology are presented. Copyright © 2010 John Wiley & Sons, Ltd.

Exploring the relations among physical fitness, executive functioning, and low academic achievement.

PubMed

de Bruijn, A G M; Hartman, E; Kostons, D; Visscher, C; Bosker, R J

2018-03-01

Physical fitness seems to be related to academic performance, at least when taking the role of executive functioning into account. This assumption is highly relevant for the vulnerable population of low academic achievers because their academic performance might benefit from enhanced physical fitness. The current study examined whether physical fitness and executive functioning are independent predictors of low mathematics and spelling achievement or whether the relation between physical fitness and low achievement is mediated by specific executive functions. In total, 477 students from second- and third-grade classes of 12 primary schools were classified as either low or average-to-high achievers in mathematics and spelling based on their scores on standardized achievement tests. Multilevel structural equation models were built with direct paths between physical fitness and academic achievement and added indirect paths via components of executive functioning: inhibition, verbal working memory, visuospatial working memory, and shifting. Physical fitness was only indirectly related to low achievement via specific executive functions, depending on the academic domain involved. Verbal working memory was a mediator between physical fitness and low achievement in both domains, whereas visuospatial working memory had a mediating role only in mathematics. Physical fitness interventions aiming to improve low academic achievement, thus, could potentially be successful. The mediating effect of executive functioning suggests that these improvements in academic achievement will be preceded by enhanced executive functions, either verbal working memory (in spelling) or both verbal and visuospatial working memory (in mathematics). Copyright © 2017 Elsevier Inc. All rights reserved.

Nurses' practice environment and work-family conflict in relation to burn out: a multilevel modelling approach.

PubMed

Leineweber, Constanze; Westerlund, Hugo; Chungkham, Holendro Singh; Lindqvist, Rikard; Runesdotter, Sara; Tishelman, Carol

2014-01-01

To investigate associations between nurse work practice environment measured at department level and individual level work-family conflict on burnout, measured as emotional exhaustion, depersonalization and personal accomplishment among Swedish RNs. A multilevel model was fit with the individual RN at the 1st, and the hospital department at the 2nd level using cross-sectional RN survey data from the Swedish part of RN4CAST, an EU 7th framework project. The data analysed here is based on a national sample of 8,620 RNs from 369 departments in 53 hospitals. Generally, RNs reported high values of personal accomplishment and lower values of emotional exhaustion and depersonalization. High work-family conflict increased the risk for emotional exhaustion, but for neither depersonalization nor personal accomplishment. On department level adequate staffing and good leadership and support for nurses reduced the risk for emotional exhaustion and depersonalization. Personal accomplishment was statistically significantly related to staff adequacy. The findings suggest that adequate staffing, good leadership, and support for nurses are crucial for RNs' mental health. Our findings also highlight the importance of hospital managers developing policies and practices to facilitate the successful combination of work with private life for employees.

Income inequality and high blood pressure in Colombia: a multilevel analysis.

PubMed

Lucumi, Diego I; Schulz, Amy J; Roux, Ana V Diez; Grogan-Kaylor, Andrew

2017-11-21

The objective of this research was to examine the association between income inequality and high blood pressure in Colombia. Using a nationally representative Colombian sample of adults, and data from departments and municipalities, we fit sex-stratified linear and logistic multilevel models with blood pressure as a continuous and binary variable, respectively. In adjusted models, women living in departments with the highest quintile of income inequality in 1997 had higher systolic blood pressure than their counterparts living in the lowest quintile of income inequality (mean difference 4.42mmHg; 95%CI: 1.46, 7.39). Women living in departments that were at the fourth and fifth quintile of income inequality in 1994 were more likely to have hypertension than those living in departments at the first quintile in the same year (OR: 1.56 and 1.48, respectively). For men, no associations of income inequality with either systolic blood pressure or hypertension were observed. Our findings are consistent with the hypothesis that income inequality is associated with increased risk of high blood pressure for women. Future studies to analyze pathways linking income inequality to high blood pressure in Colombia are needed.

Addictive Internet Use among Korean Adolescents: A National Survey

PubMed Central

Heo, Jongho; Oh, Juhwan; Subramanian, S. V.; Kim, Yoon; Kawachi, Ichiro

2014-01-01

Background A psychological disorder called ‘Internet addiction’ has newly emerged along with a dramatic increase of worldwide Internet use. However, few studies have used population-level samples nor taken into account contextual factors on Internet addiction. Methods and Findings We identified 57,857 middle and high school students (13–18 year olds) from a Korean nationally representative survey, which was surveyed in 2009. To identify associated factors with addictive Internet use, two-level multilevel regression models were fitted with individual-level responses (1st level) nested within schools (2nd level) to estimate associations of individual and school characteristics simultaneously. Gender differences of addictive Internet use were estimated with the regression model stratified by gender. Significant associations were found between addictive Internet use and school grade, parental education, alcohol use, tobacco use, and substance use. Female students in girls' schools were more likely to use Internet addictively than those in coeducational schools. Our results also revealed significant gender differences of addictive Internet use in its associated individual- and school-level factors. Conclusions Our results suggest that multilevel risk factors along with gender differences should be considered to protect adolescents from addictive Internet use. PMID:24505318

Detecting Differential Item Discrimination (DID) and the Consequences of Ignoring DID in Multilevel Item Response Models

ERIC Educational Resources Information Center

Lee, Woo-yeol; Cho, Sun-Joo

2017-01-01

Cross-level invariance in a multilevel item response model can be investigated by testing whether the within-level item discriminations are equal to the between-level item discriminations. Testing the cross-level invariance assumption is important to understand constructs in multilevel data. However, in most multilevel item response model…

Alternative Methods for Assessing Mediation in Multilevel Data: The Advantages of Multilevel SEM

ERIC Educational Resources Information Center

Preacher, Kristopher J.; Zhang, Zhen; Zyphur, Michael J.

2011-01-01

Multilevel modeling (MLM) is a popular way of assessing mediation effects with clustered data. Two important limitations of this approach have been identified in prior research and a theoretical rationale has been provided for why multilevel structural equation modeling (MSEM) should be preferred. However, to date, no empirical evidence of MSEM's…

Trimethoprim and ciprofloxacin resistance and prescribing in urinary tract infection associated with Escherichia coli: a multilevel model.

PubMed

Vellinga, Akke; Tansey, Sana; Hanahoe, Belinda; Bennett, Kathleen; Murphy, Andrew W; Cormican, Martin

2012-10-01

Individual and group level factors associated with the probability of antimicrobial resistance of uropathogenic Escherichia coli were analysed in a multilevel model. Adult patients consulting with a suspected urinary tract infection (UTI) in 22 general practices over a 9 month period supplied a urine sample for laboratory analysis. Cases were patients with a UTI associated with a resistant E. coli. Previous antimicrobial exposure and other patient characteristics were recorded from the medical files. Six hundred and thirty-three patients with an E. coli UTI and a full record for all variables were included. Of the E. coli isolates, 36% were resistant to trimethoprim and 12% to ciprofloxacin. A multilevel logistic regression model was fitted. The odds that E. coli was resistant increased with increasing number of prescriptions over the previous year for trimethoprim from 1.4 (0.8-2.2) for one previous prescription to 4.7 (1.9-12.4) for two and 6.4 (2.0-25.4) for three or more. For ciprofloxacin the ORs were 2.7 (1.2-5.6) for one and 6.5 (2.9-14.8) for two or more. The probability that uropathogenic E. coli was resistant showed important variation between practices and a difference of 17% for trimethoprim and 33% for ciprofloxacin was observed for an imaginary patient moving from a practice with low to a practice with high probability. This difference could not be explained by practice prescribing or practice resistance levels. Previous antimicrobial use and the practice visited affect the risk that a patient with a UTI will be diagnosed with an E. coli resistant to this agent, which was particularly important for ciprofloxacin.

Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data.

PubMed

Pyne, Saumyadipta; Lee, Sharon X; Wang, Kui; Irish, Jonathan; Tamayo, Pablo; Nazaire, Marc-Danie; Duong, Tarn; Ng, Shu-Kay; Hafler, David; Levy, Ronald; Nolan, Garry P; Mesirov, Jill; McLachlan, Geoffrey J

2014-01-01

In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template--used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.

Sample Size Limits for Estimating Upper Level Mediation Models Using Multilevel SEM

ERIC Educational Resources Information Center

Li, Xin; Beretvas, S. Natasha

2013-01-01

This simulation study investigated use of the multilevel structural equation model (MLSEM) for handling measurement error in both mediator and outcome variables ("M" and "Y") in an upper level multilevel mediation model. Mediation and outcome variable indicators were generated with measurement error. Parameter and standard…

Multilevel modelling: Beyond the basic applications.

PubMed

Wright, Daniel B; London, Kamala

2009-05-01

Over the last 30 years statistical algorithms have been developed to analyse datasets that have a hierarchical/multilevel structure. Particularly within developmental and educational psychology these techniques have become common where the sample has an obvious hierarchical structure, like pupils nested within a classroom. We describe two areas beyond the basic applications of multilevel modelling that are important to psychology: modelling the covariance structure in longitudinal designs and using generalized linear multilevel modelling as an alternative to methods from signal detection theory (SDT). Detailed code for all analyses is described using packages for the freeware R.

Using Visual Analysis to Evaluate and Refine Multilevel Models of Single-Case Studies

ERIC Educational Resources Information Center

Baek, Eun Kyeng; Petit-Bois, Merlande; Van den Noortgate, Wim; Beretvas, S. Natasha; Ferron, John M.

2016-01-01

In special education, multilevel models of single-case research have been used as a method of estimating treatment effects over time and across individuals. Although multilevel models can accurately summarize the effect, it is known that if the model is misspecified, inferences about the effects can be biased. Concern with the potential for model…

Locomotor function after long-duration space flight: effects and motor learning during recovery.

PubMed

Mulavara, Ajitkumar P; Feiveson, Alan H; Fiedler, James; Cohen, Helen; Peters, Brian T; Miller, Chris; Brady, Rachel; Bloomberg, Jacob J

2010-05-01

Astronauts returning from space flight and performing Earth-bound activities must rapidly transition from the microgravity-adapted sensorimotor state to that of Earth's gravity. The goal of the current study was to assess locomotor dysfunction and recovery of function after long-duration space flight using a test of functional mobility. Eighteen International Space Station crewmembers experiencing an average flight duration of 185 days performed the functional mobility test (FMT) pre-flight and post-flight. To perform the FMT, subjects walked at a self selected pace through an obstacle course consisting of several pylons and obstacles set up on a base of 10-cm-thick, medium-density foam for a total of six trials per test session. The primary outcome measure was the time to complete the course (TCC, in seconds). To assess the long-term recovery trend of locomotor function after return from space flight, a multilevel exponential recovery model was fitted to the log-transformed TCC data. All crewmembers exhibited altered locomotor function after space flight, with a median 48% increase in the TCC. From the fitted model we calculated that a typical subject would recover to 95% of his/her pre-flight level at approximately 15 days post-flight. In addition, to assess the early motor learning responses after returning from space flight, we modeled performance over the six trials during the first post-flight session by a similar multilevel exponential relation. We found a significant positive correlation between measures of long-term recovery and early motor learning (P < 0.001) obtained from the respective models. We concluded that two types of recovery processes influence an astronaut's ability to re-adapt to Earth's gravity environment. Early motor learning helps astronauts make rapid modifications in their motor control strategies during the first hours after landing. Further, this early motor learning appears to reinforce the adaptive realignment, facilitating re-adaptation to Earth's 1-g environment on return from space flight.

Multilevel structural equation models for assessing moderation within and across levels of analysis.

PubMed

Preacher, Kristopher J; Zhang, Zhen; Zyphur, Michael J

2016-06-01

Social scientists are increasingly interested in multilevel hypotheses, data, and statistical models as well as moderation or interactions among predictors. The result is a focus on hypotheses and tests of multilevel moderation within and across levels of analysis. Unfortunately, existing approaches to multilevel moderation have a variety of shortcomings, including conflated effects across levels of analysis and bias due to using observed cluster averages instead of latent variables (i.e., "random intercepts") to represent higher-level constructs. To overcome these problems and elucidate the nature of multilevel moderation effects, we introduce a multilevel structural equation modeling (MSEM) logic that clarifies the nature of the problems with existing practices and remedies them with latent variable interactions. This remedy uses random coefficients and/or latent moderated structural equations (LMS) for unbiased tests of multilevel moderation. We describe our approach and provide an example using the publicly available High School and Beyond data with Mplus syntax in Appendix. Our MSEM method eliminates problems of conflated multilevel effects and reduces bias in parameter estimates while offering a coherent framework for conceptualizing and testing multilevel moderation effects. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Multilevel Evaluation Alignment: An Explication of a Four-Step Model

ERIC Educational Resources Information Center

Yang, Huilan; Shen, Jianping; Cao, Honggao; Warfield, Charles

2004-01-01

Using the evaluation work on the W.K. Kellogg Foundation's Unleashing Resources Initiative as an example, in this article we explicate a general four-step model appropriate for multilevel evaluation alignment. We review the relevant literature, argue for the need for evaluation alignment in a multilevel context, explain the four-step model,…

Alternatives to Multilevel Modeling for the Analysis of Clustered Data

ERIC Educational Resources Information Center

Huang, Francis L.

2016-01-01

Multilevel modeling has grown in use over the years as a way to deal with the nonindependent nature of observations found in clustered data. However, other alternatives to multilevel modeling are available that can account for observations nested within clusters, including the use of Taylor series linearization for variance estimation, the design…

The Impact of Sample Size and Other Factors When Estimating Multilevel Logistic Models

ERIC Educational Resources Information Center

Schoeneberger, Jason A.

2016-01-01

The design of research studies utilizing binary multilevel models must necessarily incorporate knowledge of multiple factors, including estimation method, variance component size, or number of predictors, in addition to sample sizes. This Monte Carlo study examined the performance of random effect binary outcome multilevel models under varying…

The Impact of Misspecifying the Within-Subject Covariance Structure in Multiwave Longitudinal Multilevel Models: A Monte Carlo Study

ERIC Educational Resources Information Center

Kwok, Oi-man; West, Stephen G.; Green, Samuel B.

2007-01-01

This Monte Carlo study examined the impact of misspecifying the [big sum] matrix in longitudinal data analysis under both the multilevel model and mixed model frameworks. Under the multilevel model approach, under-specification and general-misspecification of the [big sum] matrix usually resulted in overestimation of the variances of the random…

Comparing methods of analysing datasets with small clusters: case studies using four paediatric datasets.

PubMed

Marston, Louise; Peacock, Janet L; Yu, Keming; Brocklehurst, Peter; Calvert, Sandra A; Greenough, Anne; Marlow, Neil

2009-07-01

Studies of prematurely born infants contain a relatively large percentage of multiple births, so the resulting data have a hierarchical structure with small clusters of size 1, 2 or 3. Ignoring the clustering may lead to incorrect inferences. The aim of this study was to compare statistical methods which can be used to analyse such data: generalised estimating equations, multilevel models, multiple linear regression and logistic regression. Four datasets which differed in total size and in percentage of multiple births (n = 254, multiple 18%; n = 176, multiple 9%; n = 10 098, multiple 3%; n = 1585, multiple 8%) were analysed. With the continuous outcome, two-level models produced similar results in the larger dataset, while generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) produced divergent estimates using the smaller dataset. For the dichotomous outcome, most methods, except generalised least squares multilevel modelling (ML GH 'xtlogit' in Stata) gave similar odds ratios and 95% confidence intervals within datasets. For the continuous outcome, our results suggest using multilevel modelling. We conclude that generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) should be used with caution when the dataset is small. Where the outcome is dichotomous and there is a relatively large percentage of non-independent data, it is recommended that these are accounted for in analyses using logistic regression with adjusted standard errors or multilevel modelling. If, however, the dataset has a small percentage of clusters greater than size 1 (e.g. a population dataset of children where there are few multiples) there appears to be less need to adjust for clustering.

Hierarchical models of very large problems, dilemmas, prospects, and an agenda for the future

NASA Technical Reports Server (NTRS)

Richardson, J. M., Jr.

1975-01-01

Interdisciplinary approaches to the modeling of global problems are discussed in terms of multilevel cooperation. A multilevel regionalized model of the Lake Erie Basin is analyzed along with a multilevel regionalized world modeling project. Other topics discussed include: a stratified model of interacting region in a world system, and the application of the model to the world food crisis in south Asia. Recommended research for future development of integrated models is included.

Multilevel Modeling and School Psychology: A Review and Practical Example

ERIC Educational Resources Information Center

Graves, Scott L., Jr.; Frohwerk, April

2009-01-01

The purpose of this article is to provide an overview of the state of multilevel modeling in the field of school psychology. The authors provide a systematic assessment of published research of multilevel modeling studies in 5 journals devoted to the research and practice of school psychology. In addition, a practical example from the nationally…

Meta-analysis in Stata using gllamm.

PubMed

Bagos, Pantelis G

2015-12-01

There are several user-written programs for performing meta-analysis in Stata (Stata Statistical Software: College Station, TX: Stata Corp LP). These include metan, metareg, mvmeta, and glst. However, there are several cases for which these programs do not suffice. For instance, there is no software for performing univariate meta-analysis with correlated estimates, for multilevel or hierarchical meta-analysis, or for meta-analysis of longitudinal data. In this work, we show with practical applications that many disparate models, including but not limited to the ones mentioned earlier, can be fitted using gllamm. The software is very versatile and can handle a wide variety of models with applications in a wide range of disciplines. The method presented here takes advantage of these modeling capabilities and makes use of appropriate transformations, based on the Cholesky decomposition of the inverse of the covariance matrix, known as generalized least squares, in order to handle correlated data. The models described earlier can be thought of as special instances of a general linear mixed-model formulation, but to the author's knowledge, a general exposition in order to incorporate all the available models for meta-analysis as special cases and the instructions to fit them in Stata has not been presented so far. Source code is available at http:www.compgen.org/tools/gllamm. Copyright © 2015 John Wiley & Sons, Ltd.

Multiple imputation by chained equations for systematically and sporadically missing multilevel data.

PubMed

Resche-Rigon, Matthieu; White, Ian R

2018-06-01

In multilevel settings such as individual participant data meta-analysis, a variable is 'systematically missing' if it is wholly missing in some clusters and 'sporadically missing' if it is partly missing in some clusters. Previously proposed methods to impute incomplete multilevel data handle either systematically or sporadically missing data, but frequently both patterns are observed. We describe a new multiple imputation by chained equations (MICE) algorithm for multilevel data with arbitrary patterns of systematically and sporadically missing variables. The algorithm is described for multilevel normal data but can easily be extended for other variable types. We first propose two methods for imputing a single incomplete variable: an extension of an existing method and a new two-stage method which conveniently allows for heteroscedastic data. We then discuss the difficulties of imputing missing values in several variables in multilevel data using MICE, and show that even the simplest joint multilevel model implies conditional models which involve cluster means and heteroscedasticity. However, a simulation study finds that the proposed methods can be successfully combined in a multilevel MICE procedure, even when cluster means are not included in the imputation models.

The Impact of Ignoring the Level of Nesting Structure in Nonparametric Multilevel Latent Class Models

ERIC Educational Resources Information Center

Park, Jungkyu; Yu, Hsiu-Ting

2016-01-01

The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In…

Multilevel Modeling in Psychosomatic Medicine Research

PubMed Central

Myers, Nicholas D.; Brincks, Ahnalee M.; Ames, Allison J.; Prado, Guillermo J.; Penedo, Frank J.; Benedict, Catherine

2012-01-01

The primary purpose of this manuscript is to provide an overview of multilevel modeling for Psychosomatic Medicine readers and contributors. The manuscript begins with a general introduction to multilevel modeling. Multilevel regression modeling at two-levels is emphasized because of its prevalence in psychosomatic medicine research. Simulated datasets based on some core ideas from the Familias Unidas effectiveness study are used to illustrate key concepts including: communication of model specification, parameter interpretation, sample size and power, and missing data. Input and key output files from Mplus and SAS are provided. A cluster randomized trial with repeated measures (i.e., three-level regression model) is then briefly presented with simulated data based on some core ideas from a cognitive behavioral stress management intervention in prostate cancer. PMID:23107843

Multilevel Higher-Order Item Response Theory Models

ERIC Educational Resources Information Center

Huang, Hung-Yu; Wang, Wen-Chung

2014-01-01

In the social sciences, latent traits often have a hierarchical structure, and data can be sampled from multiple levels. Both hierarchical latent traits and multilevel data can occur simultaneously. In this study, we developed a general class of item response theory models to accommodate both hierarchical latent traits and multilevel data. The…

Conducting Multilevel Analyses in Medical Education

ERIC Educational Resources Information Center

Zyphur, Michael J.; Kaplan, Seth A.; Islam, Gazi; Barsky, Adam P.; Franklin, Michael S.

2008-01-01

A significant body of education literature has begun using multilevel statistical models to examine data that reside at multiple levels of analysis. In order to provide a primer for medical education researchers, the current work gives a brief overview of some issues associated with multilevel statistical modeling. To provide an example of this…

Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models

PubMed Central

Baldwin, Scott A.; Imel, Zac E.; Braithwaite, Scott R.; Atkins, David C.

2014-01-01

Objective Multilevel models have become a standard data analysis approach in intervention research. Although the vast majority of intervention studies involve multiple outcome measures, few studies use multivariate analysis methods. The authors discuss multivariate extensions to the multilevel model that can be used by psychotherapy researchers. Method and Results Using simulated longitudinal treatment data, the authors show how multivariate models extend common univariate growth models and how the multivariate model can be used to examine multivariate hypotheses involving fixed effects (e.g., does the size of the treatment effect differ across outcomes?) and random effects (e.g., is change in one outcome related to change in the other?). An online supplemental appendix provides annotated computer code and simulated example data for implementing a multivariate model. Conclusions Multivariate multilevel models are flexible, powerful models that can enhance clinical research. PMID:24491071

Using perceptual cues for brake response to a lead vehicle: Comparing threshold and accumulator models of visual looming.

PubMed

Xue, Qingwan; Markkula, Gustav; Yan, Xuedong; Merat, Natasha

2018-06-18

Previous studies have shown the effect of a lead vehicle's speed, deceleration rate and headway distance on drivers' brake response times. However, how drivers perceive this information and use it to determine when to apply braking is still not quite clear. To better understand the underlying mechanisms, a driving simulator experiment was performed where each participant experienced nine deceleration scenarios. Previously reported effects of the lead vehicle's speed, deceleration rate and headway distance on brake response time were firstly verified in this paper, using a multilevel model. Then, as an alternative to measures of speed, deceleration rate and distance, two visual looming-based metrics (angular expansion rate θ˙ of the lead vehicle on the driver's retina, and inverse tau τ -1 , the ratio between θ˙ and the optical size θ), considered to be more in line with typical human psycho-perceptual responses, were adopted to quantify situation urgency. These metrics were used in two previously proposed mechanistic models predicting brake onset: either when looming surpasses a threshold, or when the accumulated evidence (looming and other cues) reaches a threshold. Results showed that the looming threshold model did not capture the distribution of brake response time. However, regardless of looming metric, the accumulator models fitted the distribution of brake response times better than the pure threshold models. Accumulator models, including brake lights, provided a better model fit than looming-only versions. For all versions of the mechanistic models, models using τ -1 as the measure of looming fitted better than those using θ˙, indicating that the visual cues drivers used during rear-end collision avoidance may be more close to τ -1 . Copyright © 2018 Elsevier Ltd. All rights reserved.

Constructing and validating readability models: the method of integrating multilevel linguistic features with machine learning.

PubMed

Sung, Yao-Ting; Chen, Ju-Ling; Cha, Ji-Her; Tseng, Hou-Chiang; Chang, Tao-Hsing; Chang, Kuo-En

2015-06-01

Multilevel linguistic features have been proposed for discourse analysis, but there have been few applications of multilevel linguistic features to readability models and also few validations of such models. Most traditional readability formulae are based on generalized linear models (GLMs; e.g., discriminant analysis and multiple regression), but these models have to comply with certain statistical assumptions about data properties and include all of the data in formulae construction without pruning the outliers in advance. The use of such readability formulae tends to produce a low text classification accuracy, while using a support vector machine (SVM) in machine learning can enhance the classification outcome. The present study constructed readability models by integrating multilevel linguistic features with SVM, which is more appropriate for text classification. Taking the Chinese language as an example, this study developed 31 linguistic features as the predicting variables at the word, semantic, syntax, and cohesion levels, with grade levels of texts as the criterion variable. The study compared four types of readability models by integrating unilevel and multilevel linguistic features with GLMs and an SVM. The results indicate that adopting a multilevel approach in readability analysis provides a better representation of the complexities of both texts and the reading comprehension process.

A multilevel cross-lagged structural equation analysis for reciprocal relationship between social capital and health.

PubMed

Yu, Ge; Sessions, John G; Fu, Yu; Wall, Martin

2015-10-01

We investigated the reciprocal relationship between individual social capital and perceived mental and physical health in the UK. Using data from the British Household Panel Survey from 1991 to 2008, we fitted cross-lagged structural equation models that include three indicators of social capital vis. social participation, social network, and loneliness. Given that multiple measurement points (level 1) are nested within individuals (level 2), we also applied a multilevel model to allow for residual variation in the outcomes at the occasion and individual levels. Controlling for gender, age, employment status, educational attainment, marital status, household wealth, and region, our analyses suggest that social participation predicts subsequent change in perceived mental health, and vice versa. However, whilst loneliness is found to be significantly related to perceived mental and physical health, reciprocal causality is not found for perceived mental health. Furthermore, we find evidence for reverse effects with both perceived mental and physical health appearing to be the dominant causal factor with respect to the prospective level of social network. Our findings thus shed further light on the importance of social participation and social inclusion in health promotion and aid the development of more effective public health policies in the UK. Copyright © 2015 Elsevier Ltd. All rights reserved.

Nurses' Practice Environment and Work-Family Conflict in Relation to Burn Out: A Multilevel Modelling Approach

PubMed Central

Leineweber, Constanze; Westerlund, Hugo; Chungkham, Holendro Singh; Lindqvist, Rikard; Runesdotter, Sara; Tishelman, Carol

2014-01-01

Objectives To investigate associations between nurse work practice environment measured at department level and individual level work-family conflict on burnout, measured as emotional exhaustion, depersonalization and personal accomplishment among Swedish RNs. Methods A multilevel model was fit with the individual RN at the 1st, and the hospital department at the 2nd level using cross-sectional RN survey data from the Swedish part of RN4CAST, an EU 7th framework project. The data analysed here is based on a national sample of 8,620 RNs from 369 departments in 53 hospitals. Results Generally, RNs reported high values of personal accomplishment and lower values of emotional exhaustion and depersonalization. High work-family conflict increased the risk for emotional exhaustion, but for neither depersonalization nor personal accomplishment. On department level adequate staffing and good leadership and support for nurses reduced the risk for emotional exhaustion and depersonalization. Personal accomplishment was statistically significantly related to staff adequacy. Conclusions The findings suggest that adequate staffing, good leadership, and support for nurses are crucial for RNs' mental health. Our findings also highlight the importance of hospital managers developing policies and practices to facilitate the successful combination of work with private life for employees. PMID:24820972

Coupled superconducting qudit-resonator system: Energy spectrum, state population, and state transition under microwave drive

NASA Astrophysics Data System (ADS)

Liu, W. Y.; Xu, H. K.; Su, F. F.; Li, Z. Y.; Tian, Ye; Han, Siyuan; Zhao, S. P.

2018-03-01

Superconducting quantum multilevel systems coupled to resonators have recently been considered in some applications such as microwave lasing and high-fidelity quantum logical gates. In this work, using an rf-SQUID type phase qudit coupled to a microwave coplanar waveguide resonator, we study both theoretically and experimentally the energy spectrum of the system when the qudit level spacings are varied around the resonator frequency by changing the magnetic flux applied to the qudit loop. We show that the experimental result can be well described by a theoretical model that extends from the usual two-level Jaynes-Cummings system to the present four-level system. It is also shown that due to the small anharmonicity of the phase device a simplified model capturing the leading state interactions fits the experimental spectra very well. Furthermore we use the Lindblad master equation containing various relaxation and dephasing processes to calculate the level populations in the simpler qutrit-resonator system, which allows a clear understanding of the dynamics of the system under the microwave drive. Our results help to better understand and perform the experiments of coupled multilevel and resonator systems and can be applied in the case of transmon or Xmon qudits having similar anharmonicity to the present phase device.

Hospital organizational factors influence work-family conflict in registered nurses: Multilevel modeling of a nation-wide cross-sectional survey in Sweden.

PubMed

Leineweber, C; Chungkham, H S; Westerlund, H; Tishelman, C; Lindqvist, R

2014-05-01

The present shortage of registered nurses (RNs) in many European countries is expected to continue and worsen, which poses a substantial threat to the maintenance of healthcare in this region. Work-family conflict is a known risk factor for turnover and sickness absence. This paper empirically examines whether the nurse practice environment is associated with experienced work-family conflict. A multilevel model was fit with the individual RN at the 1st, and the hospital department at the 2nd level using cross-sectional RN survey data from the Swedish part of RN4CAST, an EU 7th framework project. The data analyzed here is based on a national sample of 8356 female and 592 male RNs from 369 hospital departments. We found that 6% of the variability in work-family conflict experienced by RNs was at the department level. Organizational level factors significantly accounted for most of the variability at this level with two of the work practice environment factors examined, staffing adequacy and nurse involvement in hospital affairs, significantly related to work-family conflict. Due to the design of the study, factors on ward and work group levels could not be analyzed, but are likely to account for additional variance which in the present analysis appears to be on the individual level, with private life factors likely explaining another major part. These results suggest that higher level organizational factors in health care have a significant impact on the risk of work-family conflict among RNs through their impact on the nurse practice environment. Lower level organizational factors should be investigated in future studies using hierarchical multilevel sampling. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

One Big Happy Family? Unraveling the Relationship between Shared Perceptions of Team Psychological Contracts, Person-Team Fit and Team Performance.

PubMed

Gibbard, Katherine; Griep, Yannick; De Cooman, Rein; Hoffart, Genevieve; Onen, Denis; Zareipour, Hamidreza

2017-01-01

With the knowledge that team work is not always associated with high(er) performance, we draw from the Multi-Level Theory of Psychological Contracts, Person-Environment Fit Theory, and Optimal Distinctiveness Theory to study shared perceptions of psychological contract (PC) breach in relation to shared perceptions of complementary and supplementary fit to explain why some teams perform better than other teams. We collected three repeated survey measures in a sample of 128 respondents across 46 teams. After having made sure that we met all statistical criteria, we aggregated our focal variables to the team-level and analyzed our data by means of a longitudinal three-wave autoregressive moderated-mediation model in which each relationship was one-time lag apart. We found that shared perceptions of PC breach were directly negatively related to team output and negatively related to perceived team member effectiveness through a decrease in shared perceptions of supplementary fit. However, we also demonstrated a beneficial process in that shared perceptions of PC breach were positively related to shared perceptions of complementary fit, which in turn were positively related to team output. Moreover, best team output appeared in teams that could combine high shared perceptions of complementary fit with modest to high shared perceptions of supplementary fit. Overall, our findings seem to indicate that in terms of team output there may be a bright side to perceptions of PC breach and that perceived person-team fit may play an important role in this process.

One Big Happy Family? Unraveling the Relationship between Shared Perceptions of Team Psychological Contracts, Person-Team Fit and Team Performance

PubMed Central

Gibbard, Katherine; Griep, Yannick; De Cooman, Rein; Hoffart, Genevieve; Onen, Denis; Zareipour, Hamidreza

2017-01-01

With the knowledge that team work is not always associated with high(er) performance, we draw from the Multi-Level Theory of Psychological Contracts, Person-Environment Fit Theory, and Optimal Distinctiveness Theory to study shared perceptions of psychological contract (PC) breach in relation to shared perceptions of complementary and supplementary fit to explain why some teams perform better than other teams. We collected three repeated survey measures in a sample of 128 respondents across 46 teams. After having made sure that we met all statistical criteria, we aggregated our focal variables to the team-level and analyzed our data by means of a longitudinal three-wave autoregressive moderated-mediation model in which each relationship was one-time lag apart. We found that shared perceptions of PC breach were directly negatively related to team output and negatively related to perceived team member effectiveness through a decrease in shared perceptions of supplementary fit. However, we also demonstrated a beneficial process in that shared perceptions of PC breach were positively related to shared perceptions of complementary fit, which in turn were positively related to team output. Moreover, best team output appeared in teams that could combine high shared perceptions of complementary fit with modest to high shared perceptions of supplementary fit. Overall, our findings seem to indicate that in terms of team output there may be a bright side to perceptions of PC breach and that perceived person-team fit may play an important role in this process. PMID:29170648

Mathematical model comparing of the multi-level economics systems

NASA Astrophysics Data System (ADS)

Brykalov, S. M.; Kryanev, A. V.

2017-12-01

The mathematical model (scheme) of a multi-level comparison of the economic system, characterized by the system of indices, is worked out. In the mathematical model of the multi-level comparison of the economic systems, the indicators of peer review and forecasting of the economic system under consideration can be used. The model can take into account the uncertainty in the estimated values of the parameters or expert estimations. The model uses the multi-criteria approach based on the Pareto solutions.

Multilevel corporate environmental responsibility.

PubMed

Karassin, Orr; Bar-Haim, Aviad

2016-12-01

The multilevel empirical study of the antecedents of corporate social responsibility (CSR) has been identified as "the first knowledge gap" in CSR research. Based on an extensive literature review, the present study outlines a conceptual multilevel model of CSR, then designs and empirically validates an operational multilevel model of the principal driving factors affecting corporate environmental responsibility (CER), as a measure of CSR. Both conceptual and operational models incorporate three levels of analysis: institutional, organizational, and individual. The multilevel nature of the design allows for the assessment of the relative importance of the levels and of their components in the achievement of CER. Unweighted least squares (ULS) regression analysis reveals that the institutional-level variables have medium relationships with CER, some variables having a negative effect. The organizational level is revealed as having strong and positive significant relationships with CER, with organizational culture and managers' attitudes and behaviors as significant driving forces. The study demonstrates the importance of multilevel analysis in improving the understanding of CSR drivers, relative to single level models, even if the significance of specific drivers and levels may vary by context. Copyright © 2016 Elsevier Ltd. All rights reserved.

An ecological systems approach to bullying behaviors among middle school students in the United States.

PubMed

Lee, Chang-Hun

2011-05-01

The aim of this study is to identify an ecological prediction model of bullying behaviors. Based on an ecological systems theory, this study identifies significant factors influencing bullying behaviors at different levels of middle and high school. These levels include the microsystem, mesosystem, exosystem, and macrosystem. More specifically, the ecological factors investigated in this multilevel analysis are individual traits, family experiences, parental involvement, school climate, and community characteristics. Using data collected in 2008 from 485 randomly selected students in a school district, this study identifies a best-fitting structural model of bullying behavior. Findings suggest that the ecological model accounted for a high portion of variance in bullying behaviors. All of the ecological systems as well as individual traits were found to be significant influences on bullying behaviors either directly or indirectly.

A Multilevel AR(1) Model: Allowing for Inter-Individual Differences in Trait-Scores, Inertia, and Innovation Variance.

PubMed

Jongerling, Joran; Laurenceau, Jean-Philippe; Hamaker, Ellen L

2015-01-01

In this article we consider a multilevel first-order autoregressive [AR(1)] model with random intercepts, random autoregression, and random innovation variance (i.e., the level 1 residual variance). Including random innovation variance is an important extension of the multilevel AR(1) model for two reasons. First, between-person differences in innovation variance are important from a substantive point of view, in that they capture differences in sensitivity and/or exposure to unmeasured internal and external factors that influence the process. Second, using simulation methods we show that modeling the innovation variance as fixed across individuals, when it should be modeled as a random effect, leads to biased parameter estimates. Additionally, we use simulation methods to compare maximum likelihood estimation to Bayesian estimation of the multilevel AR(1) model and investigate the trade-off between the number of individuals and the number of time points. We provide an empirical illustration by applying the extended multilevel AR(1) model to daily positive affect ratings from 89 married women over the course of 42 consecutive days.

Dual deep modeling: multi-level modeling with dual potencies and its formalization in F-Logic.

PubMed

Neumayr, Bernd; Schuetz, Christoph G; Jeusfeld, Manfred A; Schrefl, Michael

2018-01-01

An enterprise database contains a global, integrated, and consistent representation of a company's data. Multi-level modeling facilitates the definition and maintenance of such an integrated conceptual data model in a dynamic environment of changing data requirements of diverse applications. Multi-level models transcend the traditional separation of class and object with clabjects as the central modeling primitive, which allows for a more flexible and natural representation of many real-world use cases. In deep instantiation, the number of instantiation levels of a clabject or property is indicated by a single potency. Dual deep modeling (DDM) differentiates between source potency and target potency of a property or association and supports the flexible instantiation and refinement of the property by statements connecting clabjects at different modeling levels. DDM comes with multiple generalization of clabjects, subsetting/specialization of properties, and multi-level cardinality constraints. Examples are presented using a UML-style notation for DDM together with UML class and object diagrams for the representation of two-level user views derived from the multi-level model. Syntax and semantics of DDM are formalized and implemented in F-Logic, supporting the modeler with integrity checks and rich query facilities.

Community Influences on Married Women's Safer Sex Negotiation Attitudes in Bangladesh: A Multilevel Analysis.

PubMed

Jesmin, Syeda S; Cready, Cynthia M

2016-02-01

The influence of disadvantaged or deprived community on individuals' health risk-behaviors is increasingly being documented in a growing body of literature. However, little is known about the effects of community characteristics on women's sexual attitudes and behaviors. To examine community effects on married women's safer sex negotiation attitudes, we analyzed cross-sectional data from the 2011 Bangladesh Demographic and Health Surveys on a sample of 15,134 married women in 600 communities. We estimated two multilevel logistic regression models. Model 1, which included only individual-level variables, showed that women's autonomy/empowerment, age, and HIV knowledge had significant associations with their safer sex negotiation attitudes. We did not find any socioeconomic status gradient in safer sex negotiation attitudes at the individual level. Adding community-level variables in Model 2 significantly improved the fit of the model. Strikingly, we found that higher community-level poverty was associated with greater positive safer sex negotiation attitudes. Prevailing gender norms and overall women's empowerment in the community also had significant effects. While research on community influences calls for focusing on disadvantaged communities, our research highlights the importance of not underestimating the challenges that married women in economically privileged communities may face in negotiating safer sex. To have sufficient and equitable impact on married women's sexual and reproductive health, sexual and reproductive health promotion policies and programs need to be directed to women in wealthier communities as well.

Skill-related physical fitness versus aerobic fitness as a predictor of executive functioning in children with intellectual disabilities or borderline intellectual functioning.

PubMed

Hartman, Esther; Smith, Joanne; Houwen, Suzanne; Visscher, Chris

2017-05-01

Children with intellectual disabilities (ID) or borderline intellectual disabilities (BIF) often demonstrate impairments in executive functioning (EF). Studies in typically developing children show that aerobic fitness (AF) is positively related with EF. Skill-related physical fitness (SF) might, however, be a stronger predictor of EF than AF, as cognitive challenges are inherent in application of these skills. In this study, AF and SF were examined simultaneously in relationship with domains of EF in children with ID or BIF. Seventy-three children (age range 8-11; 51 boys) with ID (IQ range 56-79) or BIF (IQ range 71-79) were measured annually over a period of 4 years on AF (20-m endurance shuttle run test) and SF (plate tapping and 10×5m run). EF was measured with the Stroop Color-Word test (inhibition), Trailmaking and Fluency test (cognitive flexibility), Self-ordered pointing task (working memory) and the Tower of London (planning). Multilevel models showed that SF was significantly associated with inhibition and both measures of cognitive flexibility, but in the same models no significant associations between AF and EF were found. In addition, age was significantly related to working memory and cognitive flexibility, favouring the older children. In children with ID or BIF, SF is of greater importance than AF in relationship with core domains of EF. Copyright © 2017 Elsevier Ltd. All rights reserved.

Estimating trajectories of energy intake through childhood and adolescence using linear-spline multilevel models.

PubMed

Anderson, Emma L; Tilling, Kate; Fraser, Abigail; Macdonald-Wallis, Corrie; Emmett, Pauline; Cribb, Victoria; Northstone, Kate; Lawlor, Debbie A; Howe, Laura D

2013-07-01

Methods for the assessment of changes in dietary intake across the life course are underdeveloped. We demonstrate the use of linear-spline multilevel models to summarize energy-intake trajectories through childhood and adolescence and their application as exposures, outcomes, or mediators. The Avon Longitudinal Study of Parents and Children assessed children's dietary intake several times between ages 3 and 13 years, using both food frequency questionnaires (FFQs) and 3-day food diaries. We estimated energy-intake trajectories for 12,032 children using linear-spline multilevel models. We then assessed the associations of these trajectories with maternal body mass index (BMI), and later offspring BMI, and also their role in mediating the relation between maternal and offspring BMIs. Models estimated average and individual energy intake at 3 years, and linear changes in energy intake from age 3 to 7 years and from age 7 to 13 years. By including the exposure (in this example, maternal BMI) in the multilevel model, we were able to estimate the average energy-intake trajectories across levels of the exposure. When energy-intake trajectories are the exposure for a later outcome (in this case offspring BMI) or a mediator (between maternal and offspring BMI), results were similar, whether using a two-step process (exporting individual-level intercepts and slopes from multilevel models and using these in linear regression/path analysis), or a single-step process (multivariate multilevel models). Trajectories were similar when FFQs and food diaries were assessed either separately, or when combined into one model. Linear-spline multilevel models provide useful summaries of trajectories of dietary intake that can be used as an exposure, outcome, or mediator.

Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data

NASA Technical Reports Server (NTRS)

Gorinevsky, Dimitry; Matthews, Bryan L.; Martin, Rodney

2012-01-01

This paper describes an application of data mining technology called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM transforms the data into aircraft performance models, flight-to-flight trends, and individual flight anomalies by fitting a multi-level regression model to the data. The model represents aircraft flight performance and takes into account fixed effects: flight-to-flight and vehicle-to-vehicle variability. The regression parameters include aerodynamic coefficients and other aircraft performance parameters that are usually identified by aircraft manufacturers in flight tests. Using DFM, the multi-terabyte FOQA data set with half-million flights was processed in a few hours. The anomalies found include wrong values of competed variables, (e.g., aircraft weight), sensor failures and baises, failures, biases, and trends in flight actuators. These anomalies were missed by the existing airline monitoring of FOQA data exceedances.

A tutorial on count regression and zero-altered count models for longitudinal substance use data

PubMed Central

Atkins, David C.; Baldwin, Scott A.; Zheng, Cheng; Gallop, Robert J.; Neighbors, Clayton

2012-01-01

Critical research questions in the study of addictive behaviors concern how these behaviors change over time - either as the result of intervention or in naturalistic settings. The combination of count outcomes that are often strongly skewed with many zeroes (e.g., days using, number of total drinks, number of drinking consequences) with repeated assessments (e.g., longitudinal follow-up after intervention or daily diary data) present challenges for data analyses. The current article provides a tutorial on methods for analyzing longitudinal substance use data, focusing on Poisson, zero-inflated, and hurdle mixed models, which are types of hierarchical or multilevel models. Two example datasets are used throughout, focusing on drinking-related consequences following an intervention and daily drinking over the past 30 days, respectively. Both datasets as well as R, SAS, Mplus, Stata, and SPSS code showing how to fit the models are available on a supplemental website. PMID:22905895

A 2 x 2 Taxonomy of Multilevel Latent Contextual Models: Accuracy-Bias Trade-Offs in Full and Partial Error Correction Models

ERIC Educational Resources Information Center

Ludtke, Oliver; Marsh, Herbert W.; Robitzsch, Alexander; Trautwein, Ulrich

2011-01-01

In multilevel modeling, group-level variables (L2) for assessing contextual effects are frequently generated by aggregating variables from a lower level (L1). A major problem of contextual analyses in the social sciences is that there is no error-free measurement of constructs. In the present article, 2 types of error occurring in multilevel data…

Performance of time-varying predictors in multilevel models under an assumption of fixed or random effects.

PubMed

Baird, Rachel; Maxwell, Scott E

2016-06-01

Time-varying predictors in multilevel models are a useful tool for longitudinal research, whether they are the research variable of interest or they are controlling for variance to allow greater power for other variables. However, standard recommendations to fix the effect of time-varying predictors may make an assumption that is unlikely to hold in reality and may influence results. A simulation study illustrates that treating the time-varying predictor as fixed may allow analyses to converge, but the analyses have poor coverage of the true fixed effect when the time-varying predictor has a random effect in reality. A second simulation study shows that treating the time-varying predictor as random may have poor convergence, except when allowing negative variance estimates. Although negative variance estimates are uninterpretable, results of the simulation show that estimates of the fixed effect of the time-varying predictor are as accurate for these cases as for cases with positive variance estimates, and that treating the time-varying predictor as random and allowing negative variance estimates performs well whether the time-varying predictor is fixed or random in reality. Because of the difficulty of interpreting negative variance estimates, 2 procedures are suggested for selection between fixed-effect and random-effect models: comparing between fixed-effect and constrained random-effect models with a likelihood ratio test or fitting a fixed-effect model when an unconstrained random-effect model produces negative variance estimates. The performance of these 2 procedures is compared. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Mediating effects of resistance training skill competency on health-related fitness and physical activity: the ATLAS cluster randomised controlled trial.

PubMed

Smith, Jordan J; Morgan, Philip J; Plotnikoff, Ronald C; Stodden, David F; Lubans, David R

2016-01-01

The purpose of this study was to examine the mediating effect of resistance training skill competency on percentage of body fat, muscular fitness and physical activity among a sample of adolescent boys participating in a school-based obesity prevention intervention. Participants were 361 adolescent boys taking part in the Active Teen Leaders Avoiding Screen-time (ATLAS) cluster randomised controlled trial: a school-based program targeting the health behaviours of economically disadvantaged adolescent males considered "at-risk" of obesity. Body fat percentage (bioelectrical impedance), muscular fitness (hand grip dynamometry and push-ups), physical activity (accelerometry) and resistance training skill competency were assessed at baseline and post-intervention (i.e., 8 months). Three separate multi-level mediation models were analysed to investigate the potential mediating effects of resistance training skill competency on each of the study outcomes using a product-of-coefficients test. Analyses followed the intention-to-treat principle. The intervention had a significant impact on the resistance training skill competency of the boys, and improvements in skill competency significantly mediated the effect of the intervention on percentage of body fat and the combined muscular fitness score. No significant mediated effects were found for physical activity. Improving resistance training skill competency may be an effective strategy for achieving improvements in body composition and muscular fitness in adolescent boys.

Translating multilevel theory into multilevel research: Challenges and opportunities for understanding the social determinants of psychiatric disorders

PubMed Central

Dunn, Erin C.; Masyn, Katherine E.; Yudron, Monica; Jones, Stephanie M.; Subramanian, S.V.

2014-01-01

The observation that features of the social environment, including family, school, and neighborhood characteristics, are associated with individual-level outcomes has spurred the development of dozens of multilevel or ecological theoretical frameworks in epidemiology, public health, psychology, and sociology, among other disciplines. Despite the widespread use of such theories in etiological, intervention, and policy studies, challenges remain in bridging multilevel theory and empirical research. This paper set out to synthesize these challenges and provide specific examples of methodological and analytical strategies researchers are using to gain a more nuanced understanding of the social determinants of psychiatric disorders, with a focus on children’s mental health. To accomplish this goal, we begin by describing multilevel theories, defining their core elements, and discussing what these theories suggest is needed in empirical work. In the second part, we outline the main challenges researchers face in translating multilevel theory into research. These challenges are presented for each stage of the research process. In the third section, we describe two methods being used as alternatives to traditional multilevel modeling techniques to better bridge multilevel theory and multilevel research. These are: (1) multilevel factor analysis and multilevel structural equation modeling; and (2) dynamic systems approaches. Through its review of multilevel theory, assessment of existing strategies, and examination of emerging methodologies, this paper offers a framework to evaluate and guide empirical studies on the social determinants of child psychiatric disorders as well as health across the lifecourse. PMID:24469555

Generalized Multilevel Structural Equation Modeling

ERIC Educational Resources Information Center

Rabe-Hesketh, Sophia; Skrondal, Anders; Pickles, Andrew

2004-01-01

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent…

Multilevel and sex-specific selection on competitive traits in North American red squirrels.

PubMed

Fisher, David N; Boutin, Stan; Dantzer, Ben; Humphries, Murray M; Lane, Jeffrey E; McAdam, Andrew G

2017-07-01

Individuals often interact more closely with some members of the population (e.g., offspring, siblings, or group members) than they do with other individuals. This structuring of interactions can lead to multilevel natural selection, where traits expressed at the group-level influence fitness alongside individual-level traits. Such multilevel selection can alter evolutionary trajectories, yet is rarely quantified in the wild, especially for species that do not interact in clearly demarcated groups. We quantified multilevel natural selection on two traits, postnatal growth rate and birth date, in a population of North American red squirrels (Tamiasciurus hudsonicus). The strongest level of selection was typically within-acoustic social neighborhoods (within 130 m of the nest), where growing faster and being born earlier than nearby litters was key, while selection on growth rate was also apparent both within-litters and within-study areas. Higher population densities increased the strength of selection for earlier breeding, but did not influence selection on growth rates. Females experienced especially strong selection on growth rate at the within-litter level, possibly linked to the biased bequeathal of the maternal territory to daughters. Our results demonstrate the importance of considering multilevel and sex-specific selection in wild species, including those that are territorial and sexually monomorphic. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

The Relationship of Physiopsychosocial Factors and Spiritual Well-Being in Elderly Residents: Implications for Evidence-Based Practice.

PubMed

Chen, Yi-Heng; Lin, Li-Chan; Chuang, Li-Lan; Chen, Mei-Li

2017-12-01

Older adults in residential settings frequently suffer from functional decline, mental illness, and social isolation, which make them more vulnerable to spiritual distress. However, empirical evidence of the interrelationships between physiopsychosocial variables and spiritual well-being are still lacking, limiting the application of the biopsychosocial-spiritual model in institutional healthcare practice. To explain the mechanisms by which these variables are linked, this cross-sectional study tested a causal model of predictors of spiritual well-being among 377 institutionalized older adults with disability using a structural equation modeling approach. The primary variables in the hypothesized model were measured using the Barthel Index for functional ability, the Geriatric Depression Scale-short form for depression, the Personal Resources Questionnaire 85-Part 2 for perceived social support, and the Spiritual Well-Being Scale for spiritual well-being. The model fit indices suggest that the hypothesized model had a reasonably adequate model fit (χ 2 = 12.18, df = 6, p = .07, goodness-of-fitness index [GFI] = 0.99, adjusted GIF index [AGFI] = 0.93, nonnormed fit index [NFI] = 0.99, comparative fit index [CFI] = 0.99). In this study, perceived social support and depression directly affected spiritual well-being, and functional ability indirectly affected spiritual well-being via perceived social support or depression. In addition, functional ability influenced perceived social support directly, which in turn influenced depression and ultimately influenced spiritual well-being. This study results confirm the effect of physiopsychosocial factors on institutionalized older adults' spiritual well-being. However, the presence and level of functional disability do not necessarily influence spiritual well-being in late life unless it is disruptive to social relationships and is thus bound to lead to low perceived social support and the onset of depression. The findings address the fact that the practice of spirituality is multidimensional and multileveled. Psychosocial interventions for institutionalized elders with disabilities should focus on increasing nurse-patient interaction and providing access to meaningful social activities to improve mental health and spiritual well-being. © 2017 Sigma Theta Tau International.

Is psychological membership in the classroom a function of standing out while fitting in? Implications for achievement motivation and emotions.

PubMed

Gray, DeLeon L

2017-04-01

Education researchers have consistently linked students' perceptions of "fitting in" at school with patterns of motivation and positive emotions. This study proposes that "standing out" is also helpful for producing these outcomes, and that standing out works in concert with perceptions of fitting in. In a sample of 702 high school students nested within 33 classrooms, principal components analysis and confirmatory factor analysis were each conducted on half of the sample. Results support the proposed structure of measures of standing out and fitting in. Multilevel latent profile analysis was then used to classify students into four profiles of standing out while fitting in (SOFI): Unfulfilled, Somewhat Fulfilled, Nearly Fulfilled, and Fulfilled. A multinomial logistic regression revealed that students of color and those on who paid free/reduced prices lunch were overrepresented in the Unfulfilled and Somewhat Fulfilled profiles. A multilevel path analysis was then performed to assess the direct and indirect associations of profile membership with measures of task value and achievement emotions. Relative to the other profiles, students in the Fulfilled SOFI Profile express greater psychological membership in their classrooms and, in turn, express higher valuing of academic material (i.e., intrinsic value, utility value, and attainment value) and more positive achievement emotions (i.e., more enjoyment and pride; less boredom, hopelessness, and shame). This investigation provides critical insights on the potential benefits of structuring academic learning environments to foster feelings of distinctiveness among adolescents; and has implications for cultivating identities and achievement motivation in academic settings. Copyright © 2017 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Mind the Gap! A Multilevel Analysis of Factors Related to Variation in Published Cost-Effectiveness Estimates within and between Countries.

PubMed

Boehler, Christian E H; Lord, Joanne

2016-01-01

Published cost-effectiveness estimates can vary considerably, both within and between countries. Despite extensive discussion, little is known empirically about factors relating to these variations. To use multilevel statistical modeling to integrate cost-effectiveness estimates from published economic evaluations to investigate potential causes of variation. Cost-effectiveness studies of statins for cardiovascular disease prevention were identified by systematic review. Estimates of incremental costs and effects were extracted from reported base case, sensitivity, and subgroup analyses, with estimates grouped in studies and in countries. Three bivariate models were developed: a cross-classified model to accommodate data from multinational studies, a hierarchical model with multinational data allocated to a single category at country level, and a hierarchical model excluding multinational data. Covariates at different levels were drawn from a long list of factors suggested in the literature. We found 67 studies reporting 2094 cost-effectiveness estimates relating to 23 countries (6 studies reporting for more than 1 country). Data and study-level covariates included patient characteristics, intervention and comparator cost, and some study methods (e.g., discount rates and time horizon). After adjusting for these factors, the proportion of variation attributable to countries was negligible in the cross-classified model but moderate in the hierarchical models (14%-19% of total variance). Country-level variables that improved the fit of the hierarchical models included measures of income and health care finance, health care resources, and population risks. Our analysis suggested that variability in published cost-effectiveness estimates is related more to differences in study methods than to differences in national context. Multinational studies were associated with much lower country-level variation than single-country studies. These findings are for a single clinical question and may be atypical. © The Author(s) 2015.

A Methodological Review of Statistical Methods for Handling Multilevel Non-Nested Longitudinal Data in Educational Research

ERIC Educational Resources Information Center

Sun, Shuyan; Pan, Wei

2014-01-01

As applications of multilevel modelling in educational research increase, researchers realize that multilevel data collected in many educational settings are often not purely nested. The most common multilevel non-nested data structure is one that involves student mobility in longitudinal studies. This article provides a methodological review of…

Promotion of cooperation by selective group extinction

NASA Astrophysics Data System (ADS)

Böttcher, Marvin A.; Nagler, Jan

2016-06-01

Multilevel selection is an important organizing principle that crucially underlies evolutionary processes from the emergence of cells to eusociality and the economics of nations. Previous studies on multilevel selection assumed that the effective higher-level selection emerges from lower-level reproduction. This leads to selection among groups, although only individuals reproduce. We introduce selective group extinction, where groups die with a probability inversely proportional to their group fitness. When accounting for this the critical benefit-to-cost ratio is substantially lowered. Because in game theory and evolutionary dynamics the degree of cooperation crucially depends on this ratio above which cooperation emerges, previous studies may have substantially underestimated the establishment and maintenance of cooperation.

Using multilevel models to quantify heterogeneity in resource selection

USGS Publications Warehouse

Wagner, Tyler; Diefenbach, Duane R.; Christensen, Sonja; Norton, Andrew S.

2011-01-01

Models of resource selection are being used increasingly to predict or model the effects of management actions rather than simply quantifying habitat selection. Multilevel, or hierarchical, models are an increasingly popular method to analyze animal resource selection because they impose a relatively weak stochastic constraint to model heterogeneity in habitat use and also account for unequal sample sizes among individuals. However, few studies have used multilevel models to model coefficients as a function of predictors that may influence habitat use at different scales or quantify differences in resource selection among groups. We used an example with white-tailed deer (Odocoileus virginianus) to illustrate how to model resource use as a function of distance to road that varies among deer by road density at the home range scale. We found that deer avoidance of roads decreased as road density increased. Also, we used multilevel models with sika deer (Cervus nippon) and white-tailed deer to examine whether resource selection differed between species. We failed to detect differences in resource use between these two species and showed how information-theoretic and graphical measures can be used to assess how resource use may have differed. Multilevel models can improve our understanding of how resource selection varies among individuals and provides an objective, quantifiable approach to assess differences or changes in resource selection.

Multilevel Modeling and Policy Development: Guidelines and Applications to Medical Travel.

PubMed

Garcia-Garzon, Eduardo; Zhukovsky, Peter; Haller, Elisa; Plakolm, Sara; Fink, David; Petrova, Dafina; Mahalingam, Vaishali; Menezes, Igor G; Ruggeri, Kai

2016-01-01

Medical travel has expanded rapidly in recent years, resulting in new markets and increased access to medical care. Whereas several studies investigated the motives of individuals seeking healthcare abroad, the conventional analytical approach is limited by substantial caveats. Classical techniques as found in the literature cannot provide sufficient insight due to the nested nature of data generated. The application of adequate analytical techniques, specifically multilevel modeling, is scarce to non-existent in the context of medical travel. This study introduces the guidelines for application of multilevel techniques in public health research by presenting an application of multilevel modeling in analyzing the decision-making patterns of potential medical travelers. Benefits and potential limitations are discussed.

Multilevel Modeling and Policy Development: Guidelines and Applications to Medical Travel

PubMed Central

Garcia-Garzon, Eduardo; Zhukovsky, Peter; Haller, Elisa; Plakolm, Sara; Fink, David; Petrova, Dafina; Mahalingam, Vaishali; Menezes, Igor G.; Ruggeri, Kai

2016-01-01

Medical travel has expanded rapidly in recent years, resulting in new markets and increased access to medical care. Whereas several studies investigated the motives of individuals seeking healthcare abroad, the conventional analytical approach is limited by substantial caveats. Classical techniques as found in the literature cannot provide sufficient insight due to the nested nature of data generated. The application of adequate analytical techniques, specifically multilevel modeling, is scarce to non-existent in the context of medical travel. This study introduces the guidelines for application of multilevel techniques in public health research by presenting an application of multilevel modeling in analyzing the decision-making patterns of potential medical travelers. Benefits and potential limitations are discussed. PMID:27252672

Social Resilience: The Value of Social Fitness with an Application to the Military

ERIC Educational Resources Information Center

Cacioppo, John T.; Reis, Harry T.; Zautra, Alex J.

2011-01-01

Resilience has been regarded narrowly as a quintessential individual property by most investigators. Social resilience, however, is inherently a multilevel construct, revealed by capacities of individuals, but also groups, to foster, engage in, and sustain positive social relationships and to endure and recover from stressors and social isolation.…

A multivariate multilevel Gaussian model with a mixed effects structure in the mean and covariance part.

PubMed

Li, Baoyue; Bruyneel, Luk; Lesaffre, Emmanuel

2014-05-20

A traditional Gaussian hierarchical model assumes a nested multilevel structure for the mean and a constant variance at each level. We propose a Bayesian multivariate multilevel factor model that assumes a multilevel structure for both the mean and the covariance matrix. That is, in addition to a multilevel structure for the mean we also assume that the covariance matrix depends on covariates and random effects. This allows to explore whether the covariance structure depends on the values of the higher levels and as such models heterogeneity in the variances and correlation structure of the multivariate outcome across the higher level values. The approach is applied to the three-dimensional vector of burnout measurements collected on nurses in a large European study to answer the research question whether the covariance matrix of the outcomes depends on recorded system-level features in the organization of nursing care, but also on not-recorded factors that vary with countries, hospitals, and nursing units. Simulations illustrate the performance of our modeling approach. Copyright © 2013 John Wiley & Sons, Ltd.

Updated User's Guide for Sammy: Multilevel R-Matrix Fits to Neutron Data Using Bayes' Equations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Larson, Nancy M

2008-10-01

In 1980 the multilevel multichannel R-matrix code SAMMY was released for use in analysis of neutron-induced cross section data at the Oak Ridge Electron Linear Accelerator. Since that time, SAMMY has evolved to the point where it is now in use around the world for analysis of many different types of data. SAMMY is not limited to incident neutrons but can also be used for incident protons, alpha particles, or other charged particles; likewise, Coulomb exit hannels can be included. Corrections for a wide variety of experimental conditions are available in the code: Doppler and resolution broadening, multiple-scattering corrections formore » capture or reaction yields, normalizations and backgrounds, to name but a few. The fitting procedure is Bayes' method, and data and parameter covariance matrices are properly treated within the code. Pre- and post-processing capabilities are also available, including (but not limited to) connections with the Evaluated Nuclear Data Files. Though originally designed for use in the resolved resonance region, SAMMY also includes a treatment for data analysis in the unresolved resonance region.« less

A Multilevel Multiset Time-Series Model for Describing Complex Developmental Processes

PubMed Central

Ma, Xin; Shen, Jianping

2017-01-01

The authors sought to develop an analytical platform where multiple sets of time series can be examined simultaneously. This multivariate platform capable of testing interaction effects among multiple sets of time series can be very useful in empirical research. The authors demonstrated that the multilevel framework can readily accommodate this analytical capacity. Given their intention to use the multilevel multiset time-series model to pursue complicated research purposes, their resulting model is relatively simple to specify, to run, and to interpret. These advantages make the adoption of their model relatively effortless as long as researchers have the basic knowledge and skills in working with multilevel growth modeling. With multiple potential extensions of their model, the establishment of this analytical platform for analysis of multiple sets of time series can inspire researchers to pursue far more advanced research designs to address complex developmental processes in reality. PMID:29881094

Aerobic Fitness, Micronutrient Status, and Academic Achievement in Indian School-Aged Children

PubMed Central

Desai, Ishaan K.; Kurpad, Anura V.; Chomitz, Virginia R.; Thomas, Tinku

2015-01-01

Aerobic fitness has been shown to have several beneficial effects on child health. However, research on its relationship with academic performance has been limited, particularly in developing countries and among undernourished populations. This study examined the association between aerobic fitness and academic achievement in clinically healthy but nutritionally compromised Indian school-aged children and assessed whether micronutrient status affects this association. 273 participants, aged 7 to 10.5 years, were enrolled from three primary schools in Bangalore, India. Data on participants’ aerobic fitness (20-m shuttle test), demographics, anthropometry, diet, physical activity, and micronutrient status were abstracted. School-wide exam scores in mathematics and Kannada language served as indicators of academic performance and were standardized by grade level. The strength of the fitness/achievement association was analyzed using Spearman’s rank correlation, multiple variable logistic regression, and multi-level models. Significant positive correlations between aerobic capacity (VO2 peak) and academic scores in math and Kannada were observed (P < 0.05). After standardizing scores across grade levels and adjusting for school, gender, socioeconomic status, and weight status (BMI Z-score), children with greater aerobic capacities (mL * kg-1 * min-1) had greater odds of scoring above average on math and Kannada exams (OR=1.08, 95% CI: 1.02 to 1.15 and OR=1.11, 95% CI: 1.04 to 1.18, respectively). This association remained significant after adjusting for micronutrient deficiencies. These findings provide preliminary evidence of a fitness/achievement association in Indian children. While the mechanisms by which aerobic fitness may be linked to academic achievement require further investigation, the results suggest that educators and policymakers should consider the adequacy of opportunities for physical activity and fitness in schools for both their physical and potential academic benefits. PMID:25806824

Aerobic fitness, micronutrient status, and academic achievement in Indian school-aged children.

PubMed

Desai, Ishaan K; Kurpad, Anura V; Chomitz, Virginia R; Thomas, Tinku

2015-01-01

Aerobic fitness has been shown to have several beneficial effects on child health. However, research on its relationship with academic performance has been limited, particularly in developing countries and among undernourished populations. This study examined the association between aerobic fitness and academic achievement in clinically healthy but nutritionally compromised Indian school-aged children and assessed whether micronutrient status affects this association. 273 participants, aged 7 to 10.5 years, were enrolled from three primary schools in Bangalore, India. Data on participants' aerobic fitness (20-m shuttle test), demographics, anthropometry, diet, physical activity, and micronutrient status were abstracted. School-wide exam scores in mathematics and Kannada language served as indicators of academic performance and were standardized by grade level. The strength of the fitness/achievement association was analyzed using Spearman's rank correlation, multiple variable logistic regression, and multi-level models. Significant positive correlations between aerobic capacity (VO2 peak) and academic scores in math and Kannada were observed (P < 0.05). After standardizing scores across grade levels and adjusting for school, gender, socioeconomic status, and weight status (BMI Z-score), children with greater aerobic capacities (mL * kg(-1) * min(-1)) had greater odds of scoring above average on math and Kannada exams (OR=1.08, 95% CI: 1.02 to 1.15 and OR=1.11, 95% CI: 1.04 to 1.18, respectively). This association remained significant after adjusting for micronutrient deficiencies. These findings provide preliminary evidence of a fitness/achievement association in Indian children. While the mechanisms by which aerobic fitness may be linked to academic achievement require further investigation, the results suggest that educators and policymakers should consider the adequacy of opportunities for physical activity and fitness in schools for both their physical and potential academic benefits.

Increasing students' physical activity during school physical education: rationale and protocol for the SELF-FIT cluster randomized controlled trial.

PubMed

Ha, Amy S; Lonsdale, Chris; Lubans, David R; Ng, Johan Y Y

2017-07-11

The Self-determined Exercise and Learning For FITness (SELF-FIT) is a multi-component school-based intervention based on tenets of self-determination theory. SELF-FIT aims to increase students' moderate-to-vigorous physical activity (MVPA) during physical education lessons, and enhance their autonomous motivation towards fitness activities. Using a cluster randomized controlled trial, we aim to examine the effects of the intervention on students' MVPA during school physical education. Secondary 2 students (approximately aged 14 years) from 26 classes in 26 different schools will be recruited. After baseline assessments, students will be randomized into either the experimental group or wait-list control group using a matched-pair randomization. Teachers allocated to the experimental group will attend two half-day workshops and deliver the SELF-FIT intervention for 8 weeks. The main intervention components include training teachers to teach in more need supportive ways, and conducting fitness exercises using a fitness dice with interchangeable faces. Other motivational components, such as playing music during classes, are also included. The primary outcome of the trial is students' MVPA during PE lessons. Secondary outcomes include students' leisure-time MVPA, perceived need support from teachers, need satisfaction, autonomous motivation towards physical education, intention to engage in physical activity, psychological well-being, and health-related fitness (cardiorespiratory and muscular fitness). Quantitative data will be analyzed using multilevel modeling approaches. Focus group interviews will also be conducted to assess students' perceptions of the intervention. The SELF-FIT intervention has been designed to improve students' health and well-being by using high-intensity activities in classes delivered by teachers who have been trained to be autonomy needs supportive. If successful, scalable interventions based on SELF-FIT could be applied in physical education at large. The trial is registered at the Australia New Zealand Clinical Trial Registry (Trial ID: ACTRN12615000633583 ; date of registration: 18 June 2015).

A Goal Programming Model for the Siting of Multilevel EMS Systems.

DTIC Science & Technology

1980-03-01

Management," unpublished Ph.D. thesis, University of Texas, Austin, Texas, 1971. -23- (11) Daskin , M. and E. Stern, " A Multiobjective Set Covering...GOAL PROGRAM4MING MODEL FOR THE SITING OF MULTILEVEL EMS SYSTE-ETC(U) UNM1AR 80 A CHARNES, J E STORBECK N000iA-75-C-569 WICLASSIFIED CCS-366 N...366 A GOAL PROGRAMMING MODEL FOR THE SITING OF MULTILEVEL EMS SYSTEMS by A . Charnes J. Storbeck March 1980 This project was partially supported by

Predicting Homework Effort: Support for a Domain-Specific, Multilevel Homework Model

ERIC Educational Resources Information Center

Trautwein, Ulrich; Ludtke, Oliver; Schnyder, Inge; Niggli, Alois

2006-01-01

According to the domain-specific, multilevel homework model proposed in the present study, students' homework effort is influenced by expectancy and value beliefs, homework characteristics, parental homework behavior, and conscientiousness. The authors used structural equation modeling and hierarchical linear modeling analyses to test the model in…

Suppressor Variables and Multilevel Mixture Modelling

ERIC Educational Resources Information Center

Darmawan, I Gusti Ngurah; Keeves, John P.

2006-01-01

A major issue in educational research involves taking into consideration the multilevel nature of the data. Since the late 1980s, attempts have been made to model social science data that conform to a nested structure. Among other models, two-level structural equation modelling or two-level path modelling and hierarchical linear modelling are two…

Multilevel examination of facility characteristics, social integration, and health for older adults living in nursing homes.

PubMed

Leedahl, Skye N; Chapin, Rosemary K; Little, Todd D

2015-01-01

Testing a model based on past research and theory, this study assessed relationships between facility characteristics (i.e., culture change efforts, social workers) and residents' social networks and social support across nursing homes; and examined relationships between multiple aspects of social integration (i.e., social networks, social capital, social engagement, social support) and mental and functional health for older adults in nursing homes. Data were collected at nursing homes using a planned missing data design with random sampling techniques. Data collection occurred at the individual-level through in-person structured interviews with older adult nursing home residents (N = 140) and at the facility-level (N = 30) with nursing home staff. The best fitting multilevel structural equation model indicated that the culture change subscale for relationships significantly predicted differences in residents' social networks. Additionally, social networks had a positive indirect relationship with mental and functional health among residents primarily via social engagement. Social capital had a positive direct relationship with both health outcomes. To predict better social integration and mental and functional health outcomes for nursing homes residents, study findings support prioritizing that close relationships exist among staff, residents, and the community as well as increased resident social engagement and social trust. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Intrinsic Emotional Fluctuation in Daily Negative Affect across Adulthood.

PubMed

Liu, Yin; Bangerter, Lauren R; Rovine, Michael J; Zarit, Steven H; Almeida, David M

2017-12-15

The study explored daily negative affect (NA) fluctuation, its associations with age, and its developmental characteristics. The sample (n = 790) was drawn from the Midlife Development in the United States; participants completed two 8-day daily diaries 10 years apart. Multilevel models were estimated within each diary component, where two single daily NA (depression and nervousness) and daily NA diversity were predicted separately by daily stressor exposures, physical health symptoms, age, gender, education, and neuroticism. The variances of within-person residual were output for single NA and NA diversity as intrinsic emotion fluctuation (IEF) within each diary component (i.e., controlled for within- and between-person contextual factors). Then multilevel growth models were fit to explore the developmental characteristics of day-to-day IEF across 10 years. At the daily level, older age was associated with less IEF in depression and nervousness. Over time, IEF in depression decreased. Additionally, IEF in NA diversity increased for older participants longitudinally. IEF represents a new conceptualization of midlife individuals' daily emotional ups and downs, specifically, the intrinsic within-person volatility of emotions. The magnitude of IEF and its longitudinal dynamics may have implications for health and well-being of middle-aged adults. © The Author(s) 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The effects of sports participation on the development of left ventricular mass in adolescent boys.

PubMed

Valente-Dos-Santos, João; Coelho-E-Silva, Manuel J; Castanheira, Joaquim; Machado-Rodrigues, Aristides M; Cyrino, Edilson S; Sherar, Lauren B; Esliger, Dale W; Elferink-Gemser, Marije T; Malina, Robert M

2015-01-01

To examine the contribution of body size, biological maturation, and nonelite sports participation to longitudinal changes of left ventricular mass (LVM) in healthy boys. One hundred and ten boys (11.0-14.5 years at baseline) were assessed biannually for 2 years. Stature, body mass, and four skinfolds were measured. Lean body mass (LBM) was estimated. Biological maturation was assessed as years from age at peak height velocity (APHV). Sports participation was assessed by questionnaire. LVM was obtained from M-mode echocardiograms using two-dimensional images. To account for the repeated measures within individual nature of longitudinal data, multilevel random effects regression analyses were used in the analysis. LVM increased on average 42 ± 18 g from 11 to 15 years (P < 0.05) and 76 ± 14 g from 3.5 years pre-APHV to 1.5 years post-APHV (P < 0.05). The multilevel model with the best statistical fit (Model B) showed that changes of 1 cm in stature, 1 year post-APHV, and 1 kg of LBM predicts 4.7, 0.5, and 1 g of LVM (P < 0.05), respectively. Among healthy, male adolescents aged 11-15 years individual differences in growth and biological maturation influence growth of LVM. Subcutaneous adiposity and sports participation were not associated with greater LVM. © 2015 Wiley Periodicals, Inc.

Associations among workplace environment, self-regulation, and domain-specific physical activities among white-collar workers: a multilevel longitudinal study.

PubMed

Watanabe, Kazuhiro; Kawakami, Norito; Otsuka, Yasumasa; Inoue, Shigeru

2018-05-31

Psychological and environmental determinants have been discussed for promoting physical activity among workers. However, few studies have investigated effects of both workplace environment and psychological determinants on physical activity. It is also unknown which domains of physical activities are promoted by these determinants. This study aimed to investigate main and interaction effects of workplace environment and individual self-regulation for physical activity on domain-specific physical activities among white-collar workers. A multi-site longitudinal study was conducted at baseline and about 5-month follow-up. A total of 49 worksites and employees within the worksites were recruited. Inclusion criteria for the worksites (a) were located in the Kanto area, Japan and (b) employed two or more employees. Employee inclusion criteria were (a) employed by the worksites, (b) aged 18 years or older, and (c) white-collar workers. For outcomes, three domain-specific physical activities (occupational, transport-related, and leisure-time) at baseline and follow-up were measured. For independent variables, self-regulation for physical activity, workplace environments (parking/bike, signs/bulletin boards/advertisements, stairs/elevators, physical activity/fitness facilities, work rules, written policies, and health promotion programs), and covariates at baseline were measured. Hierarchical Linear Modeling was conducted to investigate multilevel associations. Of the recruited worksites, 23 worksites and 562 employees, and 22 worksites and 459 employees completed the baseline and the follow-up surveys. As results of Hierarchical Linear Modeling, stairs/elevator (γ=3.80 [SE=1.80], p<0.05), physical activity/fitness facilities (γ=4.98 [SE=1.09], p<0.01), and written policies (γ=2.10 [SE=1.02], p<0.05) were significantly and positively associated with occupational physical activity. Self-regulation for physical activity was associated significantly with leisure-time physical activity (γ=0.09 [SE=0.04], p<0.05) but insignificantly with occupational and transport-related physical activity (γ=0.11 [SE=0.16] and γ=-0.00 [SE=0.06]). Significant interaction effects of workplace environments (physical activity/fitness facilities, work rules, and written policies) and self-regulation were observed on transport-related and leisure-time physical activity. Workplace environments such as physical activity/fitness facilities, written policies, work rules, and signs for stair use at stairs and elevators; self-regulation for physical activity; and their interactions may be effective to promote three domain-specific physical activities. This study has practical implications for designing multi-component interventions that include both environmental and psychological approaches to increase effect sizes to promote overall physical activity.

Analyzing average and conditional effects with multigroup multilevel structural equation models

PubMed Central

Mayer, Axel; Nagengast, Benjamin; Fletcher, John; Steyer, Rolf

2014-01-01

Conventionally, multilevel analysis of covariance (ML-ANCOVA) has been the recommended approach for analyzing treatment effects in quasi-experimental multilevel designs with treatment application at the cluster-level. In this paper, we introduce the generalized ML-ANCOVA with linear effect functions that identifies average and conditional treatment effects in the presence of treatment-covariate interactions. We show how the generalized ML-ANCOVA model can be estimated with multigroup multilevel structural equation models that offer considerable advantages compared to traditional ML-ANCOVA. The proposed model takes into account measurement error in the covariates, sampling error in contextual covariates, treatment-covariate interactions, and stochastic predictors. We illustrate the implementation of ML-ANCOVA with an example from educational effectiveness research where we estimate average and conditional effects of early transition to secondary schooling on reading comprehension. PMID:24795668

Community-level cohesion without cooperation.

PubMed

Tikhonov, Mikhail

2016-06-16

Recent work draws attention to community-community encounters ('coalescence') as likely an important factor shaping natural ecosystems. This work builds on MacArthur's classic model of competitive coexistence to investigate such community-level competition in a minimal theoretical setting. It is shown that the ability of a species to survive a coalescence event is best predicted by a community-level 'fitness' of its native community rather than the intrinsic performance of the species itself. The model presented here allows formalizing a macroscopic perspective whereby a community harboring organisms at varying abundances becomes equivalent to a single organism expressing genes at different levels. While most natural communities do not satisfy the strict criteria of multicellularity developed by multi-level selection theory, the effective cohesion described here is a generic consequence of resource partitioning, requires no cooperative interactions, and can be expected to be widespread in microbial ecosystems.

The association of fitness and school absenteeism across gender and poverty: a prospective multilevel analysis in New York City middle schools.

PubMed

D'Agostino, Emily M; Day, Sophia E; Konty, Kevin J; Larkin, Michael; Saha, Subir; Wyka, Katarzyna

2018-03-01

One-fifth to one-third of students in high poverty, urban school districts do not attend school regularly (missing ≥6 days/year). Fitness is shown to be associated with absenteeism, although this relationship may differ across poverty and gender subgroups. Six cohorts of New York City public school students were followed up from grades 5 to 8 during 2006/2007-2012/2013 (n = 349,381). Stratified three-level longitudinal generalized linear mixed models were used to test the association between changes in fitness and 1-year lagged child-specific days absent across gender and poverty. In girls attending schools in high/very high poverty areas, greater improvements in fitness the prior year were associated with greater reductions in absenteeism (P = .034). Relative to the reference group (>20% decrease in fitness composite percentile scores from the prior year), girls with a large increase in fitness (>20%) demonstrated 10.3% fewer days absent (incidence rate ratio [IRR] 95% confidence interval [CI]: 0.834, 0.964), followed by those who had a 10%-20% increase in fitness (9.2%; IRR 95% CI: 0.835, 0.987), no change (5.4%; IRR 95% CI: 0.887, 1.007), and a 10%-20% decrease in fitness (3.8%; IRR 95% CI: 0.885, 1.045). In girls attending schools in low/mid poverty areas, fitness and absenteeism also had an inverse relationship, but no clear trend emerged. In boys, fitness and absenteeism had an inverse relationship but was not significant in either poverty group. Fitness improvements may be more important to reducing absenteeism in high/very high poverty girls compared with low/mid poverty girls and both high/very high and low/mid poverty boys. Expanding school-based physical activity programs for youth particularly in high poverty neighborhoods may increase student attendance. Copyright © 2018 Elsevier Inc. All rights reserved.

Construction of Covariance Functions with Variable Length Fields

NASA Technical Reports Server (NTRS)

Gaspari, Gregory; Cohn, Stephen E.; Guo, Jing; Pawson, Steven

2005-01-01

This article focuses on construction, directly in physical space, of three-dimensional covariance functions parametrized by a tunable length field, and on an application of this theory to reproduce the Quasi-Biennial Oscillation (QBO) in the Goddard Earth Observing System, Version 4 (GEOS-4) data assimilation system. These Covariance models are referred to as multi-level or nonseparable, to associate them with the application where a multi-level covariance with a large troposphere to stratosphere length field gradient is used to reproduce the QBO from sparse radiosonde observations in the tropical lower stratosphere. The multi-level covariance functions extend well-known single level covariance functions depending only on a length scale. Generalizations of the first- and third-order autoregressive covariances in three dimensions are given, providing multi-level covariances with zero and three derivatives at zero separation, respectively. Multi-level piecewise rational covariances with two continuous derivatives at zero separation are also provided. Multi-level powerlaw covariances are constructed with continuous derivatives of all orders. Additional multi-level covariance functions are constructed using the Schur product of single and multi-level covariance functions. A multi-level powerlaw covariance used to reproduce the QBO in GEOS-4 is described along with details of the assimilation experiments. The new covariance model is shown to represent the vertical wind shear associated with the QBO much more effectively than in the baseline GEOS-4 system.

General method to find the attractors of discrete dynamic models of biological systems.

PubMed

Gan, Xiao; Albert, Réka

2018-04-01

Analyzing the long-term behaviors (attractors) of dynamic models of biological networks can provide valuable insight. We propose a general method that can find the attractors of multilevel discrete dynamical systems by extending a method that finds the attractors of a Boolean network model. The previous method is based on finding stable motifs, subgraphs whose nodes' states can stabilize on their own. We extend the framework from binary states to any finite discrete levels by creating a virtual node for each level of a multilevel node, and describing each virtual node with a quasi-Boolean function. We then create an expanded representation of the multilevel network, find multilevel stable motifs and oscillating motifs, and identify attractors by successive network reduction. In this way, we find both fixed point attractors and complex attractors. We implemented an algorithm, which we test and validate on representative synthetic networks and on published multilevel models of biological networks. Despite its primary motivation to analyze biological networks, our motif-based method is general and can be applied to any finite discrete dynamical system.

General method to find the attractors of discrete dynamic models of biological systems

NASA Astrophysics Data System (ADS)

Gan, Xiao; Albert, Réka

2018-04-01

Analyzing the long-term behaviors (attractors) of dynamic models of biological networks can provide valuable insight. We propose a general method that can find the attractors of multilevel discrete dynamical systems by extending a method that finds the attractors of a Boolean network model. The previous method is based on finding stable motifs, subgraphs whose nodes' states can stabilize on their own. We extend the framework from binary states to any finite discrete levels by creating a virtual node for each level of a multilevel node, and describing each virtual node with a quasi-Boolean function. We then create an expanded representation of the multilevel network, find multilevel stable motifs and oscillating motifs, and identify attractors by successive network reduction. In this way, we find both fixed point attractors and complex attractors. We implemented an algorithm, which we test and validate on representative synthetic networks and on published multilevel models of biological networks. Despite its primary motivation to analyze biological networks, our motif-based method is general and can be applied to any finite discrete dynamical system.

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…

Physical education policy compliance and Latino children’s fitness: Does the association vary by school neighborhood socioeconomic advantage?

PubMed Central

Sanchez-Vaznaugh, Emma V.; Goldman Rosas, Lisa; Fernández-Peña, José Ramón; Baek, Jonggyu; Egerter, Susan; Sánchez, Brisa N.

2017-01-01

Objectives To investigate the contribution of school neighborhood socioeconomic advantage to the association between school-district physical education policy compliance in California public schools and Latino students’ physical fitness. Methods Cross-sectional Fitnessgram data for public-school students were linked with school- and district-level information, district-level physical education policy compliance from 2004–2005 and 2005–2006, and 2000 United States Census data. Multilevel logistic regression models examined whether income and education levels in school neighborhoods moderated the effects of district-level physical education policy compliance on Latino fifth-graders’ fitness levels. Results Physical education compliance data were available for 48 California school districts, which included 64,073 Latino fifth-graders. Fewer than half (23, or 46%) of these districts were found to be in compliance, and only 16% of Latino fifth-graders attended schools in compliant districts. Overall, there was a positive association between district compliance with physical education policy and fitness (OR, 95%CI: 1.38, 1.07, 1.78) adjusted for covariates. There was no significant interaction between school neighborhood socioeconomic advantage and physical education policy compliance (p>.05): there was a positive pattern in the association between school district compliance with physical education policy and student fitness levels across levels of socioeconomic advantage, though the association was not always significant. Conclusions Across neighborhoods with varying levels of socioeconomic advantage, increasing physical education policy compliance in elementary schools may be an effective strategy for improving fitness among Latino children. PMID:28591139

Comparative assessment of analytical approaches to quantify the risk for introduction of rare animal diseases: the example of avian influenza in Spain.

PubMed

Sánchez-Vizcaíno, Fernando; Perez, Andrés; Martínez-López, Beatriz; Sánchez-Vizcaíno, José Manuel

2012-08-01

Trade of animals and animal products imposes an uncertain and variable risk for exotic animal diseases introduction into importing countries. Risk analysis provides importing countries with an objective, transparent, and internationally accepted method for assessing that risk. Over the last decades, European Union countries have conducted probabilistic risk assessments quite frequently to quantify the risk for rare animal diseases introduction into their territories. Most probabilistic animal health risk assessments have been typically classified into one-level and multilevel binomial models. One-level models are more simple than multilevel models because they assume that animals or products originate from one single population. However, it is unknown whether such simplification may result in substantially different results compared to those obtained through the use of multilevel models. Here, data used on a probabilistic multilevel binomial model formulated to assess the risk for highly pathogenic avian influenza introduction into Spain were reanalyzed using a one-level binomial model and their outcomes were compared. An alternative ordinal model is also proposed here, which makes use of simpler assumptions and less information compared to those required by traditional one-level and multilevel approaches. Results suggest that, at least under certain circumstances, results of the one-level and ordinal approaches are similar to those obtained using multilevel models. Consequently, we argue that, when data are insufficient to run traditional probabilistic models, the ordinal approach presented here may be a suitable alternative to rank exporting countries in terms of the risk that they impose for the spread of rare animal diseases into disease-free countries. © 2012 Society for Risk Analysis.

Male tolerance and male–male bonds in a multilevel primate society

PubMed Central

Patzelt, Annika; Kopp, Gisela H.; Ndao, Ibrahima; Kalbitzer, Urs; Zinner, Dietmar

2014-01-01

Male relationships in most species of mammals generally are characterized by intense intrasexual competition, with little bonding among unrelated individuals. In contrast, human societies are characterized by high levels of cooperation and strong bonds among both related and unrelated males. The emergence of cooperative male–male relationships has been linked to the multilevel structure of traditional human societies. Based on an analysis of the patterns of spatial and social interaction in combination with genetic relatedness data of wild Guinea baboons (Papio papio), we show that this species exhibits a multilevel social organization in which males maintain strong bonds and are highly tolerant of each other. Several “units” of males with their associated females form “parties,” which team up as “gangs.” Several gangs of the same “community” use the same home range. Males formed strong bonds predominantly within parties; however, these bonds were not correlated with genetic relatedness. Agonistic interactions were relatively rare and were restricted to a few dyads. Although the social organization of Guinea baboons resembles that of hamadryas baboons, we found stronger male–male affiliation and more elaborate greeting rituals among male Guinea baboons and less aggression toward females. Thus, the social relationships of male Guinea baboons differ markedly from those of other members of the genus, adding valuable comparative data to test hypotheses regarding social evolution. We suggest that this species constitutes an intriguing model to study the predictors and fitness benefits of male bonds, thus contributing to a better understanding of the evolution of this important facet of human social behavior. PMID:25201960

Male tolerance and male-male bonds in a multilevel primate society.

PubMed

Patzelt, Annika; Kopp, Gisela H; Ndao, Ibrahima; Kalbitzer, Urs; Zinner, Dietmar; Fischer, Julia

2014-10-14

Male relationships in most species of mammals generally are characterized by intense intrasexual competition, with little bonding among unrelated individuals. In contrast, human societies are characterized by high levels of cooperation and strong bonds among both related and unrelated males. The emergence of cooperative male-male relationships has been linked to the multilevel structure of traditional human societies. Based on an analysis of the patterns of spatial and social interaction in combination with genetic relatedness data of wild Guinea baboons (Papio papio), we show that this species exhibits a multilevel social organization in which males maintain strong bonds and are highly tolerant of each other. Several "units" of males with their associated females form "parties," which team up as "gangs." Several gangs of the same "community" use the same home range. Males formed strong bonds predominantly within parties; however, these bonds were not correlated with genetic relatedness. Agonistic interactions were relatively rare and were restricted to a few dyads. Although the social organization of Guinea baboons resembles that of hamadryas baboons, we found stronger male-male affiliation and more elaborate greeting rituals among male Guinea baboons and less aggression toward females. Thus, the social relationships of male Guinea baboons differ markedly from those of other members of the genus, adding valuable comparative data to test hypotheses regarding social evolution. We suggest that this species constitutes an intriguing model to study the predictors and fitness benefits of male bonds, thus contributing to a better understanding of the evolution of this important facet of human social behavior.

Integrating Space with Place in Health Research: A Multilevel Spatial Investigation Using Child Mortality in 1880 Newark, New Jersey

PubMed Central

Xu, Hongwei; Logan, John R.; Short, Susan E.

2014-01-01

Research on neighborhoods and health increasingly acknowledges the need to conceptualize, measure, and model spatial features of social and physical environments. In ignoring underlying spatial dynamics, we run the risk of biased statistical inference and misleading results. In this paper, we propose an integrated multilevel-spatial approach for Poisson models of discrete responses. In an empirical example of child mortality in 1880 Newark, New Jersey, we compare this multilevel-spatial approach with the more typical aspatial multilevel approach. Results indicate that spatially-defined egocentric neighborhoods, or distance-based measures, outperform administrative areal units, such as census units. In addition, although results did not vary by specific definitions of egocentric neighborhoods, they were sensitive to geographic scale and modeling strategy. Overall, our findings confirm that adopting a spatial-multilevel approach enhances our ability to disentangle the effect of space from that of place, and point to the need for more careful spatial thinking in population research on neighborhoods and health. PMID:24763980

A multi-level pore-water sampler for permeable sediments

USGS Publications Warehouse

Martin, J.B.; Hartl, K.M.; Corbett, D.R.; Swarzenski, P.W.; Cable, J.E.

2003-01-01

The construction and operation of a multi-level piezometer (multisampler) designed to collect pore water from permeable sediments up to 230 cm below the sediment-water interface is described. Multisamplers are constructed from 1 1/2 inch schedule 80 PVC pipe. One-quarter-inch flexible PVC tubing leads from eight ports at variable depths to a 1 1/2 inch tee fitting at the top of the PVC pipe. Multisamplers are driven into the sediments using standard fence-post drivers. Water is pumped from the PVC tubing with a peristaltic pump. Field tests in Banana River Lagoon, Florida, demonstrate the utility of multisamplers. These tests include collection of multiple samples from the permeable sediments and reveal mixing between shallow pore water and overlying lagoon water.

Interviewer effects on non-response propensity in longitudinal surveys: a multilevel modelling approach

PubMed Central

Vassallo, Rebecca; Durrant, Gabriele B; Smith, Peter W F; Goldstein, Harvey

2015-01-01

The paper investigates two different multilevel approaches, the multilevel cross-classified and the multiple-membership models, for the analysis of interviewer effects on wave non-response in longitudinal surveys. The models proposed incorporate both interviewer and area effects to account for the non-hierarchical structure, the influence of potentially more than one interviewer across waves and possible confounding of area and interviewer effects arising from the non-random allocation of interviewers across areas. The methods are compared by using a data set: the UK Family and Children Survey. PMID:25598587

Adolescent Trajectories of Aerobic Fitness and Adiposity as Markers of Cardiometabolic Risk in Adulthood.

PubMed

Jackowski, S A; Eisenmann, J C; Sherar, L B; Bailey, D A; Baxter-Jones, A D G

2017-01-01

The aim of this study was to investigate whether adolescent growth trajectories of aerobic fitness and adiposity were associated with mid-adulthood cardiometabolic risk (CMR). Participants were drawn from the Saskatchewan Growth and Development Study (1963-1973). Adolescent growth trajectories for maximal aerobic capacity (absolute VO 2 (AbsVO 2 )), skinfolds (SF), representing total body (Sum6SF) and central adiposity (TrunkSF), and body mass index (BMI) were determined from 7 to 17 years of age. In mid-adulthood (40 to 50 years of age), 61 individuals (23 females) returned for follow-ups. A CMR score was calculated to group participants as displaying either high or a low CMR. Multilevel hierarchical models were constructed, comparing the adolescent growth trajectories of AbsVO 2, Sum6SF, TrunkSF, and BMI between CMR groupings. There were no significant differences in the adolescent development of AbsVO 2, Sum6SF, TrunkSF, and BMI between adult CMR groupings ( p > 0.05). Individuals with high CMR accrued 62% greater adjusted total body fat percentage from adolescence to adulthood ( p =0.03). Growth trajectories of adolescent aerobic fitness and adiposity do not appear to be associated with mid-adulthood CMR. Individuals should be encouraged to participate in behaviours that promote healthy aerobic fitness and adiposity levels throughout life to reduce lifelong CMR.

Adolescent Trajectories of Aerobic Fitness and Adiposity as Markers of Cardiometabolic Risk in Adulthood

PubMed Central

Eisenmann, J. C.; Sherar, L. B.; Bailey, D. A.; Baxter-Jones, A. D. G.

2017-01-01

Purpose The aim of this study was to investigate whether adolescent growth trajectories of aerobic fitness and adiposity were associated with mid-adulthood cardiometabolic risk (CMR). Methods Participants were drawn from the Saskatchewan Growth and Development Study (1963–1973). Adolescent growth trajectories for maximal aerobic capacity (absolute VO2 (AbsVO2)), skinfolds (SF), representing total body (Sum6SF) and central adiposity (TrunkSF), and body mass index (BMI) were determined from 7 to 17 years of age. In mid-adulthood (40 to 50 years of age), 61 individuals (23 females) returned for follow-ups. A CMR score was calculated to group participants as displaying either high or a low CMR. Multilevel hierarchical models were constructed, comparing the adolescent growth trajectories of AbsVO2, Sum6SF, TrunkSF, and BMI between CMR groupings. Results There were no significant differences in the adolescent development of AbsVO2, Sum6SF, TrunkSF, and BMI between adult CMR groupings (p > 0.05). Individuals with high CMR accrued 62% greater adjusted total body fat percentage from adolescence to adulthood (p=0.03). Conclusions Growth trajectories of adolescent aerobic fitness and adiposity do not appear to be associated with mid-adulthood CMR. Individuals should be encouraged to participate in behaviours that promote healthy aerobic fitness and adiposity levels throughout life to reduce lifelong CMR. PMID:29279776

How to compare cross-lagged associations in a multilevel autoregressive model.

PubMed

Schuurman, Noémi K; Ferrer, Emilio; de Boer-Sonnenschein, Mieke; Hamaker, Ellen L

2016-06-01

By modeling variables over time it is possible to investigate the Granger-causal cross-lagged associations between variables. By comparing the standardized cross-lagged coefficients, the relative strength of these associations can be evaluated in order to determine important driving forces in the dynamic system. The aim of this study was twofold: first, to illustrate the added value of a multilevel multivariate autoregressive modeling approach for investigating these associations over more traditional techniques; and second, to discuss how the coefficients of the multilevel autoregressive model should be standardized for comparing the strength of the cross-lagged associations. The hierarchical structure of multilevel multivariate autoregressive models complicates standardization, because subject-based statistics or group-based statistics can be used to standardize the coefficients, and each method may result in different conclusions. We argue that in order to make a meaningful comparison of the strength of the cross-lagged associations, the coefficients should be standardized within persons. We further illustrate the bivariate multilevel autoregressive model and the standardization of the coefficients, and we show that disregarding individual differences in dynamics can prove misleading, by means of an empirical example on experienced competence and exhaustion in persons diagnosed with burnout. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Post test review of a single car test of multi-level passenger equipment

DOT National Transportation Integrated Search

2008-04-22

The single car test of multi-level equipment described in : this paper was designed to help evaluate the crashworthiness of : a multi-level car in a controlled collision. The data collected : from this test will be used to refine engineering models. ...

Evaluating multi-level models to test occupancy state responses of Plethodontid salamanders

USGS Publications Warehouse

Kroll, Andrew J.; Garcia, Tiffany S.; Jones, Jay E.; Dugger, Catherine; Murden, Blake; Johnson, Josh; Peerman, Summer; Brintz, Ben; Rochelle, Michael

2015-01-01

Plethodontid salamanders are diverse and widely distributed taxa and play critical roles in ecosystem processes. Due to salamander use of structurally complex habitats, and because only a portion of a population is available for sampling, evaluation of sampling designs and estimators is critical to provide strong inference about Plethodontid ecology and responses to conservation and management activities. We conducted a simulation study to evaluate the effectiveness of multi-scale and hierarchical single-scale occupancy models in the context of a Before-After Control-Impact (BACI) experimental design with multiple levels of sampling. Also, we fit the hierarchical single-scale model to empirical data collected for Oregon slender and Ensatina salamanders across two years on 66 forest stands in the Cascade Range, Oregon, USA. All models were fit within a Bayesian framework. Estimator precision in both models improved with increasing numbers of primary and secondary sampling units, underscoring the potential gains accrued when adding secondary sampling units. Both models showed evidence of estimator bias at low detection probabilities and low sample sizes; this problem was particularly acute for the multi-scale model. Our results suggested that sufficient sample sizes at both the primary and secondary sampling levels could ameliorate this issue. Empirical data indicated Oregon slender salamander occupancy was associated strongly with the amount of coarse woody debris (posterior mean = 0.74; SD = 0.24); Ensatina occupancy was not associated with amount of coarse woody debris (posterior mean = -0.01; SD = 0.29). Our simulation results indicate that either model is suitable for use in an experimental study of Plethodontid salamanders provided that sample sizes are sufficiently large. However, hierarchical single-scale and multi-scale models describe different processes and estimate different parameters. As a result, we recommend careful consideration of study questions and objectives prior to sampling data and fitting models.

Parameter Recovery for the 1-P HGLLM with Non-Normally Distributed Level-3 Residuals

ERIC Educational Resources Information Center

Kara, Yusuf; Kamata, Akihito

2017-01-01

A multilevel Rasch model using a hierarchical generalized linear model is one approach to multilevel item response theory (IRT) modeling and is referred to as a one-parameter hierarchical generalized linear logistic model (1-P HGLLM). Although it has the flexibility to model nested structure of data with covariates, the model assumes the normality…

Association between tobacco control policies and smoking behaviour among adolescents in 29 European countries.

PubMed

Hublet, Anne; Schmid, Holger; Clays, Els; Godeau, Emmanuelle; Gabhainn, Saoirse Nic; Joossens, Luk; Maes, Lea

2009-11-01

To investigate the associations between well-known, cost-effective tobacco control policies at country level and smoking prevalence among 15-year-old adolescents. Multi-level modelling based on the 2005-06 Health Behaviour in School-aged Children Study, a cross-national study at individual level, and with country-level variables from the Tobacco Control Scale and published country-level databases. Twenty-nine European countries. A total of 25 599 boys and 26 509 girls. Self-reported regular smoking defined as at least weekly smoking, including daily smoking (dichotomous). Interaction effects between gender and smoking policies were identified, therefore boys and girls were analysed separately. Large cross-national differences in smoking prevalence were documented. Intraclass correlations (ICC) of 0.038 (boys) and 0.035 (girls) were found. In the final multi-level model for boys, besides the significance of the individual variables such as family affluence, country-level affluence and the legality of vending machines were related significantly to regular smoking [b(country affluence) = -0.010; b(partial restriction vending machines) = -0.366, P < 0.05]. Price policy was of borderline significance [b(price policy) = -0.026, P = 0.050]. All relationships were in the expected direction. The model fit is not as good for girls; only the legality of vending machines had a borderline significance in the final model [b(total ban vending machines) = -0.372, P = 0.06]. For boys, some of the currently recommended tobacco control policies may help to reduce smoking prevalence. However, the model is less suitable for girls, indicating gender differences in the potential efficacy of smoking policies. Future research should address this issue.

Physical fitness and academic performance in primary school children with and without a social disadvantage.

PubMed

de Greeff, J W; Hartman, E; Mullender-Wijnsma, M J; Bosker, R J; Doolaard, S; Visscher, C

2014-10-01

This study examined the differences between children with a low socioeconomic status [socially disadvantaged children (SDC)] and children without this disadvantage (non-SDC) on physical fitness and academic performance. In addition, this study determined the association between physical fitness and academic performance, and investigated the possible moderator effect of SDC. Data on 544 children were collected and analysed (130 SDC, 414 non-SDC, mean age = 8.0 ± 0.7). Physical fitness was measured with tests for cardiovascular and muscular fitness. Academic performance was evaluated using scores on mathematics, spelling and reading. SDC did not differ on physical fitness, compared with non-SDC, but scored significantly lower on academic performance. In the total group, multilevel analysis showed positive associations between cardiovascular fitness and mathematics (β = 0.23), and between cardiovascular fitness and spelling (β = 0.16), but not with reading. No associations were found between muscular fitness and academic performance. A significant interaction effect between SDC and cardiovascular fitness was found for spelling. To conclude, results showed a specific link between cardiovascular fitness and mathematics, regardless of socioeconomic status. SDC did moderate the relationship between cardiovascular fitness and spelling. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Evidencing Learning Outcomes: A Multi-Level, Multi-Dimensional Course Alignment Model

ERIC Educational Resources Information Center

Sridharan, Bhavani; Leitch, Shona; Watty, Kim

2015-01-01

This conceptual framework proposes a multi-level, multi-dimensional course alignment model to implement a contextualised constructive alignment of rubric design that authentically evidences and assesses learning outcomes. By embedding quality control mechanisms at each level for each dimension, this model facilitates the development of an aligned…

Examining Elementary Social Studies Marginalization: A Multilevel Model

ERIC Educational Resources Information Center

Fitchett, Paul G.; Heafner, Tina L.; Lambert, Richard G.

2014-01-01

Utilizing data from the National Center for Education Statistics Schools and Staffing Survey (SASS), a multilevel model (Hierarchical Linear Model) was developed to examine the association of teacher/classroom and state level indicators on reported elementary social studies instructional time. Findings indicated that state testing policy was a…

Handling Correlations between Covariates and Random Slopes in Multilevel Models

ERIC Educational Resources Information Center

Bates, Michael David; Castellano, Katherine E.; Rabe-Hesketh, Sophia; Skrondal, Anders

2014-01-01

This article discusses estimation of multilevel/hierarchical linear models that include cluster-level random intercepts and random slopes. Viewing the models as structural, the random intercepts and slopes represent the effects of omitted cluster-level covariates that may be correlated with included covariates. The resulting correlations between…

Modeling Learning in Doubly Multilevel Binary Longitudinal Data Using Generalized Linear Mixed Models: An Application to Measuring and Explaining Word Learning.

PubMed

Cho, Sun-Joo; Goodwin, Amanda P

2016-04-01

When word learning is supported by instruction in experimental studies for adolescents, word knowledge outcomes tend to be collected from complex data structure, such as multiple aspects of word knowledge, multilevel reader data, multilevel item data, longitudinal design, and multiple groups. This study illustrates how generalized linear mixed models can be used to measure and explain word learning for data having such complexity. Results from this application provide deeper understanding of word knowledge than could be attained from simpler models and show that word knowledge is multidimensional and depends on word characteristics and instructional contexts.

Modeling Fetal Weight for Gestational Age: A Comparison of a Flexible Multi-level Spline-based Model with Other Approaches

PubMed Central

Villandré, Luc; Hutcheon, Jennifer A; Perez Trejo, Maria Esther; Abenhaim, Haim; Jacobsen, Geir; Platt, Robert W

2011-01-01

We present a model for longitudinal measures of fetal weight as a function of gestational age. We use a linear mixed model, with a Box-Cox transformation of fetal weight values, and restricted cubic splines, in order to flexibly but parsimoniously model median fetal weight. We systematically compare our model to other proposed approaches. All proposed methods are shown to yield similar median estimates, as evidenced by overlapping pointwise confidence bands, except after 40 completed weeks, where our method seems to produce estimates more consistent with observed data. Sex-based stratification affects the estimates of the random effects variance-covariance structure, without significantly changing sex-specific fitted median values. We illustrate the benefits of including sex-gestational age interaction terms in the model over stratification. The comparison leads to the conclusion that the selection of a model for fetal weight for gestational age can be based on the specific goals and configuration of a given study without affecting the precision or value of median estimates for most gestational ages of interest. PMID:21931571

A multilevel model for cardiovascular disease prevalence in the US and its application to micro area prevalence estimates.

PubMed

Congdon, Peter

2009-01-30

Estimates of disease prevalence for small areas are increasingly required for the allocation of health funds according to local need. Both individual level and geographic risk factors are likely to be relevant to explaining prevalence variations, and in turn relevant to the procedure for small area prevalence estimation. Prevalence estimates are of particular importance for major chronic illnesses such as cardiovascular disease. A multilevel prevalence model for cardiovascular outcomes is proposed that incorporates both survey information on patient risk factors and the effects of geographic location. The model is applied to derive micro area prevalence estimates, specifically estimates of cardiovascular disease for Zip Code Tabulation Areas in the USA. The model incorporates prevalence differentials by age, sex, ethnicity and educational attainment from the 2005 Behavioral Risk Factor Surveillance System survey. Influences of geographic context are modelled at both county and state level, with the county effects relating to poverty and urbanity. State level influences are modelled using a random effects approach that allows both for spatial correlation and spatial isolates. To assess the importance of geographic variables, three types of model are compared: a model with person level variables only; a model with geographic effects that do not interact with person attributes; and a full model, allowing for state level random effects that differ by ethnicity. There is clear evidence that geographic effects improve statistical fit. Geographic variations in disease prevalence partly reflect the demographic composition of area populations. However, prevalence variations may also show distinct geographic 'contextual' effects. The present study demonstrates by formal modelling methods that improved explanation is obtained by allowing for distinct geographic effects (for counties and states) and for interaction between geographic and person variables. Thus an appropriate methodology to estimate prevalence at small area level should include geographic effects as well as person level demographic variables.

A multilevel model for cardiovascular disease prevalence in the US and its application to micro area prevalence estimates

PubMed Central

Congdon, Peter

2009-01-01

Background Estimates of disease prevalence for small areas are increasingly required for the allocation of health funds according to local need. Both individual level and geographic risk factors are likely to be relevant to explaining prevalence variations, and in turn relevant to the procedure for small area prevalence estimation. Prevalence estimates are of particular importance for major chronic illnesses such as cardiovascular disease. Methods A multilevel prevalence model for cardiovascular outcomes is proposed that incorporates both survey information on patient risk factors and the effects of geographic location. The model is applied to derive micro area prevalence estimates, specifically estimates of cardiovascular disease for Zip Code Tabulation Areas in the USA. The model incorporates prevalence differentials by age, sex, ethnicity and educational attainment from the 2005 Behavioral Risk Factor Surveillance System survey. Influences of geographic context are modelled at both county and state level, with the county effects relating to poverty and urbanity. State level influences are modelled using a random effects approach that allows both for spatial correlation and spatial isolates. Results To assess the importance of geographic variables, three types of model are compared: a model with person level variables only; a model with geographic effects that do not interact with person attributes; and a full model, allowing for state level random effects that differ by ethnicity. There is clear evidence that geographic effects improve statistical fit. Conclusion Geographic variations in disease prevalence partly reflect the demographic composition of area populations. However, prevalence variations may also show distinct geographic 'contextual' effects. The present study demonstrates by formal modelling methods that improved explanation is obtained by allowing for distinct geographic effects (for counties and states) and for interaction between geographic and person variables. Thus an appropriate methodology to estimate prevalence at small area level should include geographic effects as well as person level demographic variables. PMID:19183458

Modeling of BN Lifetime Prediction of a System Based on Integrated Multi-Level Information

PubMed Central

Wang, Xiaohong; Wang, Lizhi

2017-01-01

Predicting system lifetime is important to ensure safe and reliable operation of products, which requires integrated modeling based on multi-level, multi-sensor information. However, lifetime characteristics of equipment in a system are different and failure mechanisms are inter-coupled, which leads to complex logical correlations and the lack of a uniform lifetime measure. Based on a Bayesian network (BN), a lifetime prediction method for systems that combine multi-level sensor information is proposed. The method considers the correlation between accidental failures and degradation failure mechanisms, and achieves system modeling and lifetime prediction under complex logic correlations. This method is applied in the lifetime prediction of a multi-level solar-powered unmanned system, and the predicted results can provide guidance for the improvement of system reliability and for the maintenance and protection of the system. PMID:28926930

Modeling of BN Lifetime Prediction of a System Based on Integrated Multi-Level Information.

PubMed

Wang, Jingbin; Wang, Xiaohong; Wang, Lizhi

2017-09-15

Predicting system lifetime is important to ensure safe and reliable operation of products, which requires integrated modeling based on multi-level, multi-sensor information. However, lifetime characteristics of equipment in a system are different and failure mechanisms are inter-coupled, which leads to complex logical correlations and the lack of a uniform lifetime measure. Based on a Bayesian network (BN), a lifetime prediction method for systems that combine multi-level sensor information is proposed. The method considers the correlation between accidental failures and degradation failure mechanisms, and achieves system modeling and lifetime prediction under complex logic correlations. This method is applied in the lifetime prediction of a multi-level solar-powered unmanned system, and the predicted results can provide guidance for the improvement of system reliability and for the maintenance and protection of the system.

Multilevel Modeling of Two Cyclical Processes: Extending Differential Structural Equation Modeling to Nonlinear Coupled Systems

ERIC Educational Resources Information Center

Butner, Jonathan; Amazeen, Polemnia G.; Mulvey, Genna M.

2005-01-01

The authors present a dynamical multilevel model that captures changes over time in the bidirectional, potentially asymmetric influence of 2 cyclical processes. S. M. Boker and J. Graham's (1998) differential structural equation modeling approach was expanded to the case of a nonlinear coupled oscillator that is common in bimanual coordination…

Coupling Longitudinal Data and Multilevel Modeling to Examine the Antecedents and Consequences of Jealousy Experiences in Romantic Relationships: A Test of the Relational Turbulence Model

ERIC Educational Resources Information Center

Theiss, Jennifer A.; Solomon, Denise Haunani

2006-01-01

We used longitudinal data and multilevel modeling to examine how intimacy, relational uncertainty, and failed attempts at interdependence influence emotional, cognitive, and communicative responses to romantic jealousy, and how those experiences shape subsequent relationship characteristics. The relational turbulence model (Solomon & Knobloch,…

A General Multilevel SEM Framework for Assessing Multilevel Mediation

ERIC Educational Resources Information Center

Preacher, Kristopher J.; Zyphur, Michael J.; Zhang, Zhen

2010-01-01

Several methods for testing mediation hypotheses with 2-level nested data have been proposed by researchers using a multilevel modeling (MLM) paradigm. However, these MLM approaches do not accommodate mediation pathways with Level-2 outcomes and may produce conflated estimates of between- and within-level components of indirect effects. Moreover,…

The Consequences of Ignoring Individuals' Mobility in Multilevel Growth Models: A Monte Carlo Study

ERIC Educational Resources Information Center

Luo, Wen; Kwok, Oi-man

2012-01-01

In longitudinal multilevel studies, especially in educational settings, it is fairly common that participants change their group memberships over time (e.g., students switch to different schools). Participant's mobility changes the multilevel data structure from a purely hierarchical structure with repeated measures nested within individuals and…

The Effects of Autonomy and Empowerment on Employee Turnover: Test of a Multilevel Model in Teams

ERIC Educational Resources Information Center

Liu, Dong; Zhang, Shu; Wang, Lei; Lee, Thomas W.

2011-01-01

Extending research on voluntary turnover in the team setting, this study adopts a multilevel self-determination theoretical approach to examine the unique roles of individual and social-contextual motivational precursors, autonomy orientation and autonomy support, in reducing team member voluntary turnover. Analysis of multilevel time-lagged data…

Neighbourhood socioeconomic inequality and gender differences in body mass index: The role of unhealthy behaviours.

PubMed

Feng, Xiaoqi; Wilson, Andrew

2017-08-01

Reported differences in the severity of the social gradient in body mass index (BMI) by gender may be attributable to differences in behaviour. Self-reported height, weight, socioeconomic and behavioural data were obtained for a sample of 10,281 Australians aged ≥15years in 2009. Multilevel regressions were fitted with BMI as the outcome variable. Two-way interactions between gender and neighbourhood disadvantage were fitted, adjusted for confounders. Models were then adjusted for four behavioural factors ("chips, snacks and confectionary", "smoking, little fruit or veg", "time poor and less physically active" and "alcohol consumption"). Additional models were fitted on a subset with accurate perceptions of weight status (determined by World Health Organization criteria) to control for potential social desirability bias. Although higher BMI was observed for men in most disadvantaged compared with most affluent neighbourhoods (coefficient 0.87, 95% CI 0.35 to 1.40), this pattern was stronger among women (1.80, 95% CI 1.17 to 2.42). Adjusting for differences in behaviours attenuated, but did not fully explain the differences in social gradients observed for men (0.73, 95% CI 0.21 to 1.26) and women (1.73, 1.10 to 2.36). Differences in behaviour did not explain contrasting socioeconomic gradients in adult BMI by gender. Further research on differences in BMI, health and behaviour over time aligned with how heavy a person may perceive themselves to be is warranted. Copyright © 2017. Published by Elsevier Inc.

Political democracy, economic liberalization, and macro-sociological models of intergenerational mobility.

PubMed

Gugushvili, Alexi

2017-08-01

Building on the previously investigated macro-sociological models which analyze the consequences of economic development, income inequality, and international migration on social mobility, this article studies the specific contextual covariates of intergenerational reproduction of occupational status in post-communist societies. It is theorized that social mobility is higher in societies with democratic political regimes and less liberalized economies. The outlined hypotheses are tested by using micro- and macro-level datasets for 21 post-communist societies which are fitted into multilevel mixed-effects linear regressions. The derived findings suggest that factors specific to transition societies, conventional macro-level variables, and the legacy of the Soviet Union explain variation in intergenerational social mobility, but the results vary depending which birth cohorts survey participants belong to and whether or not they stem from advantaged or disadvantaged social origins. These findings are robust to various alternative data, sample, and method specifications. Copyright © 2017 Elsevier Inc. All rights reserved.

Community characteristics and implementation factors associated with effective systems of care.

PubMed

Lunn, Laurel M; Heflinger, Craig Anne; Wang, Wei; Greenbaum, Paul E; Kutash, Krista; Boothroyd, Roger A; Friedman, Robert M

2011-07-01

How are characteristics of communities associated with the implementation of the principles of systems of care (SOC)? This study uses multilevel modeling with a stratified random sample (N = 225) of US counties to explore community-level predictors of the implementation factors of the System of Care Implementation Survey. A model composed of community-level social indicators fits well with 5 of 14 factors identified as relevant for effective SOCs. As hypothesized, community disadvantage was negatively and residential stability positively associated with the implementation of SOC principles. Designation as a mental health professional shortage area was positively related to some implementation scores, as was the percentage of minority residents, while rurality was not significantly associated with any of the factors. Given the limitations of the study, the results should be interpreted with caution, but suggest that further research is merited to clarify these relationships that could inform efforts directed at promoting SOCs.

A multilevel model of the impact of farm-level best management practices on phosphorus runoff

USDA-ARS?s Scientific Manuscript database

Multilevel or hierarchical models have been applied for a number of years in the social sciences but only relatively recently in the environmental sciences. These models can be developed in either a frequentist or Bayesian context and have similarities to other methods such as empirical Bayes analys...

Cross-Classified Random Effects Models in Institutional Research

ERIC Educational Resources Information Center

Meyers, Laura E.

2012-01-01

Multilevel modeling offers researchers a rich array of tools that can be used for a variety of purposes, such as analyzing specific institutional issues, looking for macro-level trends, and helping to shape and inform educational policy. One of the more complex multilevel modeling tools available to institutional researchers is cross-classified…

Outward Bound Outcome Model Validation and Multilevel Modeling

ERIC Educational Resources Information Center

Luo, Yuan-Chun

2011-01-01

This study was intended to measure construct validity for the Outward Bound Outcomes Instrument (OBOI) and to predict outcome achievement from individual characteristics and course attributes using multilevel modeling. A sample of 2,340 participants was collected by Outward Bound USA between May and September 2009 using the OBOI. Two phases of…

Introduction to Multilevel Item Response Theory Analysis: Descriptive and Explanatory Models

ERIC Educational Resources Information Center

Sulis, Isabella; Toland, Michael D.

2017-01-01

Item response theory (IRT) models are the main psychometric approach for the development, evaluation, and refinement of multi-item instruments and scaling of latent traits, whereas multilevel models are the primary statistical method when considering the dependence between person responses when primary units (e.g., students) are nested within…

Teaching Quality Management Model for the Training of Innovation Ability and the Multilevel Decomposition Indicators

ERIC Educational Resources Information Center

Lu, Xingjiang; Yao, Chen; Zheng, Jianmin

2013-01-01

This paper focuses on the training of undergraduate students' innovation ability. On top of the theoretical framework of the Quality Function Deployment (QFD), we propose a teaching quality management model. Based on this model, we establish a multilevel decomposition indicator system, which integrates innovation ability characterized by four…

Seeing the forest and the trees: multilevel models reveal both species and community patterns

Treesearch

Michelle M. Jackson; Monica G. Turner; Scott M. Pearson; Anthony R. Ives

2012-01-01

Studies designed to understand species distributions and community assemblages typically use separate analytical approaches (e.g., logistic regression and ordination) to model the distribution of individual species and to relate community composition to environmental variation. Multilevel models (MLMs) offer a promising strategy for integrating species and community-...

Distinguishing Continuous and Discrete Approaches to Multilevel Mixture IRT Models: A Model Comparison Perspective

ERIC Educational Resources Information Center

Zhu, Xiaoshu

2013-01-01

The current study introduced a general modeling framework, multilevel mixture IRT (MMIRT) which detects and describes characteristics of population heterogeneity, while accommodating the hierarchical data structure. In addition to introducing both continuous and discrete approaches to MMIRT, the main focus of the current study was to distinguish…

A Multilevel Analysis of Phase II of the Louisiana School Effectiveness Study.

ERIC Educational Resources Information Center

Kennedy, Eugene; And Others

This paper presents findings of a study that used conventional modeling strategies (student- and school-level) and a new multilevel modeling strategy, Hierarchical Linear Modeling, to investigate school effects on student-achievement outcomes for data collected as part of Phase 2 of the Louisiana School Effectiveness Study. The purpose was to…

Multilevel Modeling and Ordinary Least Squares Regression: How Comparable Are They?

ERIC Educational Resources Information Center

Huang, Francis L.

2018-01-01

Studies analyzing clustered data sets using both multilevel models (MLMs) and ordinary least squares (OLS) regression have generally concluded that resulting point estimates, but not the standard errors, are comparable with each other. However, the accuracy of the estimates of OLS models is important to consider, as several alternative techniques…

Developing an Adequately Specified Model of State Level Student Achievement with Multilevel Data.

ERIC Educational Resources Information Center

Bernstein, Lawrence

Limitations of using linear, unilevel regression procedures in modeling student achievement are discussed. This study is a part of a broader study that is developing an empirically-based predictive model of variables associated with academic achievement from a multilevel perspective and examining the differences by which parameters are estimated…

A Bayesian Multi-Level Factor Analytic Model of Consumer Price Sensitivities across Categories

ERIC Educational Resources Information Center

Duvvuri, Sri Devi; Gruca, Thomas S.

2010-01-01

Identifying price sensitive consumers is an important problem in marketing. We develop a Bayesian multi-level factor analytic model of the covariation among household-level price sensitivities across product categories that are substitutes. Based on a multivariate probit model of category incidence, this framework also allows the researcher to…

Finite Mixture Multilevel Multidimensional Ordinal IRT Models for Large Scale Cross-Cultural Research

ERIC Educational Resources Information Center

de Jong, Martijn G.; Steenkamp, Jan-Benedict E. M.

2010-01-01

We present a class of finite mixture multilevel multidimensional ordinal IRT models for large scale cross-cultural research. Our model is proposed for confirmatory research settings. Our prior for item parameters is a mixture distribution to accommodate situations where different groups of countries have different measurement operations, while…

The Effect of Small Sample Size on Two-Level Model Estimates: A Review and Illustration

ERIC Educational Resources Information Center

McNeish, Daniel M.; Stapleton, Laura M.

2016-01-01

Multilevel models are an increasingly popular method to analyze data that originate from a clustered or hierarchical structure. To effectively utilize multilevel models, one must have an adequately large number of clusters; otherwise, some model parameters will be estimated with bias. The goals for this paper are to (1) raise awareness of the…

Physical Activity in Physical Education: Are Longer Lessons Better?

PubMed Central

Smith, Nicole J.; Monnat, Shannon M.; Lounsbery, Monica A.F.

2015-01-01

BACKGROUND The purpose of this study was to compare physical activity (PA) outcomes in a sample of high school physical education (PE) lessons from schools that adopted traditional versus modified block schedule formats. METHODS We used the System for Observing Fitness Instruction Time (SOFIT) to conduct observations of 168 high school (HS) PE lessons delivered by 22 PE teachers in 4 schools. We used t-tests and multilevel models were used to explore variability in moderate PA and vigorous PA. RESULTS PA outcomes were significantly different between modified block and traditional schools. Students who attended traditional schools engaged in more vigorous PA in PE lessons. Modified block lessons lost more scheduled lesson time due to poor transition to and from the locker room. PA outcomes were positively associated with fitness and teacher promotion of PA and negatively associated with lost time, class size, management, and knowledge. CONCLUSIONS Though PE proponents widely advocate for more PE minutes, this study showed that greater time scheduled in PE does not necessarily result in more student accrual of MVPA minutes. PMID:25611935

The influence of gender equality policies on gender inequalities in health in Europe.

PubMed

Palència, Laia; Malmusi, Davide; De Moortel, Deborah; Artazcoz, Lucía; Backhans, Mona; Vanroelen, Christophe; Borrell, Carme

2014-09-01

Few studies have addressed the effect of gender policies on women's health and gender inequalities in health. This study aims to analyse the relationship between the orientation of public gender equality policies and gender inequalities in health in European countries, and whether this relationship is mediated by gender equality at country level or by other individual social determinants of health. A multilevel cross-sectional study was performed using individual-level data extracted from the European Social Survey 2010. The study sample consisted of 23,782 men and 28,655 women from 26 European countries. The dependent variable was self-perceived health. Individual independent variables were gender, age, immigrant status, educational level, partner status and employment status. The main contextual independent variable was a modification of Korpi's typology of family policy models (Dual-earner, Traditional-Central, Traditional-Southern, Market-oriented and Contradictory). Other contextual variables were the Gender Empowerment Measure (GEM), to measure country-level gender equality, and the Gross Domestic Product (GDP). For each country and country typology the prevalence of fair/poor health by gender was calculated and prevalence ratios (PR, women compared to men) and 95% confidence intervals (CI) were computed. Multilevel robust Poisson regression models were fitted. Women had poorer self-perceived health than men in countries with traditional family policies (PR = 1.13, 95%CI: 1.07-1.21 in Traditional-Central and PR = 1.27, 95%CI: 1.19-1.35 in Traditional-Southern) and in Contradictory countries (PR = 1.08, 95%CI: 1.05-1.11). In multilevel models, only gender inequalities in Traditional-Southern countries were significantly higher than those in Dual-earner countries. Gender inequalities in self-perceived health were higher, women reporting worse self-perceived health than men, in countries with family policies that were less oriented to gender equality (especially in the Traditional-Southern country-group). This was partially explained by gender inequalities in the individual social determinants of health but not by GEM or GDP. Copyright © 2014 Elsevier Ltd. All rights reserved.

Determinants of Academic Entrepreneurship Behavior: A Multilevel Model

ERIC Educational Resources Information Center

Llano, Joseph Anthony

2010-01-01

It is well established that universities encourage the acquisition and dissemination of new knowledge among university community members and beyond. However, what is less well understood is how universities encourage entrepreneurial (opportunity discovery, evaluation, and exploiting) behavior. This research investigated a multilevel model of the…

Attachment, Autonomy, and Emotional Reliance: A Multilevel Model

ERIC Educational Resources Information Center

Lynch, Martin F.

2013-01-01

This article reports a test of a multilevel model investigating how attachment security and autonomy contribute to emotional reliance, or the willingness to seek interpersonal support. Participants ("N" = 247) completed online measures of attachment, autonomy, emotional reliance, and vitality with respect to several everyday…

Longitudinal associations between exercise identity and exercise motivation: A multilevel growth curve model approach.

PubMed

Ntoumanis, N; Stenling, A; Thøgersen-Ntoumani, C; Vlachopoulos, S; Lindwall, M; Gucciardi, D F; Tsakonitis, C

2018-02-01

Past work linking exercise identity and exercise motivation has been cross-sectional. This is the first study to model the relations between different types of exercise identity and exercise motivation longitudinally. Understanding the dynamic associations between these sets of variables has implications for theory development and applied research. This was a longitudinal survey study. Participants were 180 exercisers (79 men, 101 women) from Greece, who were recruited from fitness centers and were asked to complete questionnaires assessing exercise identity (exercise beliefs and role-identity) and exercise motivation (intrinsic, identified, introjected, external motivation, and amotivation) three times within a 6 month period. Multilevel growth curve modeling examined the role of motivational regulations as within- and between-level predictors of exercise identity, and a model in which exercise identity predicted exercise motivation at the within- and between-person levels. Results showed that within-person changes in intrinsic motivation, introjected, and identified regulations were positively and reciprocally related to within-person changes in exercise beliefs; intrinsic motivation was also a positive predictor of within-person changes in role-identity but not vice versa. Between-person differences in the means of predictor variables were predictive of initial levels and average rates of change in the outcome variables. The findings show support to the proposition that a strong exercise identity (particularly exercise beliefs) can foster motivation for behaviors that reinforce this identity. We also demonstrate that such relations can be reciprocal overtime and can depend on the type of motivation in question as well as between-person differences in absolute levels of these variables. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Evaluating Technical Efficiency of Nursing Care Using Data Envelopment Analysis and Multilevel Modeling.

PubMed

Min, Ari; Park, Chang Gi; Scott, Linda D

2016-05-23

Data envelopment analysis (DEA) is an advantageous non-parametric technique for evaluating relative efficiency of performance. This article describes use of DEA to estimate technical efficiency of nursing care and demonstrates the benefits of using multilevel modeling to identify characteristics of efficient facilities in the second stage of analysis. Data were drawn from LTCFocUS.org, a secondary database including nursing home data from the Online Survey Certification and Reporting System and Minimum Data Set. In this example, 2,267 non-hospital-based nursing homes were evaluated. Use of DEA with nurse staffing levels as inputs and quality of care as outputs allowed estimation of the relative technical efficiency of nursing care in these facilities. In the second stage, multilevel modeling was applied to identify organizational factors contributing to technical efficiency. Use of multilevel modeling avoided biased estimation of findings for nested data and provided comprehensive information on differences in technical efficiency among counties and states. © The Author(s) 2016.

Multilevel modelling of somatotype components: the Portuguese sibling study on growth, fitness, lifestyle and health.

PubMed

Pereira, Sara; Katzmarzyk, Peter T; Gomes, Thayse Natacha; Souza, Michele; Chaves, Raquel N; Santos, Fernanda K Dos; Santos, Daniel; Hedeker, Donald; Maia, José A R

2017-06-01

Somatotype is a complex trait influenced by different genetic and environmental factors as well as by other covariates whose effects are still unclear. To (1) estimate siblings' resemblance in their general somatotype; (2) identify sib-pair (brother-brother (BB), sister-sister (SS), brother-sister (BS)) similarities in individual somatotype components; (3) examine the degree to which between and within variances differ among sib-ships; and (4) investigate the effects of physical activity (PA) and family socioeconomic status (SES) on these relationships. The sample comprises 1058 Portuguese siblings (538 females) aged 9-20 years. Somatotype was calculated using the Health-Carter method, while PA and SES information was obtained by questionnaire. Multi-level modelling was done in SuperMix software. Older subjects showed the lowest values for endomorphy and mesomorphy, but the highest values for ectomorphy; and more physically active subjects showed the highest values for mesomorphy. In general, the familiality of somatotype was moderate (ρ = 0.35). Same-sex siblings had the strongest resemblance (endomorphy: ρ SS > ρ BB > ρ BS ; mesomorphy: ρ BB = ρ SS > ρ BS ; ectomorphy: ρ BB > ρ SS > ρ BS ). For the ectomorphy and mesomorphy components, BS pairs showed the highest between sib-ship variance, but the lowest within sib-ship variance; while for endomorphy BS showed the lowest between and within sib-ship variances. These results highlight the significant familial effects on somatotype and the complexity of the role of familial resemblance in explaining variance in somatotypes.

Proinflammatory Dietary Intake is Associated with Increased Risk of Colorectal Cancer: Results of a Case-Control Study in Argentina Using a Multilevel Modeling Approach.

PubMed

Niclis, Camila; Pou, Sonia A; Shivappa, Nitin; Hébert, James R; Steck, Susan E; Díaz, María Del Pilar

2018-01-01

Little evidence regarding the inflammatory potential of diet and its effect on colorectal cancer exists in Latin American countries. The aim of the present study was to evaluate the association between the Dietary Inflammatory Index (DII®) and colorectal cancer (CRC) risk in Córdoba, Argentina. A frequency-matched case-control study (N = 446, including 144 (32.3%) CRC cases and 302 (67.7%) controls was conducted in Córdoba (Argentina) from 2008 through 2015. DII® scores were computed based on dietary intake assessed by a validated food frequency questionnaire (FFQ). Multilevel logistic regression models were fit to evaluate the association between DII scores and CRC, following adjustment for age, body mass index, sex, energy intake, smoking habits, socio-economic status, physical activity, and use of nonsteroidal anti-inflammatory drugs as first-level covariates and level of urbanization as the contextual variable. Odds of colorectal cancer increased linearly with increasing DII scores (OR continuous 1.34; 95%CI 1.07 to 1.69 and OR tertile3 vs. tertile1 1.21; 95%CI 1.01 to 1.44). The association was stronger among men than women (OR continuous 1.29; 95%CI 1.21 to 1.37 vs. OR continuous 1.05; 95%CI 0.83 to 1.33, respectively). A proinflammatory diet, reflected by higher DII scores, was positively associated with colorectal cancer occurrence, mainly in men.

Translating the Socio-Ecological Perspective into Multilevel Interventions: Gaps between Theory and Practice

ERIC Educational Resources Information Center

Schölmerich, Vera L. N.; Kawachi, Ichiro

2016-01-01

Multilevel interventions are inspired by socio-ecological models, and seek to create change on various levels--for example by increasing the health literacy of individuals as well as modifying the social norms within a community. Despite becoming a buzzword in public health, actual multilevel interventions remain scarce. In this commentary, we…

Beneficial laggards: multilevel selection, cooperative polymorphism and division of labour in threshold public good games

PubMed Central

2010-01-01

Background The origin and stability of cooperation is a hot topic in social and behavioural sciences. A complicated conundrum exists as defectors have an advantage over cooperators, whenever cooperation is costly so consequently, not cooperating pays off. In addition, the discovery that humans and some animal populations, such as lions, are polymorphic, where cooperators and defectors stably live together -- while defectors are not being punished--, is even more puzzling. Here we offer a novel explanation based on a Threshold Public Good Game (PGG) that includes the interaction of individual and group level selection, where individuals can contribute to multiple collective actions, in our model group hunting and group defense. Results Our results show that there are polymorphic equilibria in Threshold PGGs; that multi-level selection does not select for the most cooperators per group but selects those close to the optimum number of cooperators (in terms of the Threshold PGG). In particular for medium cost values division of labour evolves within the group with regard to the two types of cooperative actions (hunting vs. defense). Moreover we show evidence that spatial population structure promotes cooperation in multiple PGGs. We also demonstrate that these results apply for a wide range of non-linear benefit function types. Conclusions We demonstrate that cooperation can be stable in Threshold PGG, even when the proportion of so called free riders is high in the population. A fundamentally new mechanism is proposed how laggards, individuals that have a high tendency to defect during one specific group action can actually contribute to the fitness of the group, by playing part in an optimal resource allocation in Threshold Public Good Games. In general, our results show that acknowledging a multilevel selection process will open up novel explanations for collective actions. PMID:21044340

No effect of unemployment on intimate partner-related femicide during the financial crisis: a longitudinal ecological study in Spain.

PubMed

Torrubiano-Domínguez, J; Vives-Cases, C; San-Sebastián, M; Sanz-Barbero, B; Goicolea, I; Álvarez-Dardet, C

2015-09-30

Spain's financial crisis has been characterized by an increase in unemployment. This increase could have produced an increase in deaths of women due to intimate partner-related femicides (IPF). This study aims to determine whether the increase in unemployment among both sexes in different regions in Spain is related to an increase in the rates of IPF during the current financial crisis period. An ecological longitudinal study was carried out in Spain's 17 regions. Two study periods were defined: pre-crisis period (2005-2007) and crisis period (2008-2013). IPF rates adjusted by age and unemployment rates for men and women were calculated. We fitted multilevel linear regression models in which observations at level 1 were nested within regions according to a repeated measurements design. Rates of unemployment have progressively increased in Spain, rising above 20 % from 2008 to 2013 in some regions. IPF rates decreased in some regions during crisis period with respect to pre-crisis period. The multilevel analysis does not support the existence of a significant relationship between the increase in unemployment in men and women and the decrease in IPF since 2008. The increase in unemployment in men and women in Spain does not appear to have an effect on IPF. The results of the multilevel analysis discard the hypothesis that the increase in the rates of unemployment in women and men are related to an increase in IPF rates. The decline in IPF since 2008 might be interpreted as the result of exposure to other factors such as the lower frequency of divorces in recent years or the medium term effects of the integral protection measures of the law on gender violence that began in 2005.

Using SEM to Analyze Complex Survey Data: A Comparison between Design-Based Single-Level and Model-Based Multilevel Approaches

ERIC Educational Resources Information Center

Wu, Jiun-Yu; Kwok, Oi-man

2012-01-01

Both ad-hoc robust sandwich standard error estimators (design-based approach) and multilevel analysis (model-based approach) are commonly used for analyzing complex survey data with nonindependent observations. Although these 2 approaches perform equally well on analyzing complex survey data with equal between- and within-level model structures…

Using Design-Based Latent Growth Curve Modeling with Cluster-Level Predictor to Address Dependency

ERIC Educational Resources Information Center

Wu, Jiun-Yu; Kwok, Oi-Man; Willson, Victor L.

2014-01-01

The authors compared the effects of using the true Multilevel Latent Growth Curve Model (MLGCM) with single-level regular and design-based Latent Growth Curve Models (LGCM) with or without the higher-level predictor on various criterion variables for multilevel longitudinal data. They found that random effect estimates were biased when the…

Illustration of a Multilevel Model for Meta-Analysis

ERIC Educational Resources Information Center

de la Torre, Jimmy; Camilli, Gregory; Vargas, Sadako; Vernon, R. Fox

2007-01-01

In this article, the authors present a multilevel (or hierarchical linear) model that illustrates issues in the application of the model to data from meta-analytic studies. In doing so, several issues are discussed that typically arise in the course of a meta-analysis. These include the presence of non-zero between-study variability, how multiple…

The Development of a Multi-Level Model for Crisis Preparedness and Intervention in the Greek Educational System

ERIC Educational Resources Information Center

Hatzichristiou, Chryse; Issari, Philia; Lykitsakou, Konstantina; Lampropoulou, Aikaterini; Dimitropoulou, Panayiota

2011-01-01

This article proposes a multi-level model for crisis preparedness and intervention in the Greek educational system. It presents: a) a brief overview of leading models of school crisis preparedness and intervention as well as cultural considerations for contextually relevant crisis response; b) a description of existing crisis intervention…

Explaining Technology Integration in K-12 Classrooms: A Multilevel Path Analysis Model

ERIC Educational Resources Information Center

Liu, Feng; Ritzhaupt, Albert D.; Dawson, Kara; Barron, Ann E.

2017-01-01

The purpose of this research was to design and test a model of classroom technology integration in the context of K-12 schools. The proposed multilevel path analysis model includes teacher, contextual, and school related variables on a teacher's use of technology and confidence and comfort using technology as mediators of classroom technology…

Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis

ERIC Educational Resources Information Center

Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.

2006-01-01

Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…

Standardized Mean Differences in Two-Level Cross-Classified Random Effects Models

ERIC Educational Resources Information Center

Lai, Mark H. C.; Kwok, Oi-Man

2014-01-01

Multilevel modeling techniques are becoming more popular in handling data with multilevel structure in educational and behavioral research. Recently, researchers have paid more attention to cross-classified data structure that naturally arises in educational settings. However, unlike traditional single-level research, methodological studies about…

Mind the Gap! A Multilevel Analysis of Factors Related to Variation in Published Cost-Effectiveness Estimates within and between Countries

PubMed Central

Boehler, Christian E. H.; Lord, Joanne

2016-01-01

Background. Published cost-effectiveness estimates can vary considerably, both within and between countries. Despite extensive discussion, little is known empirically about factors relating to these variations. Objectives. To use multilevel statistical modeling to integrate cost-effectiveness estimates from published economic evaluations to investigate potential causes of variation. Methods. Cost-effectiveness studies of statins for cardiovascular disease prevention were identified by systematic review. Estimates of incremental costs and effects were extracted from reported base case, sensitivity, and subgroup analyses, with estimates grouped in studies and in countries. Three bivariate models were developed: a cross-classified model to accommodate data from multinational studies, a hierarchical model with multinational data allocated to a single category at country level, and a hierarchical model excluding multinational data. Covariates at different levels were drawn from a long list of factors suggested in the literature. Results. We found 67 studies reporting 2094 cost-effectiveness estimates relating to 23 countries (6 studies reporting for more than 1 country). Data and study-level covariates included patient characteristics, intervention and comparator cost, and some study methods (e.g., discount rates and time horizon). After adjusting for these factors, the proportion of variation attributable to countries was negligible in the cross-classified model but moderate in the hierarchical models (14%−19% of total variance). Country-level variables that improved the fit of the hierarchical models included measures of income and health care finance, health care resources, and population risks. Conclusions. Our analysis suggested that variability in published cost-effectiveness estimates is related more to differences in study methods than to differences in national context. Multinational studies were associated with much lower country-level variation than single-country studies. These findings are for a single clinical question and may be atypical. PMID:25878194

Using multilevel spatial models to understand salamander site occupancy patterns after wildfire

USGS Publications Warehouse

Chelgren, Nathan; Adams, Michael J.; Bailey, Larissa L.; Bury, R. Bruce

2011-01-01

Studies of the distribution of elusive forest wildlife have suffered from the confounding of true presence with the uncertainty of detection. Occupancy modeling, which incorporates probabilities of species detection conditional on presence, is an emerging approach for reducing observation bias. However, the current likelihood modeling framework is restrictive for handling unexplained sources of variation in the response that may occur when there are dependence structures such as smaller sampling units that are nested within larger sampling units. We used multilevel Bayesian occupancy modeling to handle dependence structures and to partition sources of variation in occupancy of sites by terrestrial salamanders (family Plethodontidae) within and surrounding an earlier wildfire in western Oregon, USA. Comparison of model fit favored a spatial N-mixture model that accounted for variation in salamander abundance over models that were based on binary detection/non-detection data. Though catch per unit effort was higher in burned areas than unburned, there was strong support that this pattern was due to a higher probability of capture for individuals in burned plots. Within the burn, the odds of capturing an individual given it was present were 2.06 times the odds outside the burn, reflecting reduced complexity of ground cover in the burn. There was weak support that true occupancy was lower within the burned area. While the odds of occupancy in the burn were 0.49 times the odds outside the burn among the five species, the magnitude of variation attributed to the burn was small in comparison to variation attributed to other landscape variables and to unexplained, spatially autocorrelated random variation. While ordinary occupancy models may separate the biological pattern of interest from variation in detection probability when all sources of variation are known, the addition of random effects structures for unexplained sources of variation in occupancy and detection probability may often more appropriately represent levels of uncertainty. ?? 2011 by the Ecological Society of America.

Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes.

PubMed

Nyman, Elin; Rozendaal, Yvonne J W; Helmlinger, Gabriel; Hamrén, Bengt; Kjellsson, Maria C; Strålfors, Peter; van Riel, Natal A W; Gennemark, Peter; Cedersund, Gunnar

2016-04-06

We are currently in the middle of a major shift in biomedical research: unprecedented and rapidly growing amounts of data may be obtained today, from in vitro, in vivo and clinical studies, at molecular, physiological and clinical levels. To make use of these large-scale, multi-level datasets, corresponding multi-level mathematical models are needed, i.e. models that simultaneously capture multiple layers of the biological, physiological and disease-level organization (also referred to as quantitative systems pharmacology-QSP-models). However, today's multi-level models are not yet embedded in end-usage applications, neither in drug research and development nor in the clinic. Given the expectations and claims made historically, this seemingly slow adoption may seem surprising. Therefore, we herein consider a specific example-type 2 diabetes-and critically review the current status and identify key remaining steps for these models to become mainstream in the future. This overview reveals how, today, we may use models to ask scientific questions concerning, e.g., the cellular origin of insulin resistance, and how this translates to the whole-body level and short-term meal responses. However, before these multi-level models can become truly useful, they need to be linked with the capabilities of other important existing models, in order to make them 'personalized' (e.g. specific to certain patient phenotypes) and capable of describing long-term disease progression. To be useful in drug development, it is also critical that the developed models and their underlying data and assumptions are easily accessible. For clinical end-usage, in addition, model links to decision-support systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.

The relationship between multilevel models and non-parametric multilevel mixture models: Discrete approximation of intraclass correlation, random coefficient distributions, and residual heteroscedasticity.

PubMed

Rights, Jason D; Sterba, Sonya K

2016-11-01

Multilevel data structures are common in the social sciences. Often, such nested data are analysed with multilevel models (MLMs) in which heterogeneity between clusters is modelled by continuously distributed random intercepts and/or slopes. Alternatively, the non-parametric multilevel regression mixture model (NPMM) can accommodate the same nested data structures through discrete latent class variation. The purpose of this article is to delineate analytic relationships between NPMM and MLM parameters that are useful for understanding the indirect interpretation of the NPMM as a non-parametric approximation of the MLM, with relaxed distributional assumptions. We define how seven standard and non-standard MLM specifications can be indirectly approximated by particular NPMM specifications. We provide formulas showing how the NPMM can serve as an approximation of the MLM in terms of intraclass correlation, random coefficient means and (co)variances, heteroscedasticity of residuals at level 1, and heteroscedasticity of residuals at level 2. Further, we discuss how these relationships can be useful in practice. The specific relationships are illustrated with simulated graphical demonstrations, and direct and indirect interpretations of NPMM classes are contrasted. We provide an R function to aid in implementing and visualizing an indirect interpretation of NPMM classes. An empirical example is presented and future directions are discussed. © 2016 The British Psychological Society.

Get Over It! A Multilevel Threshold Autoregressive Model for State-Dependent Affect Regulation.

PubMed

De Haan-Rietdijk, Silvia; Gottman, John M; Bergeman, Cindy S; Hamaker, Ellen L

2016-03-01

Intensive longitudinal data provide rich information, which is best captured when specialized models are used in the analysis. One of these models is the multilevel autoregressive model, which psychologists have applied successfully to study affect regulation as well as alcohol use. A limitation of this model is that the autoregressive parameter is treated as a fixed, trait-like property of a person. We argue that the autoregressive parameter may be state-dependent, for example, if the strength of affect regulation depends on the intensity of affect experienced. To allow such intra-individual variation, we propose a multilevel threshold autoregressive model. Using simulations, we show that this model can be used to detect state-dependent regulation with adequate power and Type I error. The potential of the new modeling approach is illustrated with two empirical applications that extend the basic model to address additional substantive research questions.

The Effects of Including Observed Means or Latent Means as Covariates in Multilevel Models for Cluster Randomized Trials

ERIC Educational Resources Information Center

Aydin, Burak; Leite, Walter L.; Algina, James

2016-01-01

We investigated methods of including covariates in two-level models for cluster randomized trials to increase power to detect the treatment effect. We compared multilevel models that included either an observed cluster mean or a latent cluster mean as a covariate, as well as the effect of including Level 1 deviation scores in the model. A Monte…

Multi-Level Reduced Order Modeling Equipped with Probabilistic Error Bounds

NASA Astrophysics Data System (ADS)

Abdo, Mohammad Gamal Mohammad Mostafa

This thesis develops robust reduced order modeling (ROM) techniques to achieve the needed efficiency to render feasible the use of high fidelity tools for routine engineering analyses. Markedly different from the state-of-the-art ROM techniques, our work focuses only on techniques which can quantify the credibility of the reduction which can be measured with the reduction errors upper-bounded for the envisaged range of ROM model application. Our objective is two-fold. First, further developments of ROM techniques are proposed when conventional ROM techniques are too taxing to be computationally practical. This is achieved via a multi-level ROM methodology designed to take advantage of the multi-scale modeling strategy typically employed for computationally taxing models such as those associated with the modeling of nuclear reactor behavior. Second, the discrepancies between the original model and ROM model predictions over the full range of model application conditions are upper-bounded in a probabilistic sense with high probability. ROM techniques may be classified into two broad categories: surrogate construction techniques and dimensionality reduction techniques, with the latter being the primary focus of this work. We focus on dimensionality reduction, because it offers a rigorous approach by which reduction errors can be quantified via upper-bounds that are met in a probabilistic sense. Surrogate techniques typically rely on fitting a parametric model form to the original model at a number of training points, with the residual of the fit taken as a measure of the prediction accuracy of the surrogate. This approach, however, does not generally guarantee that the surrogate model predictions at points not included in the training process will be bound by the error estimated from the fitting residual. Dimensionality reduction techniques however employ a different philosophy to render the reduction, wherein randomized snapshots of the model variables, such as the model parameters, responses, or state variables, are projected onto lower dimensional subspaces, referred to as the "active subspaces", which are selected to capture a user-defined portion of the snapshots variations. Once determined, the ROM model application involves constraining the variables to the active subspaces. In doing so, the contribution from the variables discarded components can be estimated using a fundamental theorem from random matrix theory which has its roots in Dixon's theory, developed in 1983. This theory was initially presented for linear matrix operators. The thesis extends this theorem's results to allow reduction of general smooth nonlinear operators. The result is an approach by which the adequacy of a given active subspace determined using a given set of snapshots, generated either using the full high fidelity model, or other models with lower fidelity, can be assessed, which provides insight to the analyst on the type of snapshots required to reach a reduction that can satisfy user-defined preset tolerance limits on the reduction errors. Reactor physics calculations are employed as a test bed for the proposed developments. The focus will be on reducing the effective dimensionality of the various data streams such as the cross-section data and the neutron flux. The developed methods will be applied to representative assembly level calculations, where the size of the cross-section and flux spaces are typically large, as required by downstream core calculations, in order to capture the broad range of conditions expected during reactor operation. (Abstract shortened by ProQuest.).

Predictors of Segmented School Day Physical Activity and Sedentary Time in Children from a Northwest England Low-Income Community

PubMed Central

Taylor, Sarah L.; Curry, Whitney B.; Knowles, Zoe R.; Noonan, Robert J.; McGrane, Bronagh; Fairclough, Stuart J.

2017-01-01

Background: Schools have been identified as important settings for health promotion through physical activity participation, particularly as children are insufficiently active for health. The aim of this study was to investigate the child and school-level influences on children′s physical activity levels and sedentary time during school hours in a sample of children from a low-income community; Methods: One hundred and eighty-six children (110 boys) aged 9–10 years wore accelerometers for 7 days, with 169 meeting the inclusion criteria of 16 h∙day−1 for a minimum of three week days. Multilevel prediction models were constructed to identify significant predictors of sedentary time, light, and moderate to vigorous physical activity during school hour segments. Child-level predictors (sex, weight status, maturity offset, cardiorespiratory fitness, physical activity self-efficacy, physical activity enjoyment) and school-level predictors (number on roll, playground area, provision score) were entered into the models; Results: Maturity offset, fitness, weight status, waist circumference-to-height ratio, sedentary time, moderate to vigorous physical activity, number of children on roll and playground area significantly predicted physical activity and sedentary time; Conclusions: Research should move towards considering context-specific physical activity and its correlates to better inform intervention strategies. PMID:28509887

Patterns of local segregation: Do they matter for neighborhood crime?

PubMed

Krivo, Lauren J; Byron, Reginald A; Calder, Catherine A; Peterson, Ruth D; Browning, Christopher R; Kwan, Mei-Po; Lee, Jae Yong

2015-11-01

In this paper, we extend recent research on the spatial measurement of segregation and the spatial dynamics of urban crime by conceptualizing, measuring, and describing local segregation by race-ethnicity and economic status, and examining the linkages of these conditions with levels of neighborhood violent and property crime. The analyses are based on all 8895 census tracts within a sample of 86 large U.S. cities. We fit multilevel models of crime that incorporate measures of local segregation. The results reveal that, net of city-level and neighborhood characteristics, White-Black local segregation is associated with lower violent and property crime. In contrast, local segregation of low income from high income households is connected with higher crime, particularly neighborhood violence. Copyright © 2015 Elsevier Inc. All rights reserved.

Population density, socioeconomic environment and all-cause mortality: a multilevel survival analysis of 2.7 million individuals in Denmark.

PubMed

Meijer, Mathias; Kejs, Anne Mette; Stock, Christiane; Bloomfield, Kim; Ejstrud, Bo; Schlattmann, Peter

2012-03-01

This study examines the relative effects of population density and area-level SES on all-cause mortality in Denmark. A shared frailty model was fitted with 2.7 million persons aged 30-81 years in 2,121 parishes. Residence in areas with high population density increased all-cause mortality for all age groups. For older age groups, residence in areas with higher proportions of unemployed persons had an additional effect. Area-level factors explained considerably more variation in mortality among the elderly than among younger generations. Overall this study suggests that structural prevention efforts in neighborhoods could help reduce mortality when mediating processes between area-level socioeconomic status, population density and mortality are found. Copyright © 2011 Elsevier Ltd. All rights reserved.

Transportation and socioeconomic impacts of bypasses on communities : an integrated synthesis of panel data, multilevel, and spatial econometric models with case studies.

DOT National Transportation Integrated Search

2011-09-21

Title: Transportation and Socioeconomic Impacts of Bypasses on Communities: An Integrated Synthesis of Panel Data, Multilevel, and Spatial Econometric Models with Case Studies. The title used at the start of this project was Transportation and Soc...

Multilevel Evaluation Systems Project. Final Report.

ERIC Educational Resources Information Center

Herman, Joan L.

Several studies were conducted in 1987 by the Multilevel Evaluation Systems Project, which focuses on developing a model for a multi-purpose, multi-user evaluation system to facilitate educational decision making and evaluation. The project model emphasizes on-going integrated assessment of individuals, classes, and programs using a variety of…

Income Inequality and Risk of Suicide in New York City Neighborhoods: A Multilevel Case-Control Study

ERIC Educational Resources Information Center

Miller, Jeffrey R.; Piper, Tinka Markham; Ahern, Jennifer; Tracy, Melissa; Tardiff, Kenneth J.; Vlahov, David; Galea, Sandro

2005-01-01

Evidence on the relationship between income inequality and suicide is inconsistent. Data from the New York City Office of the Chief Medical Examiner for all fatal injuries was collected to conduct a multilevel case-control study. In multilevel models, suicide decedents (n = 374) were more likely than accident controls (n = 453) to reside in…

National Profiles of Classroom Quality and Family Involvement: A Multilevel Examination of Proximal Influences on Head Start Children's School Readiness

ERIC Educational Resources Information Center

Bulotsky-Shearer, Rebecca J.; Wen, Xiaoli; Faria, Ann-Marie; Hahs-Vaughn, Debbie L.; Korfmacher, Jon

2012-01-01

Guided by a developmental and ecological model, the study employed latent profile analysis to identify a multilevel typology of family involvement and Head Start classroom quality. Using the nationally representative Head Start Family and Child Experiences Survey (FACES 1997; N = 1870), six multilevel latent profiles were estimated, characterized…

Multilevel regression analyses to investigate the relationship between two variables over time: examining the longitudinal association between intrusion and avoidance.

PubMed

Suvak, Michael K; Walling, Sherry M; Iverson, Katherine M; Taft, Casey T; Resick, Patricia A

2009-12-01

Multilevel modeling is a powerful and flexible framework for analyzing nested data structures (e.g., repeated measures or longitudinal designs). The authors illustrate a series of multilevel regression procedures that can be used to elucidate the nature of the relationship between two variables across time. The goal is to help trauma researchers become more aware of the utility of multilevel modeling as a tool for increasing the field's understanding of posttraumatic adaptation. These procedures are demonstrated by examining the relationship between two posttraumatic symptoms, intrusion and avoidance, across five assessment points in a sample of rape and robbery survivors (n = 286). Results revealed that changes in intrusion were highly correlated with changes in avoidance over the 18-month posttrauma period.

A multilevel control system for the large space telescope. [numerical analysis/optimal control

NASA Technical Reports Server (NTRS)

Siljak, D. D.; Sundareshan, S. K.; Vukcevic, M. B.

1975-01-01

A multilevel scheme was proposed for control of Large Space Telescope (LST) modeled by a three-axis-six-order nonlinear equation. Local controllers were used on the subsystem level to stabilize motions corresponding to the three axes. Global controllers were applied to reduce (and sometimes nullify) the interactions among the subsystems. A multilevel optimization method was developed whereby local quadratic optimizations were performed on the subsystem level, and global control was again used to reduce (nullify) the effect of interactions. The multilevel stabilization and optimization methods are presented as general tools for design and then used in the design of the LST Control System. The methods are entirely computerized, so that they can accommodate higher order LST models with both conceptual and numerical advantages over standard straightforward design techniques.

At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study.

PubMed

Hamaker, E L; Asparouhov, T; Brose, A; Schmiedek, F; Muthén, B

2018-04-06

With the growing popularity of intensive longitudinal research, the modeling techniques and software options for such data are also expanding rapidly. Here we use dynamic multilevel modeling, as it is incorporated in the new dynamic structural equation modeling (DSEM) toolbox in Mplus, to analyze the affective data from the COGITO study. These data consist of two samples of over 100 individuals each who were measured for about 100 days. We use composite scores of positive and negative affect and apply a multilevel vector autoregressive model to allow for individual differences in means, autoregressions, and cross-lagged effects. Then we extend the model to include random residual variances and covariance, and finally we investigate whether prior depression affects later depression scores through the random effects of the daily diary measures. We end with discussing several urgent-but mostly unresolved-issues in the area of dynamic multilevel modeling.

Multi-level optimization of a beam-like space truss utilizing a continuum model

NASA Technical Reports Server (NTRS)

Yates, K.; Gurdal, Z.; Thangjitham, S.

1992-01-01

A continuous beam model is developed for approximate analysis of a large, slender, beam-like truss. The model is incorporated in a multi-level optimization scheme for the weight minimization of such trusses. This scheme is tested against traditional optimization procedures for savings in computational cost. Results from both optimization methods are presented for comparison.

Investigating Associations between School Climate and Bullying in Secondary Schools: Multilevel Contextual Effects Modeling

ERIC Educational Resources Information Center

Konishi, Chiaki; Miyazaki, Yasuo; Hymel, Shelley; Waterhouse, Terry

2017-01-01

This study examined how student reports of bullying were related to different dimensions of school climate, at both the school and the student levels, using a contextual effects model in a two-level multilevel modeling framework. Participants included 48,874 secondary students (grades 8 to 12; 24,244 girls) from 76 schools in Western Canada.…

Estimating Multi-Level Discrete-Time Hazard Models Using Cross-Sectional Data: Neighborhood Effects on the Onset of Adolescent Cigarette Use.

ERIC Educational Resources Information Center

Reardon, Sean F.; Brennan, Robert T.; Buka, Stephen L.

2002-01-01

Developed procedures for constructing a retrospective person-period data set from cross-sectional data and discusses modeling strategies for estimating multilevel discrete-time event history models. Applied the methods to the analysis of cigarette use by 1,979 urban adolescents. Results show the influence of the racial composition of the…

The Application of a Multiphase Triangulation Approach to Mixed Methods: The Research of an Aspiring School Principal Development Program

ERIC Educational Resources Information Center

Youngs, Howard; Piggot-Irvine, Eileen

2012-01-01

Mixed methods research has emerged as a credible alternative to unitary research approaches. The authors show how a combination of a triangulation convergence model with a triangulation multilevel model was used to research an aspiring school principal development pilot program. The multilevel model is used to show the national and regional levels…

A multi-level model of emerging technology: An empirical study of the evolution of biotechnology from 1976 to 2003

PubMed Central

van Witteloostuijn, Arjen

2018-01-01

In this paper, we develop an ecological, multi-level model that can be used to study the evolution of emerging technology. More specifically, by defining technology as a system composed of a set of interacting components, we can build upon the argument of multi-level density dependence from organizational ecology to develop a distribution-independent model of technological evolution. This allows us to distinguish between different stages of component development, which provides more insight into the emergence of stable component configurations, or dominant designs. We validate our hypotheses in the biotechnology industry by using patent data from the USPTO from 1976 to 2003. PMID:29795575

Highly-Efficient and Modular Medium-Voltage Converters

DTIC Science & Technology

2015-09-28

HVDC modular multilevel converter in decoupled double synchronous reference frame for voltage oscillation reduction," IEEE Trans. Ind...Electron., vol. 29, pp. 77-88, Jan 2014. [10] M. Guan and Z. Xu, "Modeling and control of a modular multilevel converter -based HVDC system under...34 Modular multilevel converter design for VSC HVDC applications," IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 3, pp.

Mathematics Low Achievement in Greece: A Multilevel Analysis of the Programme for International Student Assessment (PISA) 2012 Data

ERIC Educational Resources Information Center

Karakolidis, Anastasios; Pitsia, Vasiliki; Emvalotis, Anastassios

2016-01-01

The main aim of the present study was to carry out an in-depth examination of mathematics underperformance in Greece. By applying a binary multilevel model to the PISA 2012 data, this study investigated the factors which were linked to low achievement in mathematics. The multilevel analysis revealed that students' gender, immigration status,…

Multilevel Factor Analyses of Family Data from the Hawai'i Family Study of Cognition

ERIC Educational Resources Information Center

McArdle, John J.; Hamagami, Fumiaki; Bautista, Randy; Onoye, Jane; Hishinuma, Earl S.; Prescott, Carol A.; Takeshita, Junji; Zonderman, Alan B.; Johnson, Ronald C.

2014-01-01

In this study, we reanalyzed the classic Hawai'i Family Study of Cognition (HFSC) data using contemporary multilevel modeling techniques. We used the HFSC baseline data ("N" = 6,579) and reexamined the factorial structure of 16 cognitive variables using confirmatory (restricted) measurement models in an explicit sequence. These models…

Using Multilevel Modeling in Counseling Research

ERIC Educational Resources Information Center

Lynch, Martin F.

2012-01-01

This conceptual and practical overview of multilevel modeling (MLM) for researchers in counseling and development provides guidelines on setting up SPSS to perform MLM and an example of how to present the findings. It also provides a discussion on how counseling and developmental researchers can use MLM to address their own research questions.…

Multilevel Modeling: Overview and Applications to Research in Counseling Psychology

ERIC Educational Resources Information Center

Kahn, Jeffrey H.

2011-01-01

Multilevel modeling (MLM) is rapidly becoming the standard method of analyzing nested data, for example, data from students within multiple schools, data on multiple clients seen by a smaller number of therapists, and even longitudinal data. Although MLM analyses are likely to increase in frequency in counseling psychology research, many readers…

Multilevel and Single-Level Models for Measured and Latent Variables When Data Are Clustered

ERIC Educational Resources Information Center

Stapleton, Laura M.; McNeish, Daniel M.; Yang, Ji Seung

2016-01-01

Multilevel models are often used to evaluate hypotheses about relations among constructs when data are nested within clusters (Raudenbush & Bryk, 2002), although alternative approaches are available when analyzing nested data (Binder & Roberts, 2003; Sterba, 2009). The overarching goal of this article is to suggest when it is appropriate…

Multilevel Cognitive Diagnosis Models for Assessing Changes in Latent Attributes

ERIC Educational Resources Information Center

Huang, Hung-Yu

2017-01-01

Cognitive diagnosis models (CDMs) have been developed to evaluate the mastery status of individuals with respect to a set of defined attributes or skills that are measured through testing. When individuals are repeatedly administered a cognitive diagnosis test, a new class of multilevel CDMs is required to assess the changes in their attributes…

Multilevel Structural Equation Models for the Analysis of Comparative Data on Educational Performance

ERIC Educational Resources Information Center

Goldstein, Harvey; Bonnet, Gerard; Rocher, Thierry

2007-01-01

The Programme for International Student Assessment comparative study of reading performance among 15-year-olds is reanalyzed using statistical procedures that allow the full complexity of the data structures to be explored. The article extends existing multilevel factor analysis and structural equation models and shows how this can extract richer…

Multilevel Factor Analysis by Model Segregation: New Applications for Robust Test Statistics

ERIC Educational Resources Information Center

Schweig, Jonathan

2014-01-01

Measures of classroom environments have become central to policy efforts that assess school and teacher quality. This has sparked a wide interest in using multilevel factor analysis to test measurement hypotheses about classroom-level variables. One approach partitions the total covariance matrix and tests models separately on the…

Covariate Selection for Multilevel Models with Missing Data

PubMed Central

Marino, Miguel; Buxton, Orfeu M.; Li, Yi

2017-01-01

Missing covariate data hampers variable selection in multilevel regression settings. Current variable selection techniques for multiply-imputed data commonly address missingness in the predictors through list-wise deletion and stepwise-selection methods which are problematic. Moreover, most variable selection methods are developed for independent linear regression models and do not accommodate multilevel mixed effects regression models with incomplete covariate data. We develop a novel methodology that is able to perform covariate selection across multiply-imputed data for multilevel random effects models when missing data is present. Specifically, we propose to stack the multiply-imputed data sets from a multiple imputation procedure and to apply a group variable selection procedure through group lasso regularization to assess the overall impact of each predictor on the outcome across the imputed data sets. Simulations confirm the advantageous performance of the proposed method compared with the competing methods. We applied the method to reanalyze the Healthy Directions-Small Business cancer prevention study, which evaluated a behavioral intervention program targeting multiple risk-related behaviors in a working-class, multi-ethnic population. PMID:28239457

Multilevel poisson regression modelling for determining factors of dengue fever cases in bandung

NASA Astrophysics Data System (ADS)

Arundina, Davila Rubianti; Tantular, Bertho; Pontoh, Resa Septiani

2017-03-01

Scralatina or Dengue Fever is a kind of fever caused by serotype virus which Flavivirus genus and be known as Dengue Virus. Dengue Fever caused by Aedes Aegipty Mosquito bites who infected by a dengue virus. The study was conducted in 151 villages in Bandung. Health Analysts believes that there are two factors that affect the dengue cases, Internal factor (individual) and external factor (environment). The data who used in this research is hierarchical data. The method is used for hierarchical data modelling is multilevel method. Which is, the level 1 is village and level 2 is sub-district. According exploration data analysis, the suitable Multilevel Method is Random Intercept Model. Penalized Quasi Likelihood (PQL) approach on multilevel Poisson is a proper analysis to determine factors that affecting dengue cases in the city of Bandung. Clean and Healthy Behavior factor from the village level have an effect on the number of cases of dengue fever in the city of Bandung. Factor from the sub-district level has no effect.

Experimental studies of a prototype model of the multilevel 6KW-power inverter at supply by 12 accumulators

NASA Astrophysics Data System (ADS)

Taissariyeva, K.; Issembergenov, N.; Dzhobalaeva, G.; Usembaeva, S.

2016-09-01

The given paper considers the multilevel 6 kW-power transistor inverter at supply by 12 accumulators for transformation of solar battery energy to the electric power. At the output of the multilevel transistor inverter, it is possible to receive voltage close to a sinusoidal form. The main objective of this inverter is transformation of solar energy to the electric power of industrial frequency. The analysis of the received output curves of voltage on harmonicity has been carried out. In this paper it is set forth the developed scheme of the multilevel transistor inverter (DC-to-ac converter) which allows receiving at the output the voltage close to sinusoidal form, as well as to regulation of the output voltage level. In the paper, the results of computer modeling and experimental studies are presented.

Current indirect fitness and future direct fitness are not incompatible.

PubMed

Brahma, Anindita; Mandal, Souvik; Gadagkar, Raghavendra

2018-02-01

In primitively eusocial insects, many individuals function as workers despite being capable of independent reproduction. Such altruistic behaviour is usually explained by the argument that workers gain indirect fitness by helping close genetic relatives. The focus on indirect fitness has left open the question of whether workers are also capable of getting direct fitness in the future in spite of working towards indirect fitness in the present. To investigate this question, we recorded behavioural profiles of all wasps on six naturally occurring nests of Ropalidia marginata , and then isolated all wasps in individual plastic boxes, giving them an opportunity to initiate nests and lay eggs. We found that 41% of the wasps successfully did so. Compared to those that failed to initiate nests, those that did were significantly younger, had significantly higher frequency of self-feeding behaviour on their parent nests but were not different in the levels of work performed in the parent nests. Thus ageing and poor feeding, rather than working for their colonies, constrain individuals for future independent reproduction. Hence, future direct fitness and present work towards gaining indirect fitness are not incompatible, making it easier for worker behaviour to be selected by kin selection or multilevel selection. © 2018 The Author(s).

Linear spline multilevel models for summarising childhood growth trajectories: A guide to their application using examples from five birth cohorts.

PubMed

Howe, Laura D; Tilling, Kate; Matijasevich, Alicia; Petherick, Emily S; Santos, Ana Cristina; Fairley, Lesley; Wright, John; Santos, Iná S; Barros, Aluísio Jd; Martin, Richard M; Kramer, Michael S; Bogdanovich, Natalia; Matush, Lidia; Barros, Henrique; Lawlor, Debbie A

2016-10-01

Childhood growth is of interest in medical research concerned with determinants and consequences of variation from healthy growth and development. Linear spline multilevel modelling is a useful approach for deriving individual summary measures of growth, which overcomes several data issues (co-linearity of repeat measures, the requirement for all individuals to be measured at the same ages and bias due to missing data). Here, we outline the application of this methodology to model individual trajectories of length/height and weight, drawing on examples from five cohorts from different generations and different geographical regions with varying levels of economic development. We describe the unique features of the data within each cohort that have implications for the application of linear spline multilevel models, for example, differences in the density and inter-individual variation in measurement occasions, and multiple sources of measurement with varying measurement error. After providing example Stata syntax and a suggested workflow for the implementation of linear spline multilevel models, we conclude with a discussion of the advantages and disadvantages of the linear spline approach compared with other growth modelling methods such as fractional polynomials, more complex spline functions and other non-linear models. © The Author(s) 2013.

Analyzing chromatographic data using multilevel modeling.

PubMed

Wiczling, Paweł

2018-06-01

It is relatively easy to collect chromatographic measurements for a large number of analytes, especially with gradient chromatographic methods coupled with mass spectrometry detection. Such data often have a hierarchical or clustered structure. For example, analytes with similar hydrophobicity and dissociation constant tend to be more alike in their retention than a randomly chosen set of analytes. Multilevel models recognize the existence of such data structures by assigning a model for each parameter, with its parameters also estimated from data. In this work, a multilevel model is proposed to describe retention time data obtained from a series of wide linear organic modifier gradients of different gradient duration and different mobile phase pH for a large set of acids and bases. The multilevel model consists of (1) the same deterministic equation describing the relationship between retention time and analyte-specific and instrument-specific parameters, (2) covariance relationships relating various physicochemical properties of the analyte to chromatographically specific parameters through quantitative structure-retention relationship based equations, and (3) stochastic components of intra-analyte and interanalyte variability. The model was implemented in Stan, which provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods. Graphical abstract Relationships between log k and MeOH content for acidic, basic, and neutral compounds with different log P. CI credible interval, PSA polar surface area.

Linear spline multilevel models for summarising childhood growth trajectories: A guide to their application using examples from five birth cohorts

PubMed Central

Tilling, Kate; Matijasevich, Alicia; Petherick, Emily S; Santos, Ana Cristina; Fairley, Lesley; Wright, John; Santos, Iná S.; Barros, Aluísio JD; Martin, Richard M; Kramer, Michael S; Bogdanovich, Natalia; Matush, Lidia; Barros, Henrique; Lawlor, Debbie A

2013-01-01

Childhood growth is of interest in medical research concerned with determinants and consequences of variation from healthy growth and development. Linear spline multilevel modelling is a useful approach for deriving individual summary measures of growth, which overcomes several data issues (co-linearity of repeat measures, the requirement for all individuals to be measured at the same ages and bias due to missing data). Here, we outline the application of this methodology to model individual trajectories of length/height and weight, drawing on examples from five cohorts from different generations and different geographical regions with varying levels of economic development. We describe the unique features of the data within each cohort that have implications for the application of linear spline multilevel models, for example, differences in the density and inter-individual variation in measurement occasions, and multiple sources of measurement with varying measurement error. After providing example Stata syntax and a suggested workflow for the implementation of linear spline multilevel models, we conclude with a discussion of the advantages and disadvantages of the linear spline approach compared with other growth modelling methods such as fractional polynomials, more complex spline functions and other non-linear models. PMID:24108269

Individual tree diameter increment model for managed even-aged stands of ponderosa pine throughout the western United States using a multilevel linear mixed effects model

Treesearch

Fabian C.C. Uzoh; William W. Oliver

2008-01-01

A diameter increment model is developed and evaluated for individual trees of ponderosa pine throughout the species range in the United States using a multilevel linear mixed model. Stochastic variability is broken down among period, locale, plot, tree and within-tree components. Covariates acting at tree and stand level, as breast height diameter, density, site index...

Friendship networks of inner-city adults: a latent class analysis and multi-level regression of supporter types and the association of supporter latent class membership with supporter and recipient drug use.

PubMed

Bohnert, Amy S B; German, Danielle; Knowlton, Amy R; Latkin, Carl A

2010-03-01

Social support is a multi-dimensional construct that is important to drug use cessation. The present study identified types of supportive friends among the social network members in a community-based sample and examined the relationship of supporter-type classes with supporter, recipient, and supporter-recipient relationship characteristics. We hypothesized that the most supportive network members and their support recipients would be less likely to be current heroin/cocaine users. Participants (n=1453) were recruited from low-income neighborhoods with a high prevalence of drug use. Participants identified their friends via a network inventory, and all nominated friends were included in a latent class analysis and grouped based on their probability of providing seven types of support. These latent classes were included as the dependent variable in a multi-level regression of supporter drug use, recipient drug use, and other characteristics. The best-fitting latent class model identified five support patterns: friends who provided Little/No Support, Low/Moderate Support, High Support, Socialization Support, and Financial Support. In bivariate models, friends in the High, Low/Moderate, and Financial Support were less likely to use heroin or cocaine and had less conflict with and were more trusted by the support recipient than friends in the Low/No Support class. Individuals with supporters in those same support classes compared to the Low/No Support class were less likely to use heroin or cocaine, or to be homeless or female. Multivariable models suggested similar trends. Those with current heroin/cocaine use were less likely to provide or receive comprehensive support from friends. Published by Elsevier Ireland Ltd.

Review of family relational stress and pediatric asthma: the value of biopsychosocial systemic models.

PubMed

Wood, Beatrice L; Miller, Bruce D; Lehman, Heather K

2015-06-01

Asthma is the most common chronic disease in children. Despite dramatic advances in pharmacological treatments, asthma remains a leading public health problem, especially in socially disadvantaged minority populations. Some experts believe that this health gap is due to the failure to address the impact of stress on the disease. Asthma is a complex disease that is influenced by multilevel factors, but the nature of these factors and their interrelations are not well understood. This paper aims to integrate social, psychological, and biological literatures on relations between family/parental stress and pediatric asthma, and to illustrate the utility of multilevel systemic models for guiding treatment and stimulating future research. We used electronic database searches and conducted an integrated analysis of selected epidemiological, longitudinal, and empirical studies. Evidence is substantial for the effects of family/parental stress on asthma mediated by both disease management and psychobiological stress pathways. However, integrative models containing specific pathways are scarce. We present two multilevel models, with supporting data, as potential prototypes for other such models. We conclude that these multilevel systems models may be of substantial heuristic value in organizing investigations of, and clinical approaches to, the complex social-biological aspects of family stress in pediatric asthma. However, additional systemic models are needed, and the models presented herein could serve as prototypes for model development. © 2015 Family Process Institute.

Squeezed light from conventionally pumped multi-level lasers

NASA Technical Reports Server (NTRS)

Ralph, T. C.; Savage, C. M.

1992-01-01

We have calculated the amplitude squeezing in the output of several conventionally pumped multi-level lasers. We present results which show that standard laser models can produce significantly squeezed outputs in certain parameter ranges.

Generation and transmission of multilevel quadrature amplitude modulation formats using only one optical modulator: MATLAB Simulink simulation models

NASA Astrophysics Data System (ADS)

Binh, Le Nguyen

2009-04-01

A geometrical and phasor representation technique is presented to illustrate the modulation of the lightwave carrier to generate quadrature amplitude modulated (QAM) signals. The modulation of the amplitude and phase of the lightwave carrier is implemented using only one dual-drive Mach-Zehnder interferometric modulator (MZIM) with the assistance of phasor techniques. Any multilevel modulation scheme can be generated, but we illustrate specifically, the multilevel amplitude and differential phase shift keying (MADPSK) signals. The driving voltage levels are estimated for driving the traveling wave electrodes of the modulator. Phasor diagrams are extensively used to demonstrate the effectiveness of modulation schemes. MATLAB Simulink models are formed to generate the multilevel modulation formats, transmission, and detection in optically amplified fiber communication systems. Transmission performance is obtained for the multilevel optical signals and proven to be equivalent or better than those of binary level with equivalent bit rate. Further, the resilience to nonlinear effects is much higher for MADPSK of 50% and 33% pulse width as compared to non-return-to-zero (NRZ) pulse shaping.

Prevalence and correlates of resistance training skill competence in adolescents.

PubMed

Smith, Jordan J; DeMarco, Matthew; Kennedy, Sarah G; Kelson, Mark; Barnett, Lisa M; Faigenbaum, Avery D; Lubans, David R

2018-06-01

The aim of this study is to examine the prevalence and correlates of adolescents' resistance training (RT) skill competence. Participants were 548 adolescents (14.1 ± 0.5 years) from 16 schools in New South Wales, Australia. RT skills were assessed using the Resistance Training Skills Battery. Demographics, BMI, muscular fitness, perceived strength, RT self-efficacy, and motivation for RT were also assessed. The proportion demonstrating "competence" and "near competence" in each of the six RT skills were calculated and sex differences explored. Associations between the combined RT skill score and potential correlates were examined using multi-level linear mixed models. Overall, the prevalence of competence was low (range = 3.3% to 27.9%). Females outperformed males on the squat, lunge and overhead press, whereas males performed better on the push-up (p < .05). Significant associations were seen for a number of correlates, which largely differed by sex. Muscular fitness was moderately and positively associated with RT skills among both males (β = 0.34, 95%CIs = 0.23 to 0.46) and females (β = 0.36, 95%CIs = 0.23 to 0.48). Our findings support a link between RT skills and muscular fitness. Other associations were statistically significant but small in magnitude, and should therefore be interpreted cautiously.

Modeling the Factors Associated with Children's Mental Health Difficulties in Primary School: A Multilevel Study

ERIC Educational Resources Information Center

Humphrey, Neil; Wigelsworth, Michael

2012-01-01

The current study explores some of the factors associated with children's mental health difficulties in primary school. Multilevel modeling with data from 628 children from 36 schools was used to determine how much variation in mental health difficulties exists between and within schools, and to identify characteristics at the school and…

Pretest-Posttest-Posttest Multilevel IRT Modeling of Competence Growth of Students in Higher Education in Germany

ERIC Educational Resources Information Center

Schmidt, Susanne; Zlatkin-Troitschanskaia, Olga; Fox, Jean-Paul

2016-01-01

Longitudinal research in higher education faces several challenges. Appropriate methods of analyzing competence growth of students are needed to deal with those challenges and thereby obtain valid results. In this article, a pretest-posttest-posttest multivariate multilevel IRT model for repeated measures is introduced which is designed to address…

A Multilevel Model of Minority Opinion Expression and Team Decision-Making Effectiveness

ERIC Educational Resources Information Center

Park, Guihyun; DeShon, Richard P.

2010-01-01

The consideration of minority opinions when making team decisions is an important factor that contributes to team effectiveness. A multilevel model of minority opinion influence in decision-making teams is developed to address the conditions that relate to adequate consideration of minority opinions. Using a sample of 57 teams working on a…

The Dubious Benefits of Multi-Level Modeling

ERIC Educational Resources Information Center

Gorard, Stephen

2007-01-01

This paper presents an argument against the wider adoption of complex forms of data analysis, using multi-level modeling (MLM) as an extended case study. MLM was devised to overcome some deficiencies in existing datasets, such as the bias caused by clustering. The paper suggests that MLM has an unclear theoretical and empirical basis, has not led…

Asymptotic Effect of Misspecification in the Random Part of the Multilevel Model

ERIC Educational Resources Information Center

Berkhof, Johannes; Kampen, Jarl Kennard

2004-01-01

The authors examine the asymptotic effect of omitting a random coefficient in the multilevel model and derive expressions for the change in (a) the variance components estimator and (b) the estimated variance of the fixed effects estimator. They apply the method of moments, which yields a closed form expression for the omission effect. In…

What Is Wrong with ANOVA and Multiple Regression? Analyzing Sentence Reading Times with Hierarchical Linear Models

ERIC Educational Resources Information Center

Richter, Tobias

2006-01-01

Most reading time studies using naturalistic texts yield data sets characterized by a multilevel structure: Sentences (sentence level) are nested within persons (person level). In contrast to analysis of variance and multiple regression techniques, hierarchical linear models take the multilevel structure of reading time data into account. They…

Three-Level Models for Indirect Effects in School- and Class-Randomized Experiments in Education

ERIC Educational Resources Information Center

Pituch, Keenan A.; Murphy, Daniel L.; Tate, Richard L.

2009-01-01

Due to the clustered nature of field data, multi-level modeling has become commonly used to analyze data arising from educational field experiments. While recent methodological literature has focused on multi-level mediation analysis, relatively little attention has been devoted to mediation analysis when three levels (e.g., student, class,…

Student-Teacher Racial Match and Its Association with Black Student Achievement: An Exploration Using Multilevel Structural Equation Modeling

ERIC Educational Resources Information Center

Yarnell, Lisa M.; Bohrnstedt, George W.

2018-01-01

This study examines student-teacher "racial match" for its association with Black student achievement. Multilevel structural equation modeling was used to analyze 2013 National Assessment for Educational Progress (NAEP) Grade 4 Reading Assessment data to examine interactions of teacher race and student race in their associations with…

Multilevel Modeling in the Presence of Outliers: A Comparison of Robust Estimation Methods

ERIC Educational Resources Information Center

Finch, Holmes

2017-01-01

Multilevel models (MLMs) have proven themselves to be very useful in social science research, as data from a variety of sources is sampled such that individuals at level-1 are nested within clusters such as schools, hospitals, counseling centers, and business entities at level-2. MLMs using restricted maximum likelihood estimation (REML) provide…

Homework Works If Homework Quality Is High: Using Multilevel Modeling to Predict the Development of Achievement in Mathematics

ERIC Educational Resources Information Center

Dettmers, Swantje; Trautwein, Ulrich; Ludtke, Oliver; Kunter, Mareike; Baumert, Jurgen

2010-01-01

The present study examined the associations of 2 indicators of homework quality (homework selection and homework challenge) with homework motivation, homework behavior, and mathematics achievement. Multilevel modeling was used to analyze longitudinal data from a representative national sample of 3,483 students in Grades 9 and 10; homework effects…

A Multilevel Model of Team Cultural Diversity and Creativity: The Role of Climate for Inclusion

ERIC Educational Resources Information Center

Li, Ci-Rong; Lin, Chen-Ju; Tien, Yun-Hsiang; Chen, Chien-Ming

2017-01-01

We developed a multi-level model to test how team cultural diversity may relate to team- and individual-level creativity, integrating team diversity research and information-exchange perspective. We proposed that the team climate for inclusion would moderate both the relationship between cultural diversity and team information sharing and between…

The use of simple reparameterizations to improve the efficiency of Markov chain Monte Carlo estimation for multilevel models with applications to discrete time survival models.

PubMed

Browne, William J; Steele, Fiona; Golalizadeh, Mousa; Green, Martin J

2009-06-01

We consider the application of Markov chain Monte Carlo (MCMC) estimation methods to random-effects models and in particular the family of discrete time survival models. Survival models can be used in many situations in the medical and social sciences and we illustrate their use through two examples that differ in terms of both substantive area and data structure. A multilevel discrete time survival analysis involves expanding the data set so that the model can be cast as a standard multilevel binary response model. For such models it has been shown that MCMC methods have advantages in terms of reducing estimate bias. However, the data expansion results in very large data sets for which MCMC estimation is often slow and can produce chains that exhibit poor mixing. Any way of improving the mixing will result in both speeding up the methods and more confidence in the estimates that are produced. The MCMC methodological literature is full of alternative algorithms designed to improve mixing of chains and we describe three reparameterization techniques that are easy to implement in available software. We consider two examples of multilevel survival analysis: incidence of mastitis in dairy cattle and contraceptive use dynamics in Indonesia. For each application we show where the reparameterization techniques can be used and assess their performance.

Fentanyl related overdose in Indianapolis: Estimating trends using multilevel Bayesian models.

PubMed

Phalen, Peter; Ray, Bradley; Watson, Dennis P; Huynh, Philip; Greene, Marion S

2018-03-20

The opioid epidemic has been largely attributed to changes in prescribing practices over the past 20 years. Although current overdose trends appear driven by the opioid fentanyl, heroin has remained the focus of overdose fatality assessments. We obtained full toxicology screens on lethal overdose cases in a major US city, allowing more accurate assessment of the time-course of fentanyl-related deaths. We used coroner data from Marion County, Indiana comprising 1583 overdose deaths recorded between January 1, 2010 and April 30, 2017. Bayesian multilevel models were fitted to predict likelihood of lethal fentanyl-related overdose using information about the victim's age, race, sex, zip code, and date of death. Three hundred and seventy-seven (23.8%) overdose deaths contained fentanyl across the seven-year period. Rates rose exponentially over time, beginning well below 15% from 2010 through 2013 before rising to approximately 50% by 2017. At the beginning of the study period, rates of fentanyl overdose were lowest among Black persons but increased more rapidly, eventually surpassing Whites. Currently, White females are at particularly low risk of fentanyl overdose whereas Black females are at high risk. Rates were highest for younger and middle-aged groups. Over time, fentanyl was more likely detected without the presence of other opioids. Fentanyl has increasingly been detected in fatal overdose deaths in Marion County. Policy and program responses must focus on education for those at highest risk of fentanyl exposure and death. These responses should also be tailored to meet the unique needs of high-risk demographics. Copyright © 2018 Elsevier Ltd. All rights reserved.

Childhood Obesity Research Demonstration (CORD): The Cross-Site Overview and Opportunities for Interventions Addressing Obesity Community-Wide

PubMed Central

Belay, Brook; Dooyema, Carrie A.; Williams, Nancy; Blanck, Heidi M.

2015-01-01

Abstract Background: This is the first of a set of articles in this issue on the Childhood Obesity Research Demonstration (CORD) project and provides an overview of the multisite approach and community-wide interventions. Innovative multisetting, multilevel approaches that integrate primary healthcare and public health interventions to improve outcomes for children with obesity need to be evaluated. The CORD project aims to improve BMI and obesity-related behaviors among underserved 2- to 12-year-old children by utilizing these approaches. Methods: The CORD consortium, structure, model terminology and key components, and common measures were solidified in year 1 of the CORD project. Demonstration sites applied the CORD model across communities in years 2 and 3. Evaluation plans for year 4 include site-specific analyses as well as cross-site impact, process, and sustainability evaluations. Results: The CORD approach resulted in commonalities and differences in participant, intervention, comparison, and outcome elements across sites. Products are to include analytic results as well as cost assessment, lessons learned, tools, and materials. Discussion: Foreseen opportunities and challenges arise from the similarities and unique aspects across sites. Communities adapted interventions to fit their local context and build on strengths, but, in turn, this flexibility makes cross-site evaluation challenging. Conclusion: The CORD project represents an evidence-based approach that integrates primary care and public health strategies and evaluates multisetting multilevel interventions, thus adding to the limited research in this field. CORD products will be disseminated to a variety of stakeholders to aid the understanding, prevention, and management of childhood obesity. PMID:25679059

Theoretical and software considerations for nonlinear dynamic analysis

NASA Technical Reports Server (NTRS)

Schmidt, R. J.; Dodds, R. H., Jr.

1983-01-01

In the finite element method for structural analysis, it is generally necessary to discretize the structural model into a very large number of elements to accurately evaluate displacements, strains, and stresses. As the complexity of the model increases, the number of degrees of freedom can easily exceed the capacity of present-day software system. Improvements of structural analysis software including more efficient use of existing hardware and improved structural modeling techniques are discussed. One modeling technique that is used successfully in static linear and nonlinear analysis is multilevel substructuring. This research extends the use of multilevel substructure modeling to include dynamic analysis and defines the requirements for a general purpose software system capable of efficient nonlinear dynamic analysis. The multilevel substructuring technique is presented, the analytical formulations and computational procedures for dynamic analysis and nonlinear mechanics are reviewed, and an approach to the design and implementation of a general purpose structural software system is presented.

Coherent population transfer in multi-level Allen-Eberly models

NASA Astrophysics Data System (ADS)

Li, Wei; Cen, Li-Xiang

2018-04-01

We investigate the solvability of multi-level extensions of the Allen-Eberly model and the population transfer yielded by the corresponding dynamical evolution. We demonstrate that, under a matching condition of the frequency, the driven two-level system and its multi-level extensions possess a stationary-state solution in a canonical representation associated with a unitary transformation. As a consequence, we show that the resulting protocol is able to realize complete population transfer in a nonadiabatic manner. Moreover, we explore the imperfect pulsing process with truncation and display that the nonadiabatic effect in the evolution can lead to suppression to the cutoff error of the protocol.

Multilevel radiative thermal memory realized by the hysteretic metal-insulator transition of vanadium dioxide

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ito, Kota, E-mail: kotaito@mosk.tytlabs.co.jp; Nishikawa, Kazutaka; Iizuka, Hideo

Thermal information processing is attracting much interest as an analog of electronic computing. We experimentally demonstrated a radiative thermal memory utilizing a phase change material. The hysteretic metal-insulator transition of vanadium dioxide (VO{sub 2}) allows us to obtain a multilevel memory. We developed a Preisach model to explain the hysteretic radiative heat transfer between a VO{sub 2} film and a fused quartz substrate. The transient response of our memory predicted by the Preisach model agrees well with the measured response. Our multilevel thermal memory paves the way for thermal information processing as well as contactless thermal management.

The cultural evolution of emergent group-level traits.

PubMed

Smaldino, Paul E

2014-06-01

Many of the most important properties of human groups - including properties that may give one group an evolutionary advantage over another - are properly defined only at the level of group organization. Yet at present, most work on the evolution of culture has focused solely on the transmission of individual-level traits. I propose a conceptual extension of the theory of cultural evolution, particularly related to the evolutionary competition between cultural groups. The key concept in this extension is the emergent group-level trait. This type of trait is characterized by the structured organization of differentiated individuals and constitutes a unit of selection that is qualitatively different from selection on groups as defined by traditional multilevel selection (MLS) theory. As a corollary, I argue that the traditional focus on cooperation as the defining feature of human societies has missed an essential feature of cooperative groups. Traditional models of cooperation assume that interacting with one cooperator is equivalent to interacting with any other. However, human groups involve differential roles, meaning that receiving aid from one individual is often preferred to receiving aid from another. In this target article, I discuss the emergence and evolution of group-level traits and the implications for the theory of cultural evolution, including ramifications for the evolution of human cooperation, technology, and cultural institutions, and for the equivalency of multilevel selection and inclusive fitness approaches.

The role of maternal factors in sibling relationship quality: a multilevel study of multiple dyads per family.

PubMed

Jenkins, Jennifer; Rasbash, Jon; Leckie, George; Gass, Krista; Dunn, Judy

2012-06-01

  Although many children grow up with more than one sibling, we do not yet know if sibling dyads within families show similarities to one another on sibling affection and hostility. In the present study the hypotheses were tested that (a) there will be significant between family variation in change in sibling affection and hostility and (b) this between family variation will be explained by maternal affective climate, operationalized as positive and negative ambient parenting, differential parenting and maternal malaise.   A general population sample of families with single and multiple sibling dyads were visited twice, 2 years apart. Up to 2 children in a family acted as informants; 253 relationships were rated in 118 families. A cross-classified, multilevel model was fit to separate between-family and within-family variance in sibling relationships while simultaneously controlling for informant and partner influences.   Thirty-seven percent of the variance in change in sibling affection and 32% of the variance in change in sibling hostility was between family variance. The measured maternal affective climate including, maternal malaise and maternal ambient and differential hostility and affection explained between family differences.   Sibling relationship quality clusters in families and is partly explained by maternal affective climate. © 2011 The Authors. Journal of Child Psychology and Psychiatry © 2011 Association for Child and Adolescent Mental Health.

Determining Mass and Persistence of a Reactive Brominated-Solvent DNAPL Source Using Mass Depletion-Mass Flux Reduction Relationships During Pumping

NASA Astrophysics Data System (ADS)

Johnston, C. D.; Davis, G. B.; Bastow, T.; Annable, M. D.; Trefry, M. G.; Furness, A.; Geste, Y.; Woodbury, R.; Rhodes, S.

2011-12-01

Measures of the source mass and depletion characteristics of recalcitrant dense non-aqueous phase liquid (DNAPL) contaminants are critical elements for assessing performance of remediation efforts. This is in addition to understanding the relationships between source mass depletion and changes to dissolved contaminant concentration and mass flux in groundwater. Here we present results of applying analytical source-depletion concepts to pumping from within the DNAPL source zone of a 10-m thick heterogeneous layered aquifer to estimate the original source mass and characterise the time trajectory of source depletion and mass flux in groundwater. The multi-component, reactive DNAPL source consisted of the brominated solvent tetrabromoethane (TBA) and its transformation products (mostly tribromoethene - TriBE). Coring and multi-level groundwater sampling indicated the DNAPL to be mainly in lower-permeability layers, suggesting the source had already undergone appreciable depletion. Four simplified source dissolution models (exponential, power function, error function and rational mass) were able to describe the concentration history of the total molar concentration of brominated organics in extracted groundwater during 285 days of pumping. Approximately 152 kg of brominated compounds were extracted. The lack of significant kinetic mass transfer limitations in pumped concentrations was notable. This was despite the heterogeneous layering in the aquifer and distribution of DNAPL. There was little to choose between the model fits to pumped concentration time series. The variance of groundwater velocities in the aquifer determined during a partitioning inter-well tracer test (PITT) were used to parameterise the models. However, the models were found to be relatively insensitive to this parameter. All models indicated an initial source mass around 250 kg which compared favourably to an estimate of 220 kg derived from the PITT. The extrapolated concentrations from the dissolution models diverged, showing disparate approaches to possible remediation objectives. However, it also showed that an appreciable proportion of the source would need to be removed to discriminate between the models. This may limit the utility of such modelling early in the history of a DNAPL source. A further limitation is the simplified approach of analysing the combined parent/daughter compounds with different solubilities as a total molar concentration. Although the fitted results gave confidence to this approach, there were appreciable changes in relative abundance. The dissolution and partitioning processes are discussed in relation to the lower-solubility TBA becoming dominant in pumped groundwater over time, despite its known rapid transformation to TriBE. These processes are also related to the architecture of the depleting source as revealed by multi-level groundwater sampling under reversed pumping/injection conditions.

The Performance of Multilevel Growth Curve Models under an Autoregressive Moving Average Process

ERIC Educational Resources Information Center

Murphy, Daniel L.; Pituch, Keenan A.

2009-01-01

The authors examined the robustness of multilevel linear growth curve modeling to misspecification of an autoregressive moving average process. As previous research has shown (J. Ferron, R. Dailey, & Q. Yi, 2002; O. Kwok, S. G. West, & S. B. Green, 2007; S. Sivo, X. Fan, & L. Witta, 2005), estimates of the fixed effects were unbiased, and Type I…

Estimation of Contextual Effects through Nonlinear Multilevel Latent Variable Modeling with a Metropolis-Hastings Robbins-Monro Algorithm

ERIC Educational Resources Information Center

Yang, Ji Seung; Cai, Li

2014-01-01

The main purpose of this study is to improve estimation efficiency in obtaining maximum marginal likelihood estimates of contextual effects in the framework of nonlinear multilevel latent variable model by adopting the Metropolis-Hastings Robbins-Monro algorithm (MH-RM). Results indicate that the MH-RM algorithm can produce estimates and standard…

Exploring the Association between Transformational Leadership and Teacher's Self-Efficacy in Greek Education System: A Multilevel SEM Model

ERIC Educational Resources Information Center

Gkolia, Aikaterini; Koustelios, Athanasios; Belias, Dimitrios

2018-01-01

The main aim of this study is to examine the effect of principals' transformational leadership on teachers' self-efficacy across 77 different Greek elementary and secondary schools based on a centralized education system. For the investigation of the above effect multilevel Structural Equation Modelling analysis was conducted, recognizing the…

Taiwanese Students' Science Learning Self-Efficacy and Teacher and Student Science Hardiness: A Multilevel Model Approach

ERIC Educational Resources Information Center

Wang, Ya-Ling; Tsai, Chin-Chung

2016-01-01

This study aimed to investigate the factors accounting for science learning self-efficacy (the specific beliefs that people have in their ability to complete tasks in science learning) from both the teacher and the student levels. We thus propose a multilevel model to delineate its relationships with teacher and student science hardiness (i.e.,…

Modeling of Academic Achievement of Primary School Students in Ethiopia Using Bayesian Multilevel Approach

ERIC Educational Resources Information Center

Sebro, Negusse Yohannes; Goshu, Ayele Taye

2017-01-01

This study aims to explore Bayesian multilevel modeling to investigate variations of average academic achievement of grade eight school students. A sample of 636 students is randomly selected from 26 private and government schools by a two-stage stratified sampling design. Bayesian method is used to estimate the fixed and random effects. Input and…

Preschool Classroom Behavioral Context and School Readiness Outcomes for Low-Income Children: A Multilevel Examination of Child- and Classroom-Level Influences

ERIC Educational Resources Information Center

Bulotsky-Shearer, Rebecca J.; Dominguez, Ximena; Bell, Elizabeth R.

2012-01-01

Guided by an ecological theoretical model, the authors used a series of multilevel models to examine associations among children's individual problem behavior, the classroom behavioral context, and school readiness outcomes for a cohort of low-income children (N = 3,861) enrolled in 229 urban Head Start classrooms. Associations were examined…

Significant variations in the cervical cancer screening rate in China by individual-level and geographical measures of socioeconomic status: a multilevel model analysis of a nationally representative survey dataset.

PubMed

Bao, Heling; Zhang, Lei; Wang, Limin; Zhang, Mei; Zhao, Zhenping; Fang, Liwen; Cong, Shu; Zhou, Maigeng; Wang, Linhong

2018-05-01

Variations in cervical cancer screening rates in China have rarely been studied in depth. This study aimed to investigate cervical cancer screening rates in relation to both individual-level and geographical measures of socioeconomic status (SES). Data were obtained from women aged 21 years or older by face-to-face interviews between August 2013 and July 2014 as part of the Chinese Chronic Diseases and Risk Factors Surveillance. The geographical variables were obtained from the 2010 Chinese population census. The cervical cancer screening rates and 95% confidence interval (CI) were estimated and mapped. Multilevel logistic regression models were fitted. Overall, only 21.4% (95% CI: 19.6-23.1%) of 91,816 women aged ≥21 years reported having ever been screened for cervical cancer and significant geographical variations at both province and county levels were identified (P < 0.01). The cervical cancer screening rates were the lowest among the poor [13.9% (95% CI: 12.1-15.7%)], uninsured [14.4% (95% CI: 10.3-18.4%)], less-educated [16.0% (95% CI: 14.3-17.6%)], and agricultural employment [18.1% (95% CI: 15.8-20.4%)] women along with those residing in areas of low economic status [15.0% (95% CI: 11.8-18.2%)], of low urbanization [15.6% (95% CI: 13.4-17.7%)], and of low education status [16.0% (95% CI: 14.0-18.1%)]. The multilevel analysis also indicated that women with lower individual-level measures of SES residing in areas with low geographical measures of SES were significantly less likely to receive cervical cancer screening (P < 0.0001). Despite the launch of an organized cancer screening program in China, cervical cancer screening rates remain alarmingly low and significant variations based on geographical regions and measures of SES still exist. It is therefore essential to adopt strategies to better direct limited available public resources to priority groups. © 2018 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Biological and environmental determinants of 12-minute run performance in youth.

PubMed

Freitas, Duarte; Maia, José; Stasinopoulos, Mikis; Gouveia, Élvio Rúbio; Antunes, António M; Thomis, Martine; Lefevre, Johan; Claessens, Albrecht; Hedeker, Donald; Malina, Robert M

2017-11-01

The 12-minute run is a commonly used indicator of cardiorespiratory fitness in youth. Variation in growth and maturity status as potential correlates of test performance has not been systematically addressed. To evaluate biological and environmental determinants of 12-minute run performance in Portuguese youth aged 7-17 years. Mixed-longitudinal samples of 187 boys and 142 girls were surveyed in 1996, 1997 and 1998. The 12-minute run was the indicator of cardiorespiratory fitness. Height, body mass and five skinfolds were measured and skeletal maturity was assessed. Physical activity, socioeconomic status and area of residence were obtained with a questionnaire. Multi-level modelling was used for the analysis. Chronological age and sum of five skinfolds were significant predictors of 12-minute run performance. Older boys and girls ran longer distances than younger peers, while high levels of subcutaneous fat were associated with shorter running distances. Rural boys were more proficient in the 12-minute run than urban peers. Skeletal maturity, height, body mass index, physical activity and socioeconomic status were not significant predictors of 12-minute run performances. Age and sum of skinfolds in both sexes and rural residence in boys are significant predictors of 12-minute run performance in Portuguese youth.

A method of Modelling and Simulating the Back-to-Back Modular Multilevel Converter HVDC Transmission System

NASA Astrophysics Data System (ADS)

Wang, Lei; Fan, Youping; Zhang, Dai; Ge, Mengxin; Zou, Xianbin; Li, Jingjiao

2017-09-01

This paper proposes a method to simulate a back-to-back modular multilevel converter (MMC) HVDC transmission system. In this paper we utilize an equivalent networks to simulate the dynamic power system. Moreover, to account for the performance of converter station, core components of model of the converter station gives a basic model of simulation. The proposed method is applied to an equivalent real power system.

Gender-Specific Gene–Environment Interaction in Alcohol Dependence: The Impact of Daily Life Events and GABRA2

PubMed Central

Perry, Brea L.; Pescosolido, Bernice A.; Bucholz, Kathleen; Edenberg, Howard; Kramer, John; Kuperman, Samuel; Schuckit, Marc Alan; Nurnberger, John I.

2015-01-01

Gender-moderated gene–environment interactions are rarely explored, raising concerns about inaccurate specification of etiological models and inferential errors. The current study examined the influence of gender, negative and positive daily life events, and GABRA2 genotype (SNP rs279871) on alcohol dependence, testing two- and three-way interactions between these variables using multilevel regression models fit to data from 2,281 White participants in the Collaborative Study on the Genetics of Alcoholism. Significant direct effects of variables of interest were identified, as well as gender-specific moderation of genetic risk on this SNP by social experiences. Higher levels of positive life events were protective for men with the high-risk genotype, but not among men with the low-risk genotype or women, regardless of genotype. Our findings support the disinhibition theory of alcohol dependence, suggesting that gender differences in social norms, constraints and opportunities, and behavioral undercontrol may explain men and women’s distinct patterns of association. PMID:23974430

Multilevel selection analysis of a microbial social trait

PubMed Central

de Vargas Roditi, Laura; Boyle, Kerry E; Xavier, Joao B

2013-01-01

The study of microbial communities often leads to arguments for the evolution of cooperation due to group benefits. However, multilevel selection models caution against the uncritical assumption that group benefits will lead to the evolution of cooperation. We analyze a microbial social trait to precisely define the conditions favoring cooperation. We combine the multilevel partition of the Price equation with a laboratory model system: swarming in Pseudomonas aeruginosa. We parameterize a population dynamics model using competition experiments where we manipulate expression, and therefore the cost-to-benefit ratio of swarming cooperation. Our analysis shows that multilevel selection can favor costly swarming cooperation because it causes population expansion. However, due to high costs and diminishing returns constitutive cooperation can only be favored by natural selection when relatedness is high. Regulated expression of cooperative genes is a more robust strategy because it provides the benefits of swarming expansion without the high cost or the diminishing returns. Our analysis supports the key prediction that strong group selection does not necessarily mean that microbial cooperation will always emerge. PMID:23959025

Spatial Random Effects Survival Models to Assess Geographical Inequalities in Dengue Fever Using Bayesian Approach: a Case Study

NASA Astrophysics Data System (ADS)

Astuti Thamrin, Sri; Taufik, Irfan

2018-03-01

Dengue haemorrhagic fever (DHF) is an infectious disease caused by dengue virus. The increasing number of people with DHF disease correlates with the neighbourhood, for example sub-districts, and the characteristics of the sub-districts are formed from individuals who are domiciled in the sub-districts. Data containing individuals and sub-districts is a hierarchical data structure, called multilevel analysis. Frequently encountered response variable of the data is the time until an event occurs. Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in DHF survival. Using a case study approach, we report on the implications of using multilevel with spatial survival models to study geographical inequalities in all cause survival.

Regional variations in mortality rates in England and Wales: an analysis using multi-level modelling.

PubMed

Langford, I H; Bentham, G

1996-03-01

Mortality rates in England and Wales display a persistent regional pattern indicating generally poorer health in the North and West. Some of this is simply a reflection of regional differences in the extent of social deprivation which is known to exert a profound influence on health. Part of the pattern may also be the result of regional differences in urbanization which also affect mortality rates. However, there may be important regional differences over and above these compositional effects. This study attempts to establish the magnitude of such independent regional differences in mortality rates by using the techniques of multi-level modelling. Standardized mortality rates (SMRs) for males and females under 65 for 1989-91 in local authority districts are grouped into categories using the ACORN classification scheme. The Townsend Index is included as a measure of social deprivation. Using a cross-classified multi-level model, it is shown that region accounts for approximately four times more variation in SMRs than is explained by the ACORN classification. Analysis of diagnostic residuals show a clear North-South divide in excess mortality when both regional and socio-economic classification of districts are modelled simultaneously, a possibility allowed for by the use of a multi-level model.

Development of an algorithm for controlling a multilevel three-phase converter

NASA Astrophysics Data System (ADS)

Taissariyeva, Kyrmyzy; Ilipbaeva, Lyazzat

2017-08-01

This work is devoted to the development of an algorithm for controlling transistors in a three-phase multilevel conversion system. The developed algorithm allows to organize a correct operation and describes the state of transistors at each moment of time when constructing a computer model of a three-phase multilevel converter. The developed algorithm of operation of transistors provides in-phase of a three-phase converter and obtaining a sinusoidal voltage curve at the converter output.

Using multilevel modeling to assess case-mix adjusters in consumer experience surveys in health care.

PubMed

Damman, Olga C; Stubbe, Janine H; Hendriks, Michelle; Arah, Onyebuchi A; Spreeuwenberg, Peter; Delnoij, Diana M J; Groenewegen, Peter P

2009-04-01

Ratings on the quality of healthcare from the consumer's perspective need to be adjusted for consumer characteristics to ensure fair and accurate comparisons between healthcare providers or health plans. Although multilevel analysis is already considered an appropriate method for analyzing healthcare performance data, it has rarely been used to assess case-mix adjustment of such data. The purpose of this article is to investigate whether multilevel regression analysis is a useful tool to detect case-mix adjusters in consumer assessment of healthcare. We used data on 11,539 consumers from 27 Dutch health plans, which were collected using the Dutch Consumer Quality Index health plan instrument. We conducted multilevel regression analyses of consumers' responses nested within health plans to assess the effects of consumer characteristics on consumer experience. We compared our findings to the results of another methodology: the impact factor approach, which combines the predictive effect of each case-mix variable with its heterogeneity across health plans. Both multilevel regression and impact factor analyses showed that age and education were the most important case-mix adjusters for consumer experience and ratings of health plans. With the exception of age, case-mix adjustment had little impact on the ranking of health plans. On both theoretical and practical grounds, multilevel modeling is useful for adequate case-mix adjustment and analysis of performance ratings.

Univariate and multivariate spatial models of health facility utilisation for childhood fevers in an area on the coast of Kenya.

PubMed

Ouma, Paul O; Agutu, Nathan O; Snow, Robert W; Noor, Abdisalan M

2017-09-18

Precise quantification of health service utilisation is important for the estimation of disease burden and allocation of health resources. Current approaches to mapping health facility utilisation rely on spatial accessibility alone as the predictor. However, other spatially varying social, demographic and economic factors may affect the use of health services. The exclusion of these factors can lead to the inaccurate estimation of health facility utilisation. Here, we compare the accuracy of a univariate spatial model, developed only from estimated travel time, to a multivariate model that also includes relevant social, demographic and economic factors. A theoretical surface of travel time to the nearest public health facility was developed. These were assigned to each child reported to have had fever in the Kenya demographic and health survey of 2014 (KDHS 2014). The relationship of child treatment seeking for fever with travel time, household and individual factors from the KDHS2014 were determined using multilevel mixed modelling. Bayesian information criterion (BIC) and likelihood ratio test (LRT) tests were carried out to measure how selected factors improve parsimony and goodness of fit of the time model. Using the mixed model, a univariate spatial model of health facility utilisation was fitted using travel time as the predictor. The mixed model was also used to compute a multivariate spatial model of utilisation, using travel time and modelled surfaces of selected household and individual factors as predictors. The univariate and multivariate spatial models were then compared using the receiver operating area under the curve (AUC) and a percent correct prediction (PCP) test. The best fitting multivariate model had travel time, household wealth index and number of children in household as the predictors. These factors reduced BIC of the time model from 4008 to 2959, a change which was confirmed by the LRT test. Although there was a high correlation of the two modelled probability surfaces (Adj R 2  = 88%), the multivariate model had better AUC compared to the univariate model; 0.83 versus 0.73 and PCP 0.61 versus 0.45 values. Our study shows that a model that uses travel time, as well as household and individual-level socio-demographic factors, results in a more accurate estimation of use of health facilities for the treatment of childhood fever, compared to one that relies on only travel time.

Multi-level, multi-scale resource selection functions and resistance surfaces for conservation planning: Pumas as a case study

PubMed Central

Vickers, T. Winston; Ernest, Holly B.; Boyce, Walter M.

2017-01-01

The importance of examining multiple hierarchical levels when modeling resource use for wildlife has been acknowledged for decades. Multi-level resource selection functions have recently been promoted as a method to synthesize resource use across nested organizational levels into a single predictive surface. Analyzing multiple scales of selection within each hierarchical level further strengthens multi-level resource selection functions. We extend this multi-level, multi-scale framework to modeling resistance for wildlife by combining multi-scale resistance surfaces from two data types, genetic and movement. Resistance estimation has typically been conducted with one of these data types, or compared between the two. However, we contend it is not an either/or issue and that resistance may be better-modeled using a combination of resistance surfaces that represent processes at different hierarchical levels. Resistance surfaces estimated from genetic data characterize temporally broad-scale dispersal and successful breeding over generations, whereas resistance surfaces estimated from movement data represent fine-scale travel and contextualized movement decisions. We used telemetry and genetic data from a long-term study on pumas (Puma concolor) in a highly developed landscape in southern California to develop a multi-level, multi-scale resource selection function and a multi-level, multi-scale resistance surface. We used these multi-level, multi-scale surfaces to identify resource use patches and resistant kernel corridors. Across levels, we found puma avoided urban, agricultural areas, and roads and preferred riparian areas and more rugged terrain. For other landscape features, selection differed among levels, as did the scales of selection for each feature. With these results, we developed a conservation plan for one of the most isolated puma populations in the U.S. Our approach captured a wide spectrum of ecological relationships for a population, resulted in effective conservation planning, and can be readily applied to other wildlife species. PMID:28609466

Multi-level, multi-scale resource selection functions and resistance surfaces for conservation planning: Pumas as a case study.

PubMed

Zeller, Katherine A; Vickers, T Winston; Ernest, Holly B; Boyce, Walter M

2017-01-01

The importance of examining multiple hierarchical levels when modeling resource use for wildlife has been acknowledged for decades. Multi-level resource selection functions have recently been promoted as a method to synthesize resource use across nested organizational levels into a single predictive surface. Analyzing multiple scales of selection within each hierarchical level further strengthens multi-level resource selection functions. We extend this multi-level, multi-scale framework to modeling resistance for wildlife by combining multi-scale resistance surfaces from two data types, genetic and movement. Resistance estimation has typically been conducted with one of these data types, or compared between the two. However, we contend it is not an either/or issue and that resistance may be better-modeled using a combination of resistance surfaces that represent processes at different hierarchical levels. Resistance surfaces estimated from genetic data characterize temporally broad-scale dispersal and successful breeding over generations, whereas resistance surfaces estimated from movement data represent fine-scale travel and contextualized movement decisions. We used telemetry and genetic data from a long-term study on pumas (Puma concolor) in a highly developed landscape in southern California to develop a multi-level, multi-scale resource selection function and a multi-level, multi-scale resistance surface. We used these multi-level, multi-scale surfaces to identify resource use patches and resistant kernel corridors. Across levels, we found puma avoided urban, agricultural areas, and roads and preferred riparian areas and more rugged terrain. For other landscape features, selection differed among levels, as did the scales of selection for each feature. With these results, we developed a conservation plan for one of the most isolated puma populations in the U.S. Our approach captured a wide spectrum of ecological relationships for a population, resulted in effective conservation planning, and can be readily applied to other wildlife species.

Identifying Synergies in Multilevel Interventions.

PubMed

Lewis, Megan A; Fitzgerald, Tania M; Zulkiewicz, Brittany; Peinado, Susana; Williams, Pamela A

2017-04-01

Social ecological models of health often describe multiple levels of influence that interact to influence health. However, it is still common for interventions to target only one or two of these levels, perhaps owing in part to a lack of guidance on how to design multilevel interventions to achieve optimal impact. The convergence strategy emphasizes that interventions at different levels mutually reinforce each other by changing patterns of interaction among two or more intervention audiences; this strategy is one approach for combining interventions at different levels to produce synergistic effects. We used semistructured interviews with 65 representatives in a cross-site national initiative that enhanced health and outcomes for patients with diabetes to examine whether the convergence strategy was a useful conceptual model for multilevel interventions. Using a framework analysis approach to analyze qualitative interview data, we found three synergistic themes that match the convergence strategy and support how multilevel interventions can be successful. These three themes were (1) enhancing engagement between patient and provider and access to quality care; (2) supporting communication, information sharing, and coordination among providers, community stakeholders, and systems; and (3) building relationships and fostering alignment among providers, community stakeholders, and systems. These results support the convergence strategy as a testable conceptual model and provide examples of successful intervention strategies for combining multilevel interventions to produce synergies across levels and promote diabetes self-management and that may extend to management of other chronic illnesses as well.

Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures

PubMed Central

Ngendahimana, David K.; Fagerholm, Cara L.; Sun, Jiayang; Bruckman, Laura S.

2017-01-01

Accelerated weathering exposures were performed on poly(ethylene-terephthalate) (PET) films. Longitudinal multi-level predictive models as a function of PET grades and exposure types were developed for the change in yellowness index (YI) and haze (%). Exposures with similar change in YI were modeled using a linear fixed-effects modeling approach. Due to the complex nature of haze formation, measurement uncertainty, and the differences in the samples’ responses, the change in haze (%) depended on individual samples’ responses and a linear mixed-effects modeling approach was used. When compared to fixed-effects models, the addition of random effects in the haze formation models significantly increased the variance explained. For both modeling approaches, diagnostic plots confirmed independence and homogeneity with normally distributed residual errors. Predictive R2 values for true prediction error and predictive power of the models demonstrated that the models were not subject to over-fitting. These models enable prediction under pre-defined exposure conditions for a given exposure time (or photo-dosage in case of UV light exposure). PET degradation under cyclic exposures combining UV light and condensing humidity is caused by photolytic and hydrolytic mechanisms causing yellowing and haze formation. Quantitative knowledge of these degradation pathways enable cross-correlation of these lab-based exposures with real-world conditions for service life prediction. PMID:28498875

A Multilevel Modelling Approach to Investigating Factors Impacting Science Achievement for Secondary School Students: PISA Hong Kong Sample

ERIC Educational Resources Information Center

Sun, Letao; Bradley, Kelly D.; Akers, Kathryn

2012-01-01

This study utilized data from the 2006 Programme for International Student Assessment Hong Kong sample to investigate the factors that impact the science achievement of 15-year-old students. A multilevel model was used to examine the factors from both student and school perspectives. At the student level, the results indicated that male students,…

Longitudinal Assessment of Intellectual Abilities of Children with Williams Syndrome: Multilevel Modeling of Performance on the Kaufman Brief Intelligence Test--Second Edition

ERIC Educational Resources Information Center

Mervis, Carolyn B.; Kistler, Doris J.; John, Angela E.; Morris, Colleen A.

2012-01-01

Multilevel modeling was used to address the longitudinal stability of standard scores (SSs) measuring intellectual ability for children with Williams syndrome (WS). Participants were 40 children with genetically confirmed WS who completed the Kaufman Brief Intelligence Test--Second Edition (KBIT-2; A. S. Kaufman & N. L. Kaufman, 2004) 4-7…

The Use of Multilevel Modeling to Estimate Which Measures Are Most Influential in Determining an Institution's Placement in Carnegie's New Doctoral/Research University Classification Schema

ERIC Educational Resources Information Center

Micceri, Theodore

2007-01-01

This research sought to determine whether any measure(s) used in the Carnegie Foundation's classification of Doctoral/Research Universities contribute to a greater degree than other measures to final rank placement. Multilevel Modeling (MLM) was applied to all eight of the Carnegie Foundation's predictor measures using final rank…

A Primer for Analyzing Nested Data: Multilevel Modeling in SPSS Using an Example from a REL Study. REL 2015-046

ERIC Educational Resources Information Center

O'Dwyer, Laura M.; Parker, Caroline E.

2014-01-01

Analyzing data that possess some form of nesting is often challenging for applied researchers or district staff who are involved in or in charge of conducting data analyses. This report provides a description of the challenges for analyzing nested data and provides a primer of how multilevel regression modeling may be used to resolve these…

Detecting Intervention Effects in a Cluster-Randomized Design Using Multilevel Structural Equation Modeling for Binary Responses

ERIC Educational Resources Information Center

Cho, Sun-Joo; Preacher, Kristopher J.; Bottge, Brian A.

2015-01-01

Multilevel modeling (MLM) is frequently used to detect group differences, such as an intervention effect in a pre-test--post-test cluster-randomized design. Group differences on the post-test scores are detected by controlling for pre-test scores as a proxy variable for unobserved factors that predict future attributes. The pre-test and post-test…

Conceptualizing and Testing Random Indirect Effects and Moderated Mediation in Multilevel Models: New Procedures and Recommendations

ERIC Educational Resources Information Center

Bauer, Daniel J.; Preacher, Kristopher J.; Gil, Karen M.

2006-01-01

The authors propose new procedures for evaluating direct, indirect, and total effects in multilevel models when all relevant variables are measured at Level 1 and all effects are random. Formulas are provided for the mean and variance of the indirect and total effects and for the sampling variances of the average indirect and total effects.…

A closed-loop multi-level model of glucose homeostasis

PubMed Central

Uluseker, Cansu; Simoni, Giulia; Dauriz, Marco; Matone, Alice

2018-01-01

Background The pathophysiologic processes underlying the regulation of glucose homeostasis are considerably complex at both cellular and systemic level. A comprehensive and structured specification for the several layers of abstraction of glucose metabolism is often elusive, an issue currently solvable with the hierarchical description provided by multi-level models. In this study we propose a multi-level closed-loop model of whole-body glucose homeostasis, coupled with the molecular specifications of the insulin signaling cascade in adipocytes, under the experimental conditions of normal glucose regulation and type 2 diabetes. Methodology/Principal findings The ordinary differential equations of the model, describing the dynamics of glucose and key regulatory hormones and their reciprocal interactions among gut, liver, muscle and adipose tissue, were designed for being embedded in a modular, hierarchical structure. The closed-loop model structure allowed self-sustained simulations to represent an ideal in silico subject that adjusts its own metabolism to the fasting and feeding states, depending on the hormonal context and invariant to circadian fluctuations. The cellular level of the model provided a seamless dynamic description of the molecular mechanisms downstream the insulin receptor in the adipocytes by accounting for variations in the surrounding metabolic context. Conclusions/Significance The combination of a multi-level and closed-loop modeling approach provided a fair dynamic description of the core determinants of glucose homeostasis at both cellular and systemic scales. This model architecture is intrinsically open to incorporate supplementary layers of specifications describing further individual components influencing glucose metabolism. PMID:29420588

A collision dynamics model of a multi-level train

DOT National Transportation Integrated Search

2006-11-05

In train collisions, multi-level rail passenger vehicles can deform in modes that are different from the behavior of single level cars. The deformation in single level cars usually occurs at the front end during a collision. In one particular inciden...

Implementation challenges and successes of a population-based colorectal cancer screening program: a qualitative study of stakeholder perspectives.

PubMed

Liles, Elizabeth G; Schneider, Jennifer L; Feldstein, Adrianne C; Mosen, David M; Perrin, Nancy; Rosales, Ana Gabriela; Smith, David H

2015-03-29

Few studies describe system-level challenges or facilitators to implementing population-based colorectal cancer (CRC) screening outreach programs. Our qualitative study explored viewpoints of multilevel stakeholders before, during, and after implementation of a centralized outreach program. Program implementation was part of a broader quality-improvement initiative. During 2008-2010, we conducted semi-structured, open-ended individual interviews and focus groups at Kaiser Permanente Northwest (KPNW), a not-for-profit group model health maintenance organization using the practical robust implementation and sustainability model to explore external and internal barriers to CRC screening. We interviewed 55 stakeholders: 8 health plan leaders, 20 primary care providers, 4 program managers, and 23 endoscopy specialists (15 gastroenterologists, 8 general surgeons), and analyzed interview transcripts to identify common as well as divergent opinions expressed by stakeholders. The majority of stakeholders at various levels consistently reported that an automated telephone-reminder system to contact patients and coordinate mailing fecal tests alleviated organizational constraints on staff's time and resources. Changing to a single-sample fecal immunochemical test (FIT) lessened patient and provider concerns about feasibility and accuracy of fecal testing. The centralized telephonic outreach program did, however, result in some screening duplication and overuse. Higher rates of FIT completion and a higher proportion of positive results with FIT required more colonoscopies. Addressing barriers at multiple levels of a health system by changing the delivery system design to add a centralized outreach program, switching to a more accurate and easier-to-use fecal test, and providing educational and electronic support had both benefits and problematic consequences. Other health care organizations can use our results to understand the complexities of implementing centralized screening programs.

Active Learning Increases Children's Physical Activity across Demographic Subgroups.

PubMed

Bartholomew, John B; Jowers, Esbelle M; Roberts, Gregory; Fall, Anna-Mária; Errisuriz, Vanessa L; Vaughn, Sharon

2018-01-01

Given the need to find more opportunities for physical activity within the elementary school day, this study was designed to asses the impact of I-CAN!, active lessons on: 1) student physical activity (PA) outcomes via accelerometry; and 2) socioeconomic status (SES), race, sex, body mass index (BMI), or fitness as moderators of this impact. Participants were 2,493 fourth grade students (45.9% male, 45.8% white, 21.7% low SES) from 28 central Texas elementary schools randomly assigned to intervention (n=19) or control (n=9). Multilevel regression models evaluated the effect of I-CAN! on PA and effect sizes were calculated. The moderating effects of SES, race, sex, BMI, and fitness were examined in separate models. Students in treatment schools took significantly more steps than those in control schools (β = 125.267, SE = 41.327, p = .002, d = .44). I-CAN! had a significant effect on MVPA with treatment schools realizing 80% (β = 0.796, SE =0.251, p = .001; d = .38) more MVPA than the control schools. There were no significant school-level differences on sedentary behavior (β = -0.177, SE = 0.824, p = .83). SES, race, sex, BMI, and fitness level did not moderate the impact of active learning on step count and MVPA. Active learning increases PA within elementary students, and does so consistently across demographic sub-groups. This is important as these sub-groups represent harder to reach populations for PA interventions. While these lessons may not be enough to help children reach daily recommendations of PA, they can supplement other opportunities for PA. This speaks to the potential of schools to adopt policy change to require active learning.

Using a dyadic logistic multilevel model to analyze couple data.

PubMed

Preciado, Mariana A; Krull, Jennifer L; Hicks, Andrew; Gipson, Jessica D

2016-02-01

There is growing recognition within the sexual and reproductive health field of the importance of incorporating both partners' perspectives when examining sexual and reproductive health behaviors. Yet, the analytical approaches to address couple data have not been readily integrated and utilized within the demographic and public health literature. This paper seeks to provide readers unfamiliar with analytical approaches to couple data an applied example of the use of dyadic logistic multilevel modeling, a useful approach to analyzing couple data to assess the individual, partner and couple characteristics that are related to individuals' reproductively relevant beliefs, attitudes and behaviors. The use of multilevel models in reproductive health research can help researchers develop a more comprehensive picture of the way in which individuals' reproductive health outcomes are situated in a larger relationship and cultural context. Copyright © 2016 Elsevier Inc. All rights reserved.

Dispositional and Environmental Predictors of the Development of Internalizing Problems in Childhood: Testing a Multilevel Model.

PubMed

Hastings, Paul D; Helm, Jonathan; Mills, Rosemary S L; Serbin, Lisa A; Stack, Dale M; Schwartzman, Alex E

2015-07-01

This investigation evaluated a multilevel model of dispositional and environmental factors contributing to the development of internalizing problems from preschool-age to school-age. In a sample of 375 families (185 daughters, 190 sons) drawn from three independent samples, preschoolers' behavioral inhibition, cortisol and gender were examined as moderators of the links between mothers' negative parenting behavior, negative emotional characteristics, and socioeconomic status when children were 3.95 years, and their internalizing problems when they were 8.34 years. Children's dispositional characteristics moderated all associations between these environmental factors and mother-reported internalizing problems in patterns that were consistent with either diathesis-stress or differential-susceptibility models of individual-environment interaction, and with gender models of developmental psychopathology. Greater inhibition and lower socioeconomic status were directly predictive of more teacher reported internalizing problems. These findings highlight the importance of using multilevel models within a bioecological framework to understand the complex pathways through which internalizing difficulties develop.

Individual schooling and women's community-level media exposure: a multilevel analysis of normative influences associated with women's justification of wife beating in Bangladesh.

PubMed

Krause, Kathleen H; Haardörfer, Regine; Yount, Kathryn M

2017-02-01

Our objective was to examine the multilevel correlates of women's justification of wife beating in Bangladesh, a form of intimate partner violence (IPV). We focus on individual-level schooling, community-level media exposure among women and their interaction. A cross-sectional study using data from the 2011 Bangladesh Demographic and Health Survey. Our sample included 17 749 ever-married women 15-49 years in 600 communities. We fit 6 multilevel logistic regression models to examine factors associated with justifying IPV; focusing on a woman's completed grades of schooling; frequent (at least once weekly) community-level media exposure among women via newspaper/magazine, television and radio; and their cross-level interaction. At the individual level, completing more grades of schooling than the community average was negatively associated with justifying IPV (0.95, 95% CI 0.94 to 0.97). The main effects of women's community-level media exposure were not significant, but suggested that frequent exposure to newspaper/magazine or television was negatively associated with justifying IPV, while exposure to radio was positively associated. In cross-level interactions, a woman's completed grades of schooling above the community average was protective against justifying IPV, even in communities where women's exposure to radio would otherwise increase the odds of justifying IPV. Different forms of media likely send different messages about gender and IPV. Girls' schooling should remain a priority, given its protective effect against justifying wife beating. Targeting girls and women who do not receive any schooling for intervention may yield the most benefit in terms of normative change regarding IPV against women. 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/.

The median hazard ratio: a useful measure of variance and general contextual effects in multilevel survival analysis.

PubMed

Austin, Peter C; Wagner, Philippe; Merlo, Juan

2017-03-15

Multilevel data occurs frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models (MLRM). MLRM incorporate cluster-specific random effects which allow one to partition the total individual variance into between-cluster variation and between-individual variation. Statistically, MLRM account for the dependency of the data within clusters and provide correct estimates of uncertainty around regression coefficients. Substantively, the magnitude of the effect of clustering provides a measure of the General Contextual Effect (GCE). When outcomes are binary, the GCE can also be quantified by measures of heterogeneity like the Median Odds Ratio (MOR) calculated from a multilevel logistic regression model. Time-to-event outcomes within a multilevel structure occur commonly in epidemiological and medical research. However, the Median Hazard Ratio (MHR) that corresponds to the MOR in multilevel (i.e., 'frailty') Cox proportional hazards regression is rarely used. Analogously to the MOR, the MHR is the median relative change in the hazard of the occurrence of the outcome when comparing identical subjects from two randomly selected different clusters that are ordered by risk. We illustrate the application and interpretation of the MHR in a case study analyzing the hazard of mortality in patients hospitalized for acute myocardial infarction at hospitals in Ontario, Canada. We provide R code for computing the MHR. The MHR is a useful and intuitive measure for expressing cluster heterogeneity in the outcome and, thereby, estimating general contextual effects in multilevel survival analysis. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

The median hazard ratio: a useful measure of variance and general contextual effects in multilevel survival analysis

PubMed Central

Wagner, Philippe; Merlo, Juan

2016-01-01

Multilevel data occurs frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models (MLRM). MLRM incorporate cluster‐specific random effects which allow one to partition the total individual variance into between‐cluster variation and between‐individual variation. Statistically, MLRM account for the dependency of the data within clusters and provide correct estimates of uncertainty around regression coefficients. Substantively, the magnitude of the effect of clustering provides a measure of the General Contextual Effect (GCE). When outcomes are binary, the GCE can also be quantified by measures of heterogeneity like the Median Odds Ratio (MOR) calculated from a multilevel logistic regression model. Time‐to‐event outcomes within a multilevel structure occur commonly in epidemiological and medical research. However, the Median Hazard Ratio (MHR) that corresponds to the MOR in multilevel (i.e., ‘frailty’) Cox proportional hazards regression is rarely used. Analogously to the MOR, the MHR is the median relative change in the hazard of the occurrence of the outcome when comparing identical subjects from two randomly selected different clusters that are ordered by risk. We illustrate the application and interpretation of the MHR in a case study analyzing the hazard of mortality in patients hospitalized for acute myocardial infarction at hospitals in Ontario, Canada. We provide R code for computing the MHR. The MHR is a useful and intuitive measure for expressing cluster heterogeneity in the outcome and, thereby, estimating general contextual effects in multilevel survival analysis. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:27885709

Longitudinal Multilevel Models of the Big Fish Little Pond Effect on Academic Self-Concept: Counterbalancing Contrast and Reflected Glory Effects in Hong Kong Schools.

ERIC Educational Resources Information Center

Marsh, Herbert W.; Kong, Chit-Kwong; Hau, Kit-Tai

Longitudinal multilevel path models (7,997 students, 44 high schools, 4 years) evaluated the effects of school-average achievement and perceived school status on academic self-concept in Hong Kong, a collectivist culture with a highly achievement-segregated high school system. Consistent with a priori predictions based on the big-fish-little-pond…

Assessing a multilevel model of young children’s oral health with national survey data

PubMed Central

Bramlett, Matthew D.; Soobader, Mah-J; Fisher-Owens, Susan A.; Weintraub, Jane A.; Gansky, Stuart A.; Platt, Larry J.; Newacheck, Paul W.

2010-01-01

Objectives To empirically test a multilevel conceptual model of children’s oral health incorporating 22 domains of children’s oral health across four levels: child, family, neighborhood and state. Data source The 2003 National Survey of Children’s Health, a module of the State and Local Area Integrated Telephone Survey conducted by the Centers for Disease Control and Prevention’s National Center for Health Statistics, is a nationally representative telephone survey of caregivers of children. Study design We examined child-, family-, neighborhood-, and state-level factors influencing parent’s report of children’s oral health using a multilevel logistic regression model, estimated for 26 736 children ages 1–5 years. Principal findings Factors operating at all four levels were associated with the likelihood that parents rated their children’s oral health as fair or poor, although most significant correlates are represented at the child or family level. Of 22 domains identified in our conceptual model, 15 domains contained factors significantly associated with young children’s oral health. At the state level, access to fluoridated water was significantly associated with favorable oral health for children. Conclusions Our results suggest that efforts to understand or improve children’s oral health should consider a multilevel approach that goes beyond solely child-level factors. PMID:20370808

Disentangling the Relative Influence of Schools and Neighborhoods on Adolescents’ Risk for Depressive Symptoms

PubMed Central

Milliren, Carly E.; Evans, Clare R.; Subramanian, S. V.; Richmond, Tracy K.

2015-01-01

Objectives. Although schools and neighborhoods influence health, little is known about their relative importance, or the influence of one context after the influence of the other has been taken into account. We simultaneously examined the influence of each setting on depression among adolescents. Methods. Analyzing data from wave 1 (1994–1995) of the National Longitudinal Study of Adolescent Health, we used cross-classified multilevel modeling to examine between-level variation and individual-, school-, and neighborhood-level predictors of adolescent depressive symptoms. Also, we compared the results of our cross-classified multilevel models (CCMMs) with those of a multilevel model wherein either school or neighborhood was excluded. Results. In CCMMs, the school-level random effect was significant and more than 3 times the neighborhood-level random effect, even after individual-level characteristics had been taken into account. Individual-level indicators (e.g., race/ethnicity, socioeconomic status) were associated with depressive symptoms, but there was no association with either school- or neighborhood-level fixed effects. The between-level variance in depressive symptoms was driven largely by schools as opposed to neighborhoods. Conclusions. Schools appear to be more salient than neighborhoods in explaining variation in depressive symptoms. Future work incorporating cross-classified multilevel modeling is needed to understand the relative effects of schools and neighborhoods. PMID:25713969

A longitudinal multilevel CFA-MTMM model for interchangeable and structurally different methods

PubMed Central

Koch, Tobias; Schultze, Martin; Eid, Michael; Geiser, Christian

2014-01-01

One of the key interests in the social sciences is the investigation of change and stability of a given attribute. Although numerous models have been proposed in the past for analyzing longitudinal data including multilevel and/or latent variable modeling approaches, only few modeling approaches have been developed for studying the construct validity in longitudinal multitrait-multimethod (MTMM) measurement designs. The aim of the present study was to extend the spectrum of current longitudinal modeling approaches for MTMM analysis. Specifically, a new longitudinal multilevel CFA-MTMM model for measurement designs with structurally different and interchangeable methods (called Latent-State-Combination-Of-Methods model, LS-COM) is presented. Interchangeable methods are methods that are randomly sampled from a set of equivalent methods (e.g., multiple student ratings for teaching quality), whereas structurally different methods are methods that cannot be easily replaced by one another (e.g., teacher, self-ratings, principle ratings). Results of a simulation study indicate that the parameters and standard errors in the LS-COM model are well recovered even in conditions with only five observations per estimated model parameter. The advantages and limitations of the LS-COM model relative to other longitudinal MTMM modeling approaches are discussed. PMID:24860515

Contextual determinants of neonatal mortality using two analysis methods, Rio Grande do Sul, Brazil.

PubMed

Zanini, Roselaine Ruviaro; Moraes, Anaelena Bragança de; Giugliani, Elsa Regina Justo; Riboldi, João

2011-02-01

To analyze neonatal mortality determinants using multilevel logistic regression and classic hierarchical models. Cohort study including 138,407 live births with birth certificates and 1,134 neonatal deaths recorded in 2003, in the state of Rio Grande do Sul, Southern Brazil. The Information System on Live Births and mortality records were linked for gathering information on individual-level exposures. Sociodemographic data and information on the pregnancy, childbirth care and characteristics of the children at birth were collected. The associated factors were estimated and compared by traditional and multilevel logistic regression analysis. The neonatal mortality rate was 8.19 deaths per 1,000 live births. Low birth weight, 1- and 5-minute Apgar score below eight, congenital malformation, pre-term birth and previous fetal loss were associated with neonatal death in the traditional model. Elective cesarean section had a protective effect. Previous fetal loss did not remain significant in the multilevel model, but the inclusion of a contextual variable (poverty rate) showed that 15% of neonatal mortality variation can be explained by varying poverty rates in the microregions. The use of multilevel models showed a small effect of contextual determinants on the neonatal mortality rate. There was found a positive association with the poverty rate in the general model, and the proportion of households with water supply among preterm newborns.

Emotion Regulation in Emerging Adult Couples: Temperament, Attachment, and HPA Response to Conflict

PubMed Central

Laurent, Heidemarie; Powers, Sally

2007-01-01

Difficulty managing the stress of conflict in close relationships can lead to mental and physical health problems, possibly through dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, the neuroendocrine stress response system. Temperament, an individual characteristic, and attachment, a dyadic characteristic, have both been implicated in emotion regulation processes and physiological reactivity, yet there is no clear consensus on how the two work together to influence the stress response, especially after childhood. The present study investigated the ways in which temperament and attachment together predict HPA response in emerging adult couples. Analyses using multilevel modeling (HLM) found that partners' dyadic fit on attachment avoidance impacted females' cortisol response patterns, and attachment avoidance further moderated the effect of males' emotionality on both their own and their partners' cortisol. Results are discussed in terms of emotional coregulation processes in romantic attachment. PMID:17681662

Social participation and self-rated health among older male veterans and non-veterans.

PubMed

Choi, Namkee G; DiNitto, Diana M; Marti, C Nathan

2016-08-01

To examine self-rated health (SRH) and its association with social participation, along with physical and mental health indicators, among USA male veterans and non-veterans aged ≥65 years. The two waves of the National Health and Aging Trend Study provided data (n = 2845 at wave 1; n = 2235 at wave 2). Multilevel mixed effects generalized linear models were fit to test the hypotheses. Despite their older age, veterans did not differ from non-veterans in their physical, mental and cognitive health, and they had better SRH. However, black and Hispanic veterans had lower SRH than non-Hispanic white veterans. Formal group activities and outings for enjoyment were positively associated with better SRH for veterans, non-veterans and all veteran cohorts. Aging veterans, especially black and Hispanic veterans, require programs and services that will help increase their social connectedness. Geriatr Gerontol Int 2016; 16: 920-927. © 2015 Japan Geriatrics Society.

Toddlers’ transition to out-of-home day care: Settling into a new care environment

PubMed Central

Datler, Wilfried; Ereky-Stevens, Katharina; Hover-Reisner, Nina; Malmberg, Lars-Erik

2012-01-01

This study investigates toddlers’ initial reaction to day care entry and their behaviour change over the first few months in care. One hundred and four toddlers (10–33 months of age) in Viennese childcare centres participated in the study. One-hour video observations were carried out at 3 time points during the first 4 months in the setting and coded into a total of 36 5-min observation segments. Multilevel models (observation segments nested within children) with an autoregressive error structure fitted data well. Two weeks after entry into care, toddlers’ levels of affect and interaction were low. Overall, changes in all areas of observed behaviour were less than expected. There were considerable individual differences in change over time, mostly unrelated to child characteristics. Significant associations between children's positive affect, their dynamic interactions and their explorative and investigative interest were found. PMID:22721743

Spouses’ Attachment Pairings Predict Neuroendocrine, Behavioral, and Psychological Responses to Marital Conflict

PubMed Central

Beck, Lindsey A.; Pietromonaco, Paula R.; DeBuse, Casey J.; Powers, Sally I.; Sayer, Aline G.

2014-01-01

This research investigated how spouses’ attachment styles jointly contributed to their stress responses. Newlywed couples discussed relationship conflicts. Salivary cortisol indexed physiological stress; observer-rated behaviors indexed behavioral stress; self-reported distress indexed psychological stress. Multilevel modeling tested predictions that couples including one anxious and one avoidant partner or two anxious partners would show distinctive stress responses. As predicted, couples with anxious wives and avoidant husbands showed physiological reactivity in anticipation of conflict: Both spouses showed sharp increases in cortisol, followed by rapid declines. These couples also showed distinctive behaviors during conflict: Anxious wives had difficulty recognizing avoidant husbands’ distress, and avoidant husbands had difficulty approaching anxious wives for support. Contrary to predictions, couples including two anxious partners did not show distinctive stress responses. Findings suggest that the fit between partners’ attachment styles can improve understanding of relationships by specifying conditions under which partners’ attachment characteristics jointly influence individual and relationship outcomes. PMID:23773048

Small area variation in diabetes prevalence in Puerto Rico.

PubMed

Tierney, Edward F; Burrows, Nilka R; Barker, Lawrence E; Beckles, Gloria L; Boyle, James P; Cadwell, Betsy L; Kirtland, Karen A; Thompson, Theodore J

2013-06-01

To estimate the 2009 prevalence of diagnosed diabetes in Puerto Rico among adults ≥ 20 years of age in order to gain a better understanding of its geographic distribution so that policymakers can more efficiently target prevention and control programs. A Bayesian multilevel model was fitted to the combined 2008-2010 Behavioral Risk Factor Surveillance System and 2009 United States Census data to estimate diabetes prevalence for each of the 78 municipios (counties) in Puerto Rico. The mean unadjusted estimate for all counties was 14.3% (range by county, 9.9%-18.0%). The average width of the confidence intervals was 6.2%. Adjusted and unadjusted estimates differed little. These 78 county estimates are higher on average and showed less variability (i.e., had a smaller range) than the previously published estimates of the 2008 diabetes prevalence for all United States counties (mean, 9.9%; range, 3.0%-18.2%).

Disease dynamics and costly punishment can foster socially imposed monogamy.

PubMed

Bauch, Chris T; McElreath, Richard

2016-04-05

Socially imposed monogamy in humans is an evolutionary puzzle because it requires costly punishment by those who impose the norm. Moreover, most societies were--and are--polygynous; yet many larger human societies transitioned from polygyny to socially imposed monogamy beginning with the advent of agriculture and larger residential groups. We use a simulation model to explore how interactions between group size, sexually transmitted infection (STI) dynamics and social norms can explain the timing and emergence of socially imposed monogamy. Polygyny dominates when groups are too small to sustain STIs. However, in larger groups, STIs become endemic (especially in concurrent polygynist networks) and have an impact on fertility, thereby mediating multilevel selection. Punishment of polygynists improves monogamist fitness within groups by reducing their STI exposure, and between groups by enabling punishing monogamist groups to outcompete polygynists. This suggests pathways for the emergence of socially imposed monogamy, and enriches our understanding of costly punishment evolution.

Disease dynamics and costly punishment can foster socially imposed monogamy

PubMed Central

Bauch, Chris T.; McElreath, Richard

2016-01-01

Socially imposed monogamy in humans is an evolutionary puzzle because it requires costly punishment by those who impose the norm. Moreover, most societies were—and are—polygynous; yet many larger human societies transitioned from polygyny to socially imposed monogamy beginning with the advent of agriculture and larger residential groups. We use a simulation model to explore how interactions between group size, sexually transmitted infection (STI) dynamics and social norms can explain the timing and emergence of socially imposed monogamy. Polygyny dominates when groups are too small to sustain STIs. However, in larger groups, STIs become endemic (especially in concurrent polygynist networks) and have an impact on fertility, thereby mediating multilevel selection. Punishment of polygynists improves monogamist fitness within groups by reducing their STI exposure, and between groups by enabling punishing monogamist groups to outcompete polygynists. This suggests pathways for the emergence of socially imposed monogamy, and enriches our understanding of costly punishment evolution. PMID:27044573

Multilevel processes and cultural adaptation: Examples from past and present small-scale societies.

PubMed

Reyes-García, V; Balbo, A L; Gomez-Baggethun, E; Gueze, M; Mesoudi, A; Richerson, P; Rubio-Campillo, X; Ruiz-Mallén, I; Shennan, S

2016-12-01

Cultural adaptation has become central in the context of accelerated global change with authors increasingly acknowledging the importance of understanding multilevel processes that operate as adaptation takes place. We explore the importance of multilevel processes in explaining cultural adaptation by describing how processes leading to cultural (mis)adaptation are linked through a complex nested hierarchy, where the lower levels combine into new units with new organizations, functions, and emergent properties or collective behaviours. After a brief review of the concept of "cultural adaptation" from the perspective of cultural evolutionary theory and resilience theory, the core of the paper is constructed around the exploration of multilevel processes occurring at the temporal, spatial, social and political scales. We do so by examining small-scale societies' case studies. In each section, we discuss the importance of the selected scale for understanding cultural adaptation and then present an example that illustrates how multilevel processes in the selected scale help explain observed patterns in the cultural adaptive process. We end the paper discussing the potential of modelling and computer simulation for studying multilevel processes in cultural adaptation.

Multilevel processes and cultural adaptation: Examples from past and present small-scale societies

PubMed Central

Reyes-García, V.; Balbo, A. L.; Gomez-Baggethun, E.; Gueze, M.; Mesoudi, A.; Richerson, P.; Rubio-Campillo, X.; Ruiz-Mallén, I.; Shennan, S.

2016-01-01

Cultural adaptation has become central in the context of accelerated global change with authors increasingly acknowledging the importance of understanding multilevel processes that operate as adaptation takes place. We explore the importance of multilevel processes in explaining cultural adaptation by describing how processes leading to cultural (mis)adaptation are linked through a complex nested hierarchy, where the lower levels combine into new units with new organizations, functions, and emergent properties or collective behaviours. After a brief review of the concept of “cultural adaptation” from the perspective of cultural evolutionary theory and resilience theory, the core of the paper is constructed around the exploration of multilevel processes occurring at the temporal, spatial, social and political scales. We do so by examining small-scale societies’ case studies. In each section, we discuss the importance of the selected scale for understanding cultural adaptation and then present an example that illustrates how multilevel processes in the selected scale help explain observed patterns in the cultural adaptive process. We end the paper discussing the potential of modelling and computer simulation for studying multilevel processes in cultural adaptation. PMID:27774109

Using Multilevel Modeling in Language Assessment Research: A Conceptual Introduction

ERIC Educational Resources Information Center

Barkaoui, Khaled

2013-01-01

This article critiques traditional single-level statistical approaches (e.g., multiple regression analysis) to examining relationships between language test scores and variables in the assessment setting. It highlights the conceptual, methodological, and statistical problems associated with these techniques in dealing with multilevel or nested…

Describing differences in weight and length growth trajectories between white and Pakistani infants in the UK: analysis of the Born in Bradford birth cohort study using multilevel linear spline models.

PubMed

Fairley, Lesley; Petherick, Emily S; Howe, Laura D; Tilling, Kate; Cameron, Noel; Lawlor, Debbie A; West, Jane; Wright, John

2013-04-01

To describe the growth pattern from birth to 2 years of UK-born white British and Pakistani infants. Birth cohort. Bradford, UK. 314 white British boys, 383 Pakistani boys, 328 white British girls and 409 Pakistani girls. Weight and length trajectories based on repeat measurements from birth to 2 years. Linear spline multilevel models for weight and length with knot points at 4 and 9 months fitted the data well. At birth Pakistani boys were 210 g lighter (95% CI -290 to -120) and 0.5 cm shorter (-1.04 to 0.02) and Pakistani girls were 180 g lighter (-260 to -100) and 0.5 cm shorter (-0.91 to -0.03) than white British boys and girls, respectively. Pakistani infants gained length faster than white British infants between 0 and 4 months (+0.3 cm/month (0.1 to 0.5) for boys and +0.4 cm/month (0.2 to 0.6) for girls) and gained more weight per month between 9 and 24 months (+10 g/month (0 to 30) for boys and +30 g/month (20 to 40) for girls). Adjustment for maternal height attenuated ethnic differences in weight and length at birth, but not in postnatal growth. Adjustment for other confounders did not explain differences in any outcomes. Pakistani infants were lighter and had shorter predicted mean length at birth than white British infants, but gained weight and length quicker in infancy. By age 2 years both ethnic groups had similar weight, but Pakistani infants were on average taller than white British infants.

Population-based cohort study of variation in the use of emergency cholecystectomy for benign gallbladder diseases.

PubMed

2016-11-01

The aims of this prospective population-based cohort study were to identify the patient and hospital characteristics associated with emergency cholecystectomy, and the influences of these in determining variations between hospitals. Data were collected for consecutive patients undergoing cholecystectomy in acute UK and Irish hospitals between 1 March and 1 May 2014. Potential explanatory variables influencing the performance of emergency cholecystectomy were analysed by means of multilevel, multivariable logistic regression modelling using a two-level hierarchical structure with patients (level 1) nested within hospitals (level 2). Data were collected on 4744 cholecystectomies from 165 hospitals. Increasing age, lower ASA fitness grade, biliary colic, the need for further imaging (magnetic retrograde cholangiopancreatography), endoscopic interventions (endoscopic retrograde cholangiopancreatography) and admission to a non-biliary centre significantly reduced the likelihood of an emergency cholecystectomy being performed. The multilevel model was used to calculate the probability of receiving an emergency cholecystectomy for a woman aged 40 years or over with an ASA grade of I or II and a BMI of at least 25·0 kg/m 2 , who presented with acute cholecystitis with an ultrasound scan showing a thick-walled gallbladder and a normal common bile duct. The mean predicted probability of receiving an emergency cholecystectomy was 0·52 (95 per cent c.i. 0·45 to 0·57). The predicted probabilities ranged from 0·02 to 0·95 across the 165 hospitals, demonstrating significant variation between hospitals. Patients with similar characteristics presenting to different hospitals with acute gallbladder pathology do not receive comparable care. © 2016 BJS Society Ltd Published by John Wiley & Sons Ltd.

Width of the confining string in Yang-Mills theory.

PubMed

Gliozzi, F; Pepe, M; Wiese, U-J

2010-06-11

We investigate the transverse fluctuations of the confining string connecting two static quarks in (2+1)D SU(2) Yang-Mills theory using Monte Carlo calculations. The exponentially suppressed signal is extracted from the large noise by a very efficient multilevel algorithm. The resulting width of the string increases logarithmically with the distance between the static quark charges. Corrections at intermediate distances due to universal higher-order terms in the effective string action are calculated analytically. They accurately fit the numerical data.

Multilevel Analysis Methods for Partially Nested Cluster Randomized Trials

ERIC Educational Resources Information Center

Sanders, Elizabeth A.

2011-01-01

This paper explores multilevel modeling approaches for 2-group randomized experiments in which a treatment condition involving clusters of individuals is compared to a control condition involving only ungrouped individuals, otherwise known as partially nested cluster randomized designs (PNCRTs). Strategies for comparing groups from a PNCRT in the…

Daily Stressors in School-Age Children: A Multilevel Approach

ERIC Educational Resources Information Center

Escobar, Milagros; Alarcón, Rafael; Blanca, María J.; Fernández-Baena, F. Javier; Rosel, Jesús F.; Trianes, María Victoria

2013-01-01

This study uses hierarchical or multilevel modeling to identify variables that contribute to daily stressors in a population of schoolchildren. Four hierarchical levels with several predictive variables were considered: student (age, sex, social adaptation of the student, number of life events and chronic stressors experienced, and educational…

Commitment to the Study of International Business and Cultural Intelligence: A Multilevel Model

ERIC Educational Resources Information Center

Ramsey, Jase R.; Barakat, Livia L.; Aad, Amine Abi

2014-01-01

Adopting a multilevel theoretical framework, we examined how metacognitive and motivational cultural intelligence influence an individual's commitment to the study of international business (IB). Data from 292 undergraduate and graduate business students nested in 12 U.S. business school classes demonstrated that individuals' metacognitive and…

Will they stay fit and healthy? A three-year follow-up evaluation of a physical activity and health intervention in Polish youth.

PubMed

Bronikowski, Michal; Bronikowska, Malgorzata

2011-11-01

In this paper we evaluate the sustainability of changes of involvement in physical activity. The paper examines the effectiveness of a model aiming at influencing the frequency of leisure time physical activity, physical fitness and body constituency in youth. The baseline of this study was a randomly selected sample of 13 year olds who participated in an intervention programme carried out in three schools in Poznan in 2005-08. From a total of 199 adolescent boys a subsample of 38 individuals from the experimental group and 34 from the control group were followed for 15 months after the interventional programme finished. From 170 girls, a subsample of 33 from the experimental group and 32 girls from the control group were also randomly selected for the follow-up study. Among the variables monitored were: physical fitness, body constituency, and frequency of leisure time physical activity. All the variables were monitored in pre-test, post-test and follow-up examinations. It was established that 15 months after the end of the interventional programme boys and girls from the intervention groups maintained a higher level of leisure time physical activity than their control group peers, and similarly in the case of selected health-related components of physical fitness. No distinctive differences were found in the case of body constituency, though, apart from muscle mass and the sum of skinfolds in girls. The study exposed an increase in leisure time physical activity in time and a positive influence on selected components of health-related variables. The findings confirm the effectiveness of a multi-level intervention programme involving self-determined out-of-school physical activity planning for school-age youths, indicating the importance of personal and social context.

Relationships between community social capital and injury in Canadian adolescents: a multilevel analysis

PubMed Central

Vafaei, Afshin; Pickett, William; Alvarado, Beatriz E

2015-01-01

Background Characteristics of social environments are potential risk factors for adolescent injury. Impacts of social capital on the occurrence of such injuries have rarely been explored. Methods General health questionnaires were completed by 8910 youth aged 14 years and older as part of the 2010 Canadian Health Behaviour in School-Aged Children study. These were supplemented with community-level data from the 2006 Canada Census of Population. Multilevel logistic regression models with random intercepts were fit to examine associations of interest. The reliability and validity of variables used in this analysis had been established in past studies, or in new analyses that employed factor analysis. Results Between school differences explained 2% of the variance in the occurrence of injuries. After adjustment for all confounders, community social capital did not have any impact on the occurrence of injuries in boys: OR: 1.01, 95% CI 0.80 to 1.29. However, living in areas with low social capital was associated with lower occurrence of injuries in girls (OR 0.78, 95% CI 0.63 to 0.96). Other factors that were significantly related to injuries in both genders were younger age, engagement in more risky behaviours, and negative behavioural influences from peers. Conclusions After simultaneously taking into account the influence of community-level and individual-level factors, community levels of social capital remained a relatively strong predictor of injury among girls but not boys. Such gender effects provide important clues into the social aetiology of youth injury. PMID:26294708

Associations Between Peer Network Gender Norms and the Perpetration of Intimate Partner Violence Among Urban Tanzanian Men: a Multilevel Analysis.

PubMed

Mulawa, Marta I; Reyes, H Luz McNaughton; Foshee, Vangie A; Halpern, Carolyn T; Martin, Sandra L; Kajula, Lusajo J; Maman, Suzanne

2018-05-01

Male perpetration of intimate partner violence (IPV) against women in sub-Saharan Africa is widespread. Theory and empirical evidence suggest peer networks may play an important role in shaping IPV perpetration, though research on this topic in the region is limited. We assessed the degree to which peer network gender norms are associated with Tanzanian men's perpetration of IPV and examined whether the social cohesion of peer networks moderates this relationship. Using baseline data from sexually active men (n = 1103) nested within 59 peer networks enrolled in an on-going cluster-randomized HIV and IPV prevention trial, we fit multilevel logistic regression models to examine peer network-level factors associated with past-year physical IPV perpetration. Peer network gender norms were significantly associated with men's risk of perpetrating IPV, even after adjusting for their own attitudes toward gender roles (OR = 1.53 , p =  . 04). Peer network social cohesion moderated this relationship (OR = 1.50 , p =  . 04); the positive relationship between increasingly inequitable (i.e., traditional) peer network gender norms and men's risk of perpetrating IPV became stronger, as peer network social cohesion increased. Characteristics of the peer network context are associated with men's IPV perpetration and should be targeted in future interventions. While many IPV prevention interventions focus on changing individual attitudes, our findings support a unique approach, focused on transforming the peer context.

Deconvolution of mixing time series on a graph

PubMed Central

Blocker, Alexander W.; Airoldi, Edoardo M.

2013-01-01

In many applications we are interested in making inference on latent time series from indirect measurements, which are often low-dimensional projections resulting from mixing or aggregation. Positron emission tomography, super-resolution, and network traffic monitoring are some examples. Inference in such settings requires solving a sequence of ill-posed inverse problems, yt = Axt, where the projection mechanism provides information on A. We consider problems in which A specifies mixing on a graph of times series that are bursty and sparse. We develop a multilevel state-space model for mixing times series and an efficient approach to inference. A simple model is used to calibrate regularization parameters that lead to efficient inference in the multilevel state-space model. We apply this method to the problem of estimating point-to-point traffic flows on a network from aggregate measurements. Our solution outperforms existing methods for this problem, and our two-stage approach suggests an efficient inference strategy for multilevel models of multivariate time series. PMID:25309135

Multivariate Longitudinal Analysis with Bivariate Correlation Test.

PubMed

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated.

A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking

PubMed Central

Gilbert, David

2016-01-01

Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems. PMID:27187178

A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking.

PubMed

Pârvu, Ovidiu; Gilbert, David

2016-01-01

Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems.

Multi-level manual and autonomous control superposition for intelligent telerobot

NASA Technical Reports Server (NTRS)

Hirai, Shigeoki; Sato, T.

1989-01-01

Space telerobots are recognized to require cooperation with human operators in various ways. Multi-level manual and autonomous control superposition in telerobot task execution is described. The object model, the structured master-slave manipulation system, and the motion understanding system are proposed to realize the concept. The object model offers interfaces for task level and object level human intervention. The structured master-slave manipulation system offers interfaces for motion level human intervention. The motion understanding system maintains the consistency of the knowledge through all the levels which supports the robot autonomy while accepting the human intervention. The superposing execution of the teleoperational task at multi-levels realizes intuitive and robust task execution for wide variety of objects and in changeful environment. The performance of several examples of operating chemical apparatuses is shown.

Extending the Multi-level Method for the Simulation of Stochastic Biological Systems.

PubMed

Lester, Christopher; Baker, Ruth E; Giles, Michael B; Yates, Christian A

2016-08-01

The multi-level method for discrete-state systems, first introduced by Anderson and Higham (SIAM Multiscale Model Simul 10(1):146-179, 2012), is a highly efficient simulation technique that can be used to elucidate statistical characteristics of biochemical reaction networks. A single point estimator is produced in a cost-effective manner by combining a number of estimators of differing accuracy in a telescoping sum, and, as such, the method has the potential to revolutionise the field of stochastic simulation. In this paper, we present several refinements of the multi-level method which render it easier to understand and implement, and also more efficient. Given the substantial and complex nature of the multi-level method, the first part of this work reviews existing literature, with the aim of providing a practical guide to the use of the multi-level method. The second part provides the means for a deft implementation of the technique and concludes with a discussion of a number of open problems.

Multi-level Hierarchical Poly Tree computer architectures

NASA Technical Reports Server (NTRS)

Padovan, Joe; Gute, Doug

1990-01-01

Based on the concept of hierarchical substructuring, this paper develops an optimal multi-level Hierarchical Poly Tree (HPT) parallel computer architecture scheme which is applicable to the solution of finite element and difference simulations. Emphasis is given to minimizing computational effort, in-core/out-of-core memory requirements, and the data transfer between processors. In addition, a simplified communications network that reduces the number of I/O channels between processors is presented. HPT configurations that yield optimal superlinearities are also demonstrated. Moreover, to generalize the scope of applicability, special attention is given to developing: (1) multi-level reduction trees which provide an orderly/optimal procedure by which model densification/simplification can be achieved, as well as (2) methodologies enabling processor grading that yields architectures with varying types of multi-level granularity.

Familial and Religious Influences on Adolescent Alcohol Use: A Multi-Level Study of Students and School Communities

ERIC Educational Resources Information Center

Bjarnason, Thoroddur; Thorlindsson, Thorolfur; Sigfusdottir, Inga D.; Welch, Michael R.

2005-01-01

A multi-level Durkheimian theory of familial and religious influences on adolescent alcohol use is developed and tested with hierarchical linear modeling of data from Icelandic schools and students. On the individual level, traditional family structure, parental monitoring, parental support, religious participation, and perceptions of divine…

Help Seeking in Online Collaborative Groupwork: A Multilevel Analysis

ERIC Educational Resources Information Center

Du, Jianxia; Xu, Jianzhong; Fan, Xitao

2015-01-01

This study examined predictive models for students' help seeking in the context of online collaborative groupwork. Results from multilevel analysis revealed that most of the variance in help seeking was at the individual student level, and multiple variables at the individual level were predictive of help-seeking behaviour. Help seeking was…

A Multi-Level Examination of College and Its Influence on Ecumenical Worldview Development

ERIC Educational Resources Information Center

Mayhew, Matthew J.

2012-01-01

This multi-level, longitudinal study investigated the ecumenical worldview development of 13,932 students enrolled in one of 126 institutions. Results indicated that the final hierarchical linear model, consisting of institution-and-student-level predictors as well as slopes explaining the relationships among some of these predictors, explained…

Managing Money in Marriage: Multilevel and Cross-National Effects of the Breadwinner Role

ERIC Educational Resources Information Center

Yodanis, Carrie; Lauer, Sean

2007-01-01

We examine whether institutionalized practices and beliefs regarding breadwinning roles are associated with the choice of more or less equal money management strategies in marriage. Using cross-national data from 21 country contexts in the International Social Survey Programme and multilevel modeling, we find that in contexts of shared…

A Multilevel Evaluation of a Comprehensive Child Abuse Prevention Program

ERIC Educational Resources Information Center

Lawson, Michael A.; Alameda-Lawson, Tania; Byrnes, Edward C.

2012-01-01

Objectives: The purpose of this study is to examine the extent to which participation in a county-wide prevention program leads to improvements in protective factors associated with child abuse prevention (CAP) and whether improvements in measured protective factors relate to decreased odds of child abuse. Method: Using multilevel growth modeling,…

Multilevel Exploration of Factors Contributing to the Overrepresentation of Black Students in Office Disciplinary Referrals

ERIC Educational Resources Information Center

Bradshaw, Catherine P.; Mitchell, Mary M.; O'Brennan, Lindsey M.; Leaf, Philip J.

2010-01-01

Although there is increasing awareness of the overrepresentation of ethic minority students--particularly Black students--in disciplinary actions, the extant research has rarely empirically examined potential factors that may contribute to these disparities. The current study used a multilevel modeling approach to examine factors at the child…

Multiple Imputation of Multilevel Missing Data-Rigor versus Simplicity

ERIC Educational Resources Information Center

Drechsler, Jörg

2015-01-01

Multiple imputation is widely accepted as the method of choice to address item-nonresponse in surveys. However, research on imputation strategies for the hierarchical structures that are typically found in the data in educational contexts is still limited. While a multilevel imputation model should be preferred from a theoretical point of view if…

"Using Power Tables to Compute Statistical Power in Multilevel Experimental Designs"

ERIC Educational Resources Information Center

Konstantopoulos, Spyros

2009-01-01

Power computations for one-level experimental designs that assume simple random samples are greatly facilitated by power tables such as those presented in Cohen's book about statistical power analysis. However, in education and the social sciences experimental designs have naturally nested structures and multilevel models are needed to compute the…

Pre- and Postnatal Women's Leisure Time Physical Activity Patterns: A Multilevel Longitudinal Analysis

ERIC Educational Resources Information Center

Cramp, Anita G.; Bray, Steven R.

2009-01-01

The purpose of this study was to examine women's leisure time physical activity (LTPA) before pregnancy, during pregnancy, and through the first 7 months postnatal. Pre- and postnatal women (n = 309) completed the 12-month Modifiable Activity Questionnaire and demographic information. Multilevel modeling was used to estimate a growth curve…

Does the Organization Matter? A Multilevel Analysis of Organizational Effects in Homeless Service Innovations

ERIC Educational Resources Information Center

Cronley, Courtney; Patterson, David A.

2012-01-01

This study examined the effects of organizational culture on staff members' use of management information systems ("N" = 142) within homeless service organizations ("N" = 24), using a multilevel model. The Organizational Social Context Questionnaire was used to measure organizational culture, defined by three sub-constructs: (1) proficiency, (2)…

Identifying Synergies in Multilevel Interventions: The Convergence Strategy

ERIC Educational Resources Information Center

Lewis, Megan A.; Fitzgerald, Tania M.; Zulkiewicz, Brittany; Peinado, Susana; Williams, Pamela A.

2017-01-01

Social ecological models of health often describe multiple levels of influence that interact to influence health. However, it is still common for interventions to target only one or two of these levels, perhaps owing in part to a lack of guidance on how to design multilevel interventions to achieve optimal impact. The convergence strategy…

Assessing variance components in multilevel linear models using approximate Bayes factors: A case study of ethnic disparities in birthweight

PubMed Central

Saville, Benjamin R.; Herring, Amy H.; Kaufman, Jay S.

2013-01-01

Racial/ethnic disparities in birthweight are a large source of differential morbidity and mortality worldwide and have remained largely unexplained in epidemiologic models. We assess the impact of maternal ancestry and census tract residence on infant birth weights in New York City and the modifying effects of race and nativity by incorporating random effects in a multilevel linear model. Evaluating the significance of these predictors involves the test of whether the variances of the random effects are equal to zero. This is problematic because the null hypothesis lies on the boundary of the parameter space. We generalize an approach for assessing random effects in the two-level linear model to a broader class of multilevel linear models by scaling the random effects to the residual variance and introducing parameters that control the relative contribution of the random effects. After integrating over the random effects and variance components, the resulting integrals needed to calculate the Bayes factor can be efficiently approximated with Laplace’s method. PMID:24082430

Measures of clustering and heterogeneity in multilevel Poisson regression analyses of rates/count data

PubMed Central

Austin, Peter C.; Stryhn, Henrik; Leckie, George; Merlo, Juan

2017-01-01

Multilevel data occur frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models. These models incorporate cluster‐specific random effects that allow one to partition the total variation in the outcome into between‐cluster variation and between‐individual variation. The magnitude of the effect of clustering provides a measure of the general contextual effect. When outcomes are binary or time‐to‐event in nature, the general contextual effect can be quantified by measures of heterogeneity like the median odds ratio or the median hazard ratio, respectively, which can be calculated from a multilevel regression model. Outcomes that are integer counts denoting the number of times that an event occurred are common in epidemiological and medical research. The median (incidence) rate ratio in multilevel Poisson regression for counts that corresponds to the median odds ratio or median hazard ratio for binary or time‐to‐event outcomes respectively is relatively unknown and is rarely used. The median rate ratio is the median relative change in the rate of the occurrence of the event when comparing identical subjects from 2 randomly selected different clusters that are ordered by rate. We also describe how the variance partition coefficient, which denotes the proportion of the variation in the outcome that is attributable to between‐cluster differences, can be computed with count outcomes. We illustrate the application and interpretation of these measures in a case study analyzing the rate of hospital readmission in patients discharged from hospital with a diagnosis of heart failure. PMID:29114926

Multilevel Optimization Framework for Hierarchical Stiffened Shells Accelerated by Adaptive Equivalent Strategy

NASA Astrophysics Data System (ADS)

Wang, Bo; Tian, Kuo; Zhao, Haixin; Hao, Peng; Zhu, Tianyu; Zhang, Ke; Ma, Yunlong

2017-06-01

In order to improve the post-buckling optimization efficiency of hierarchical stiffened shells, a multilevel optimization framework accelerated by adaptive equivalent strategy is presented in this paper. Firstly, the Numerical-based Smeared Stiffener Method (NSSM) for hierarchical stiffened shells is derived by means of the numerical implementation of asymptotic homogenization (NIAH) method. Based on the NSSM, a reasonable adaptive equivalent strategy for hierarchical stiffened shells is developed from the concept of hierarchy reduction. Its core idea is to self-adaptively decide which hierarchy of the structure should be equivalent according to the critical buckling mode rapidly predicted by NSSM. Compared with the detailed model, the high prediction accuracy and efficiency of the proposed model is highlighted. On the basis of this adaptive equivalent model, a multilevel optimization framework is then established by decomposing the complex entire optimization process into major-stiffener-level and minor-stiffener-level sub-optimizations, during which Fixed Point Iteration (FPI) is employed to accelerate convergence. Finally, the illustrative examples of the multilevel framework is carried out to demonstrate its efficiency and effectiveness to search for the global optimum result by contrast with the single-level optimization method. Remarkably, the high efficiency and flexibility of the adaptive equivalent strategy is indicated by compared with the single equivalent strategy.

Contextual effects and cancer outcomes in the United States: a systematic review of characteristics in multilevel analyses.

PubMed

Zahnd, Whitney E; McLafferty, Sara L

2017-11-01

There is increasing call for the utilization of multilevel modeling to explore the relationship between place-based contextual effects and cancer outcomes in the United States. To gain a better understanding of how contextual factors are being considered, we performed a systematic review. We reviewed studies published between January 1, 2002 and December 31, 2016 and assessed the following attributes: (1) contextual considerations such as geographic scale and contextual factors used; (2) methods used to quantify contextual factors; and (3) cancer type and outcomes. We searched PubMed, Scopus, and Web of Science and initially identified 1060 studies. One hundred twenty-two studies remained after exclusions. Most studies utilized a two-level structure; census tracts were the most commonly used geographic scale. Socioeconomic factors, health care access, racial/ethnic factors, and rural-urban status were the most common contextual factors addressed in multilevel models. Breast and colorectal cancers were the most common cancer types, and screening and staging were the most common outcomes assessed in these studies. Opportunities for future research include deriving contextual factors using more rigorous approaches, considering cross-classified structures and cross-level interactions, and using multilevel modeling to explore understudied cancers and outcomes. Copyright © 2017 Elsevier Inc. All rights reserved.

To center or not to center? Investigating inertia with a multilevel autoregressive model.

PubMed

Hamaker, Ellen L; Grasman, Raoul P P P

2014-01-01

Whether level 1 predictors should be centered per cluster has received considerable attention in the multilevel literature. While most agree that there is no one preferred approach, it has also been argued that cluster mean centering is desirable when the within-cluster slope and the between-cluster slope are expected to deviate, and the main interest is in the within-cluster slope. However, we show in a series of simulations that if one has a multilevel autoregressive model in which the level 1 predictor is the lagged outcome variable (i.e., the outcome variable at the previous occasion), cluster mean centering will in general lead to a downward bias in the parameter estimate of the within-cluster slope (i.e., the autoregressive relationship). This is particularly relevant if the main question is whether there is on average an autoregressive effect. Nonetheless, we show that if the main interest is in estimating the effect of a level 2 predictor on the autoregressive parameter (i.e., a cross-level interaction), cluster mean centering should be preferred over other forms of centering. Hence, researchers should be clear on what is considered the main goal of their study, and base their choice of centering method on this when using a multilevel autoregressive model.

To center or not to center? Investigating inertia with a multilevel autoregressive model

PubMed Central

Hamaker, Ellen L.; Grasman, Raoul P. P. P.

2015-01-01

Whether level 1 predictors should be centered per cluster has received considerable attention in the multilevel literature. While most agree that there is no one preferred approach, it has also been argued that cluster mean centering is desirable when the within-cluster slope and the between-cluster slope are expected to deviate, and the main interest is in the within-cluster slope. However, we show in a series of simulations that if one has a multilevel autoregressive model in which the level 1 predictor is the lagged outcome variable (i.e., the outcome variable at the previous occasion), cluster mean centering will in general lead to a downward bias in the parameter estimate of the within-cluster slope (i.e., the autoregressive relationship). This is particularly relevant if the main question is whether there is on average an autoregressive effect. Nonetheless, we show that if the main interest is in estimating the effect of a level 2 predictor on the autoregressive parameter (i.e., a cross-level interaction), cluster mean centering should be preferred over other forms of centering. Hence, researchers should be clear on what is considered the main goal of their study, and base their choice of centering method on this when using a multilevel autoregressive model. PMID:25688215

A multilevel analysis of trimethoprim and ciprofloxacin prescribing and resistance of uropathogenic Escherichia coli in general practice.

PubMed

Vellinga, Akke; Murphy, Andrew W; Hanahoe, Belinda; Bennett, Kathleen; Cormican, Martin

2010-07-01

A retrospective analysis of databases was performed to describe trimethoprim and ciprofloxacin prescribing and resistance in Escherichia coli within general practices in the West of Ireland from 2004 to 2008. Antimicrobial susceptibility testing was performed by disc diffusion methods according to the CLSI methods and criteria on significant E. coli isolates (colony count >10(5) cfu/mL) from urine samples submitted from general practice. Data were collected over a 4.5 year period and aggregated at practice level. Data on antimicrobial prescribing of practices were obtained from the national Irish prescribing database, which accounts for approximately 70% of all medicines prescribed in primary care. A multilevel model (MLwiN) was fitted with trimethoprim/ciprofloxacin resistance rates as outcome and practice prescribing as predictor. Practice and individual routinely collected variables were controlled for in the model. Seventy-two general practices sent between 13 and 720 (median 155) samples that turned out to be E. coli positive. Prescribing at practice level was significantly correlated with the probability of antimicrobial-resistant E. coli with an odds ratio of 1.02 [95% confidence interval (CI) 1.01-1.04] for every additional prescription of trimethoprim per 1000 patients per month in the practice and 1.08 (1.04-1.11) for ciprofloxacin. Age was a significant risk factor in both models. Higher variation between practices was found for ciprofloxacin as well as a yearly increase in resistance. Comparing a 'mean' practice with 1 prescription per month with one with 10 prescriptions per month showed an increase in predicted probability of a resistant E. coli for the 'mean' patient from 23.9% to 27.5% for trimethoprim and from 3.0% to 5.5% for ciprofloxacin. A higher level of antimicrobial prescribing in a practice is associated with a higher probability of a resistant E. coli for the patient. The variation in antimicrobial resistance levels between practices was relatively higher for ciprofloxacin than for trimethoprim.

The seasonal influence of climate and environment on yellow fever transmission across Africa.

PubMed

Hamlet, Arran; Jean, Kévin; Perea, William; Yactayo, Sergio; Biey, Joseph; Van Kerkhove, Maria; Ferguson, Neil; Garske, Tini

2018-03-01

Yellow fever virus (YFV) is a vector-borne flavivirus endemic to Africa and Latin America. Ninety per cent of the global burden occurs in Africa where it is primarily transmitted by Aedes spp, with Aedes aegypti the main vector for urban yellow fever (YF). Mosquito life cycle and viral replication in the mosquito are heavily dependent on climate, particularly temperature and rainfall. We aimed to assess whether seasonal variations in climatic factors are associated with the seasonality of YF reports. We constructed a temperature suitability index for YFV transmission, capturing the temperature dependence of mosquito behaviour and viral replication within the mosquito. We then fitted a series of multilevel logistic regression models to a dataset of YF reports across Africa, considering location and seasonality of occurrence for seasonal models, against the temperature suitability index, rainfall and the Enhanced Vegetation Index (EVI) as covariates alongside further demographic indicators. Model fit was assessed by the Area Under the Curve (AUC), and models were ranked by Akaike's Information Criterion which was used to weight model outputs to create combined model predictions. The seasonal model accurately captured both the geographic and temporal heterogeneities in YF transmission (AUC = 0.81), and did not perform significantly worse than the annual model which only captured the geographic distribution. The interaction between temperature suitability and rainfall accounted for much of the occurrence of YF, which offers a statistical explanation for the spatio-temporal variability in transmission. The description of seasonality offers an explanation for heterogeneities in the West-East YF burden across Africa. Annual climatic variables may indicate a transmission suitability not always reflected in seasonal interactions. This finding, in conjunction with forecasted data, could highlight areas of increased transmission and provide insights into the occurrence of large outbreaks, such as those seen in Angola, the Democratic Republic of the Congo and Brazil.

The seasonal influence of climate and environment on yellow fever transmission across Africa

PubMed Central

Hamlet, Arran; Perea, William; Yactayo, Sergio; Biey, Joseph; Van Kerkhove, Maria; Ferguson, Neil

2018-01-01

Background Yellow fever virus (YFV) is a vector-borne flavivirus endemic to Africa and Latin America. Ninety per cent of the global burden occurs in Africa where it is primarily transmitted by Aedes spp, with Aedes aegypti the main vector for urban yellow fever (YF). Mosquito life cycle and viral replication in the mosquito are heavily dependent on climate, particularly temperature and rainfall. We aimed to assess whether seasonal variations in climatic factors are associated with the seasonality of YF reports. Methodology/Principal findings We constructed a temperature suitability index for YFV transmission, capturing the temperature dependence of mosquito behaviour and viral replication within the mosquito. We then fitted a series of multilevel logistic regression models to a dataset of YF reports across Africa, considering location and seasonality of occurrence for seasonal models, against the temperature suitability index, rainfall and the Enhanced Vegetation Index (EVI) as covariates alongside further demographic indicators. Model fit was assessed by the Area Under the Curve (AUC), and models were ranked by Akaike’s Information Criterion which was used to weight model outputs to create combined model predictions. The seasonal model accurately captured both the geographic and temporal heterogeneities in YF transmission (AUC = 0.81), and did not perform significantly worse than the annual model which only captured the geographic distribution. The interaction between temperature suitability and rainfall accounted for much of the occurrence of YF, which offers a statistical explanation for the spatio-temporal variability in transmission. Conclusions/Significance The description of seasonality offers an explanation for heterogeneities in the West-East YF burden across Africa. Annual climatic variables may indicate a transmission suitability not always reflected in seasonal interactions. This finding, in conjunction with forecasted data, could highlight areas of increased transmission and provide insights into the occurrence of large outbreaks, such as those seen in Angola, the Democratic Republic of the Congo and Brazil. PMID:29543798

Exploring the impact of different multi-level measures of physician communities in patient-centric care networks on healthcare outcomes: A multi-level regression approach.

PubMed

Uddin, Shahadat

2016-02-04

A patient-centric care network can be defined as a network among a group of healthcare professionals who provide treatments to common patients. Various multi-level attributes of the members of this network have substantial influence to its perceived level of performance. In order to assess the impact different multi-level attributes of patient-centric care networks on healthcare outcomes, this study first captured patient-centric care networks for 85 hospitals using health insurance claim dataset. From these networks, this study then constructed physician collaboration networks based on the concept of patient-sharing network among physicians. A multi-level regression model was then developed to explore the impact of different attributes that are organised at two levels on hospitalisation cost and hospital length of stay. For Level-1 model, the average visit per physician significantly predicted both hospitalisation cost and hospital length of stay. The number of different physicians significantly predicted only the hospitalisation cost, which has significantly been moderated by age, gender and Comorbidity score of patients. All Level-1 findings showed significance variance across physician collaboration networks having different community structure and density. These findings could be utilised as a reflective measure by healthcare decision makers. Moreover, healthcare managers could consider them in developing effective healthcare environments.

mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification.

PubMed

Razzaq, Muhammad Asif; Villalonga, Claudia; Lee, Sungyoung; Akhtar, Usman; Ali, Maqbool; Kim, Eun-Soo; Khattak, Asad Masood; Seung, Hyonwoo; Hur, Taeho; Bang, Jaehun; Kim, Dohyeong; Ali Khan, Wajahat

2017-10-24

The emerging research on automatic identification of user's contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user's contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demonstrates extension of our previous work from a single domain (i.e., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness. We propose multi-level Context-aware Framework (mlCAF), which fuses the multi-level cross-domain contexts in order to arbitrate richer behavioral contexts. This work explicitly focuses on key challenges linked to multi-level context modeling, reasoning and fusioning based on the mlCAF open-source ontology. More specifically, it addresses the interpretation of contexts from three different domains, their fusioning conforming to richer contextual information. This paper contributes in terms of ontology evolution with additional domains, context definitions, rules and inclusion of semantic queries. For the framework evaluation, multi-level cross-domain contexts collected from 20 users were used to ascertain abstract contexts, which served as basis for behavior modeling and lifestyle identification. The experimental results indicate a context recognition average accuracy of around 92.65% for the collected cross-domain contexts.

mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification

PubMed Central

Villalonga, Claudia; Lee, Sungyoung; Akhtar, Usman; Ali, Maqbool; Kim, Eun-Soo; Khattak, Asad Masood; Seung, Hyonwoo; Hur, Taeho; Kim, Dohyeong; Ali Khan, Wajahat

2017-01-01

The emerging research on automatic identification of user’s contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user’s contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demonstrates extension of our previous work from a single domain (i.e., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness. We propose multi-level Context-aware Framework (mlCAF), which fuses the multi-level cross-domain contexts in order to arbitrate richer behavioral contexts. This work explicitly focuses on key challenges linked to multi-level context modeling, reasoning and fusioning based on the mlCAF open-source ontology. More specifically, it addresses the interpretation of contexts from three different domains, their fusioning conforming to richer contextual information. This paper contributes in terms of ontology evolution with additional domains, context definitions, rules and inclusion of semantic queries. For the framework evaluation, multi-level cross-domain contexts collected from 20 users were used to ascertain abstract contexts, which served as basis for behavior modeling and lifestyle identification. The experimental results indicate a context recognition average accuracy of around 92.65% for the collected cross-domain contexts. PMID:29064459

Cell-phone vs microphone recordings: Judging emotion in the voice.

PubMed

Green, Joshua J; Eigsti, Inge-Marie

2017-09-01

Emotional states can be conveyed by vocal cues such as pitch and intensity. Despite the ubiquity of cellular telephones, there is limited information on how vocal emotional states are perceived during cell-phone transmissions. Emotional utterances (neutral, happy, angry) were elicited from two female talkers and simultaneously recorded via microphone and cell-phone. Ten-step continua (neutral to happy, neutral to angry) were generated using the straight algorithm. Analyses compared reaction time (RT) and emotion judgment as a function of recording type (microphone vs cell-phone). Logistic regression revealed no judgment differences between recording types, though there were interactions with emotion type. Multi-level model analyses indicated that RT data were best fit by a quadratic model, with slower RT at the middle of each continuum, suggesting greater ambiguity, and slower RT for cell-phone stimuli across blocks. While preliminary, results suggest that critical acoustic cues to emotion are largely retained in cell-phone transmissions, though with effects of recording source on RT, and support the methodological utility of collecting speech samples by phone.

Patterns of Yoga Practice and Physical Activity Following a Yoga Intervention for Adults With or at Risk for Type 2 Diabetes

PubMed Central

Alexander, Gina; Innes, Kim E.; Bourguignon, Cheryl; Bovbjerg, Viktor E.; Kulbok, Pamela; Taylor, Ann Gill

2012-01-01

Background The current study described patterns of yoga practice and examined differences in physical activity over time between individuals with or at risk for type 2 diabetes who completed an 8-week yoga intervention compared with controls. Methods A longitudinal comparative design measured the effect of a yoga intervention on yoga practice and physical activity, using data at baseline and postintervention months 3, 6, and 15. Results Disparate patterns of yoga practice occurred between intervention and control participants over time, but the subjective definition of yoga practice limits interpretation. Multilevel model estimates indicated that treatment group did not have a significant influence in the rate of change in physical activity over the study period. While age and education were not significant individual predictors, the inclusion of these variables in the model did improve fit. Conclusions Findings indicate that an 8-week yoga intervention had little effect on physical activity over time. Further research is necessary to explore the influence of yoga on behavioral health outcomes among individuals with or at risk for type 2 diabetes. PMID:22232506

Patterns of yoga practice and physical activity following a yoga intervention for adults with or at risk for type 2 diabetes.

PubMed

Alexander, Gina; Innes, Kim E; Bourguignon, Cheryl; Bovbjerg, Viktor E; Kulbok, Pamela; Taylor, Ann Gill

2012-01-01

The current study described patterns of yoga practice and examined differences in physical activity over time between individuals with or at risk for type 2 diabetes who completed an 8-week yoga intervention compared with controls. A longitudinal comparative design measured the effect of a yoga intervention on yoga practice and physical activity, using data at baseline and postintervention months 3, 6, and 15. Disparate patterns of yoga practice occurred between intervention and control participants over time, but the subjective definition of yoga practice limits interpretation. Multilevel model estimates indicated that treatment group did not have a significant influence in the rate of change in physical activity over the study period. While age and education were not significant individual predictors, the inclusion of these variables in the model did improve fit. Findings indicate that an 8-week yoga intervention had little effect on physical activity over time. Further research is necessary to explore the influence of yoga on behavioral health outcomes among individuals with or at risk for type 2 diabetes.

Macro-actor execution on multilevel data-driven architectures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gaudiot, J.L.; Najjar, W.

1988-12-31

The data-flow model of computation brings to multiprocessors high programmability at the expense of increased overhead. Applying the model at a higher level leads to better performance but also introduces loss of parallelism. We demonstrate here syntax directed program decomposition methods for the creation of large macro-actors in numerical algorithms. In order to alleviate some of the problems introduced by the lower resolution interpretation, we describe a multi-level of resolution and analyze the requirements for its actual hardware and software integration.

Multilevel Multidimensional Item Response Model with a Multilevel Latent Covariate

ERIC Educational Resources Information Center

Cho, Sun-Joo; Bottge, Brian A.

2015-01-01

In a pretest-posttest cluster-randomized trial, one of the methods commonly used to detect an intervention effect involves controlling pre-test scores and other related covariates while estimating an intervention effect at post-test. In many applications in education, the total post-test and pre-test scores that ignores measurement error in the…

A Multilevel Analysis of Japanese Middle School Student and School Socioeconomic Status Influence on Mathematics Achievement

ERIC Educational Resources Information Center

Takashiro, Naomi

2017-01-01

The author examined the simultaneous influence of Japanese middle school student and school socioeconomic status (SES) on student math achievement with two-level multilevel analysis models by utilizing the Trends in International Mathematics and Science Study (TIMSS) Japan data sets. The theoretical framework used in this study was…

When Cannabis Is Available and Visible at School--A Multilevel Analysis of Students' Cannabis Use

ERIC Educational Resources Information Center

Kuntsche, Emmanuel

2010-01-01

Aims: To investigate the links between the visibility of cannabis use in school (measured by teachers' reports of students being under the influence of cannabis on school premises), the proportion of cannabis users in the class, perceived availability of cannabis, as well as adolescent cannabis use. Methods: A multilevel regression model was…

A Multilevel Analysis of the Relationship between Shared Leadership and Creativity in Inter-Organizational Teams

ERIC Educational Resources Information Center

Gu, Jibao; Chen, Zhi; Huang, Qian; Liu, Hefu; Huang, Shenglan

2018-01-01

An inter-organizational team, which consists of diverse members from different organizations to conduct an initiative, has been widely treated as a critical method to improve organizational innovation. This study proposes a multilevel model to test the relationship between shared leadership and creativity at both team- and individual level in the…

Multilevel Dynamic Systems Affecting Introduction of HIV/STI Prevention Innovations among Chinese Women in Sex Work Establishments

ERIC Educational Resources Information Center

Weeks, Margaret R.; Li, Jianghong; Liao, Susu; Zhang, Qingning; Dunn, Jennifer; Wang, Yanhong; Jiang, Jingmei

2013-01-01

Social and public health scientists are increasingly interested in applying system dynamics theory to improve understanding and to harness the forces of change within complex, multilevel systems that affect community intervention implementation, effects, and sustainability. Building a system dynamics model based on ethnographic case study has the…

Role of Linguistic and Sociocultural Diversity in Reading Literacy Achievement: A Multilevel Approach

ERIC Educational Resources Information Center

Netten, Andrea; Luyten, Hans; Droop, Mienke; Verhoeven, Ludo

2016-01-01

This study examined how linguistic and sociocultural diversity have an impact on the reading literacy outcomes of a representative sample of 3,549 first-language (L1) and 208 second-language (L2) fourth-grade students in the Netherlands. A multilevel modelling analysis was conducted using Progress in International Reading Literacy Study 2006 data…

Synthesis of Single-Case Experimental Data: A Comparison of Alternative Multilevel Approaches

ERIC Educational Resources Information Center

Ferron, John; Van den Noortgate, Wim; Beretvas, Tasha; Moeyaert, Mariola; Ugille, Maaike; Petit-Bois, Merlande; Baek, Eun Kyeng

2013-01-01

Single-case or single-subject experimental designs (SSED) are used to evaluate the effect of one or more treatments on a single case. Although SSED studies are growing in popularity, the results are in theory case-specific. One systematic and statistical approach for combining single-case data within and across studies is multilevel modeling. The…

Teamwork Satisfaction: Exploring the Multilevel Interaction of Teamwork Interest and Group Extraversion

ERIC Educational Resources Information Center

French, Kimberly A.; Kottke, Janet L.

2013-01-01

Multilevel modeling is used to examine the impact of teamwork interest and group extraversion on group satisfaction. Participants included 206 undergraduates in 65 groups who were surveyed at the beginning and end of a requisite term-length group project for an upper-division university course. We hypothesized that teamwork interest and both…

Incorporating Gender Specific Approaches for Incarcerated Female Adolescents: Multilevel Risk Model for Practice

ERIC Educational Resources Information Center

Welch, Chiquitia L.; Roberts-Lewis, Amelia C.; Parker, Sharon

2009-01-01

The rise in female delinquency has resulted in large numbers of girls being incarcerated in Youth Development Centers (YDC). However, there are few gender specific treatment programs for incarcerated female adolescent offenders, particularly for those with a history of substance dependency. In this article, we present a Multi-level Risk Model…

School Climate as a Predictor of Incivility and Bullying among Public School Employees: A Multilevel Analysis

ERIC Educational Resources Information Center

Powell, Joshua E.; Powell, Anna L.; Petrosko, Joseph M.

2015-01-01

We surveyed public school educators on the workplace incivility and workplace bullying they experienced and obtained their ratings of the organizational climate of the school. We used multilevel modeling to determine the effects of individual-level and school-level predictors. Ratings of school climate were significantly related to incivility and…

Multilevel Analyses of School and Children's Characteristics Associated with Physical Activity

ERIC Educational Resources Information Center

Gomes, Thayse Natacha; dos Santos, Fernanda K.; Zhu, Weimo; Eisenmann, Joey; Maia, José A. R.

2014-01-01

Background: Children spend most of their awake time at school, and it is important to identify individual and school-level correlates of their physical activity (PA) levels. This study aimed to identify the between-school variability in Portuguese children PA and to investigate student and school PA correlates using multilevel modeling. Methods:…

Multiclassifier information fusion methods for microarray pattern recognition

NASA Astrophysics Data System (ADS)

Braun, Jerome J.; Glina, Yan; Judson, Nicholas; Herzig-Marx, Rachel

2004-04-01

This paper addresses automatic recognition of microarray patterns, a capability that could have a major significance for medical diagnostics, enabling development of diagnostic tools for automatic discrimination of specific diseases. The paper presents multiclassifier information fusion methods for microarray pattern recognition. The input space partitioning approach based on fitness measures that constitute an a-priori gauging of classification efficacy for each subspace is investigated. Methods for generation of fitness measures, generation of input subspaces and their use in the multiclassifier fusion architecture are presented. In particular, two-level quantification of fitness that accounts for the quality of each subspace as well as the quality of individual neighborhoods within the subspace is described. Individual-subspace classifiers are Support Vector Machine based. The decision fusion stage fuses the information from mulitple SVMs along with the multi-level fitness information. Final decision fusion stage techniques, including weighted fusion as well as Dempster-Shafer theory based fusion are investigated. It should be noted that while the above methods are discussed in the context of microarray pattern recognition, they are applicable to a broader range of discrimination problems, in particular to problems involving a large number of information sources irreducible to a low-dimensional feature space.

ML-Space: Hybrid Spatial Gillespie and Particle Simulation of Multi-Level Rule-Based Models in Cell Biology.

PubMed

Bittig, Arne T; Uhrmacher, Adelinde M

2017-01-01

Spatio-temporal dynamics of cellular processes can be simulated at different levels of detail, from (deterministic) partial differential equations via the spatial Stochastic Simulation algorithm to tracking Brownian trajectories of individual particles. We present a spatial simulation approach for multi-level rule-based models, which includes dynamically hierarchically nested cellular compartments and entities. Our approach ML-Space combines discrete compartmental dynamics, stochastic spatial approaches in discrete space, and particles moving in continuous space. The rule-based specification language of ML-Space supports concise and compact descriptions of models and to adapt the spatial resolution of models easily.

HIV, violence, blame and shame: pathways of risk to internalized HIV stigma among South African adolescents living with HIV.

PubMed

Pantelic, Marija; Boyes, Mark; Cluver, Lucie; Meinck, Franziska

2017-08-21

Internalized HIV stigma is a key risk factor for negative outcomes amongst adolescents living with HIV (ALHIV), including non-adherence to anti-retroviral treatment, loss-to-follow-up and morbidity. This study tested a theoretical model of multi-level risk pathways to internalized HIV stigma among South African ALHIV. From 2013 to 2015, a survey using t otal population sampling of ALHIV who had ever initiated anti-retroviral treatment (ART) in 53 public health facilities in the Eastern Cape, South Africa was conducted. Community-tracing ensured inclusion of ALHIV who were defaulting from ART or lost to follow-up. 90.1% of eligible ALHIV were interviewed ( n  = 1060, 55% female, mean age = 13.8, 21% living in rural locations). HIV stigma mechanisms (internalized, enacted, and anticipated), HIV-related disability, violence victimization (physical, emotional, sexual abuse, bullying victimization) were assessed using well-validated self-report measures. Structural equation modelling was used to test a theoretically informed model of risk pathways from HIV-related disability to internalized HIV stigma. The model controlled for age, gender and urban/rural address. Prevalence of internalized HIV stigma was 26.5%. As hypothesized, significant associations between internalized stigma and anticipated stigma, as well as depression were obtained. Unexpectedly, HIV-related disability, victimization, and enacted stigma were not directly associated with internalized stigma. Instead significant pathways were identified via anticipated HIV stigma and depression. The model fitted the data well (RMSEA = .023; CFI = .94; TLI = .95; WRMR = 1.070). These findings highlight the complicated nature of internalized HIV stigma. Whilst it is seemingly a psychological process, indirect pathways suggest multi-level mechanisms leading to internalized HIV stigma. Findings suggest that protection from violence within homes, communities and schools may interrupt risk pathways from HIV-related health problems to psychological distress and internalized HIV stigma. This highlights the potential for interventions that do not explicitly target adolescents living with HIV but are sensitive to their needs.

HIV, violence, blame and shame: pathways of risk to internalized HIV stigma among South African adolescents living with HIV

PubMed Central

Pantelic, Marija; Boyes, Mark; Cluver, Lucie; Meinck, Franziska

2017-01-01

Abstract Introduction: Internalized HIV stigma is a key risk factor for negative outcomes amongst adolescents living with HIV (ALHIV), including non-adherence to anti-retroviral treatment, loss-to-follow-up and morbidity. This study tested a theoretical model of multi-level risk pathways to internalized HIV stigma among South African ALHIV. Methods: From 2013 to 2015, a survey using total population sampling of ALHIV who had ever initiated anti-retroviral treatment (ART) in 53 public health facilities in the Eastern Cape, South Africa was conducted. Community-tracing ensured inclusion of ALHIV who were defaulting from ART or lost to follow-up. 90.1% of eligible ALHIV were interviewed (n = 1060, 55% female, mean age = 13.8, 21% living in rural locations). HIV stigma mechanisms (internalized, enacted, and anticipated), HIV-related disability, violence victimization (physical, emotional, sexual abuse, bullying victimization) were assessed using well-validated self-report measures. Structural equation modelling was used to test a theoretically informed model of risk pathways from HIV-related disability to internalized HIV stigma. The model controlled for age, gender and urban/rural address. Results: Prevalence of internalized HIV stigma was 26.5%. As hypothesized, significant associations between internalized stigma and anticipated stigma, as well as depression were obtained. Unexpectedly, HIV-related disability, victimization, and enacted stigma were not directly associated with internalized stigma. Instead significant pathways were identified via anticipated HIV stigma and depression. The model fitted the data well (RMSEA = .023; CFI = .94; TLI = .95; WRMR = 1.070). Conclusions: These findings highlight the complicated nature of internalized HIV stigma. Whilst it is seemingly a psychological process, indirect pathways suggest multi-level mechanisms leading to internalized HIV stigma. Findings suggest that protection from violence within homes, communities and schools may interrupt risk pathways from HIV-related health problems to psychological distress and internalized HIV stigma. This highlights the potential for interventions that do not explicitly target adolescents living with HIV but are sensitive to their needs. PMID:28853517

A Bayesian Multilevel Model for Microcystin Prediction in ...

EPA Pesticide Factsheets

The frequency of cyanobacteria blooms in North American lakes is increasing. A major concernwith rising cyanobacteria blooms is microcystin, a common cyanobacterial hepatotoxin. Toexplore the conditions that promote high microcystin concentrations, we analyzed the US EPANational Lake Assessment (NLA) dataset collected in the summer of 2007. The NLA datasetis reported for nine eco-regions. We used the results of random forest modeling as a means ofvariable selection from which we developed a Bayesian multilevel model of microcystin concentrations.Model parameters under a multilevel modeling framework are eco-region specific, butthey are also assumed to be exchangeable across eco-regions for broad continental scaling. Theexchangeability assumption ensures that both the common patterns and eco-region specific featureswill be reflected in the model. Furthermore, the method incorporates appropriate estimatesof uncertainty. Our preliminary results show associations between microcystin and turbidity, totalnutrients, and N:P ratios. The NLA 2012 will be used for Bayesian updating. The results willhelp develop management strategies to alleviate microcystin impacts and improve lake quality. This work provides a probabilistic framework for predicting microcystin presences in lakes. It would allow for insights to be made about how changes in nutrient concentrations could potentially change toxin levels.

Relating Measurement Invariance, Cross-Level Invariance, and Multilevel Reliability.

PubMed

Jak, Suzanne; Jorgensen, Terrence D

2017-01-01

Data often have a nested, multilevel structure, for example when data are collected from children in classrooms. This kind of data complicate the evaluation of reliability and measurement invariance, because several properties can be evaluated at both the individual level and the cluster level, as well as across levels. For example, cross-level invariance implies equal factor loadings across levels, which is needed to give latent variables at the two levels a similar interpretation. Reliability at a specific level refers to the ratio of true score variance over total variance at that level. This paper aims to shine light on the relation between reliability, cross-level invariance, and strong factorial invariance across clusters in multilevel data. Specifically, we will illustrate how strong factorial invariance across clusters implies cross-level invariance and perfect reliability at the between level in multilevel factor models.

Gain and power optimization of the wireless optical system with multilevel modulation.

PubMed

Liu, Xian

2008-06-01

When used in an outdoor environment to expedite networking access, the performance of wireless optical communication systems is affected by transmitter sway. In the design of such systems, much attention has been paid to developing power-efficient schemes. However, the bandwidth efficiency is also an important issue. One of the most natural approaches to promote bandwidth efficiency is to use multilevel modulation. This leads to multilevel pulse amplitude modulation in the context of intensity modulation and direct detection. We develop a model based on the four-level pulse amplitude modulation. We show that the model can be formulated as an optimization problem in terms of the transmitter power, bit error probability, transmitter gain, and receiver gain. The technical challenges raised by modeling and solving the problem include the analytical and numerical treatments for the improper integrals of the Gaussian functions coupled with the erfc function. The results demonstrate that, at the optimal points, the power penalty paid to the doubled bandwidth efficiency is around 3 dB.

Theoretical and software considerations for general dynamic analysis using multilevel substructured models

NASA Technical Reports Server (NTRS)

Schmidt, R. J.; Dodds, R. H., Jr.

1985-01-01

The dynamic analysis of complex structural systems using the finite element method and multilevel substructured models is presented. The fixed-interface method is selected for substructure reduction because of its efficiency, accuracy, and adaptability to restart and reanalysis. This method is extended to reduction of substructures which are themselves composed of reduced substructures. The implementation and performance of the method in a general purpose software system is emphasized. Solution algorithms consistent with the chosen data structures are presented. It is demonstrated that successful finite element software requires the use of software executives to supplement the algorithmic language. The complexity of the implementation of restart and reanalysis porcedures illustrates the need for executive systems to support the noncomputational aspects of the software. It is shown that significant computational efficiencies can be achieved through proper use of substructuring and reduction technbiques without sacrificing solution accuracy. The restart and reanalysis capabilities and the flexible procedures for multilevel substructured modeling gives economical yet accurate analyses of complex structural systems.

A multilevel model for comorbid outcomes: obesity and diabetes in the US.

PubMed

Congdon, Peter

2010-02-01

Multilevel models are overwhelmingly applied to single health outcomes, but when two or more health conditions are closely related, it is important that contextual variation in their joint prevalence (e.g., variations over different geographic settings) is considered. A multinomial multilevel logit regression approach for analysing joint prevalence is proposed here that includes subject level risk factors (e.g., age, race, education) while also taking account of geographic context. Data from a US population health survey (the 2007 Behavioral Risk Factor Surveillance System or BRFSS) are used to illustrate the method, with a six category multinomial outcome defined by diabetic status and weight category (obese, overweight, normal). The influence of geographic context is partly represented by known geographic variables (e.g., county poverty), and partly by a model for latent area influences. In particular, a shared latent variable (common factor) approach is proposed to measure the impact of unobserved area influences on joint weight and diabetes status, with the latent variable being spatially structured to reflect geographic clustering in risk.

A new rational-based optimal design strategy of ship structure based on multi-level analysis and super-element modeling method

NASA Astrophysics Data System (ADS)

Sun, Li; Wang, Deyu

2011-09-01

A new multi-level analysis method of introducing the super-element modeling method, derived from the multi-level analysis method first proposed by O. F. Hughes, has been proposed in this paper to solve the problem of high time cost in adopting a rational-based optimal design method for ship structural design. Furthermore, the method was verified by its effective application in optimization of the mid-ship section of a container ship. A full 3-D FEM model of a ship, suffering static and quasi-static loads, was used as the analyzing object for evaluating the structural performance of the mid-ship module, including static strength and buckling performance. Research results reveal that this new method could substantially reduce the computational cost of the rational-based optimization problem without decreasing its accuracy, which increases the feasibility and economic efficiency of using a rational-based optimal design method in ship structural design.

Multivariate Longitudinal Analysis with Bivariate Correlation Test

PubMed Central

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model’s parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692

Excess mortality in persons with severe mental disorders: a multilevel intervention framework and priorities for clinical practice, policy and research agendas

PubMed Central

Liu, Nancy H.; Daumit, Gail L.; Dua, Tarun; Aquila, Ralph; Charlson, Fiona; Cuijpers, Pim; Druss, Benjamin; Dudek, Kenn; Freeman, Melvyn; Fujii, Chiyo; Gaebel, Wolfgang; Hegerl, Ulrich; Levav, Itzhak; Munk Laursen, Thomas; Ma, Hong; Maj, Mario; Elena Medina‐Mora, Maria; Nordentoft, Merete; Prabhakaran, Dorairaj; Pratt, Karen; Prince, Martin; Rangaswamy, Thara; Shiers, David; Susser, Ezra; Thornicroft, Graham; Wahlbeck, Kristian; Fekadu Wassie, Abe; Whiteford, Harvey; Saxena, Shekhar

2017-01-01

Excess mortality in persons with severe mental disorders (SMD) is a major public health challenge that warrants action. The number and scope of truly tested interventions in this area remain limited, and strategies for implementation and scaling up of programmes with a strong evidence base are scarce. Furthermore, the majority of available interventions focus on a single or an otherwise limited number of risk factors. Here we present a multilevel model highlighting risk factors for excess mortality in persons with SMD at the individual, health system and socio‐environmental levels. Informed by that model, we describe a comprehensive framework that may be useful for designing, implementing and evaluating interventions and programmes to reduce excess mortality in persons with SMD. This framework includes individual‐focused, health system‐focused, and community level and policy‐focused interventions. Incorporating lessons learned from the multilevel model of risk and the comprehensive intervention framework, we identify priorities for clinical practice, policy and research agendas. PMID:28127922

Age-friendly environments and life satisfaction among South Korean elders: person-environment fit perspective.

PubMed

Park, Sojung; Lee, Sangchul

2017-07-01

Drawing on the person-environment (P-E) fit perspective, this study examined the role of environment on the well-being of vulnerable older adults in a non-western context. Using the indicators from the World Health Organization's (WHO) framework for age friendly cities (ACF), we examined life satisfaction among South Korean older adults, exploring the extent to which multidimensional environmental characteristics are associated with low socioeconomic status (SES). Using the regionally representative data from the Seoul City-wide needs assessment of middle- and old-aged adults, an analytic sample (N = 1657) focused on community-living individuals aged 65 and older. Multilevel regression models examined interaction between SES subgroups and varying aspects of the environment (i.e. physical, social, and service environment) as related to life satisfaction. Consistent with the environmental docility hypothesis, members of the most vulnerable subgroup in the Korean context - older adults who are living alone and poor - are more likely to have higher life satisfaction when they have higher levels of support in physical and social environments. Interestingly, a higher level of support in the service environment was related to lower life satisfaction for this subgroup. This study provides an empirical foundation for efforts to identify age-friendly environmental characteristics as modifiable environmental resources that can improve older adults' psychological well-being. As the first attempt to use WHO ACF indicators within the P-E fit perspective in a non-Western context, our study provides a foundation for designing support services or programs that effectively meet the needs of vulnerable older adults.

The dynamics of stress and fatigue across menopause: attractors, coupling, and resilience.

PubMed

Taylor-Swanson, Lisa; Wong, Alexander E; Pincus, David; Butner, Jonathan E; Hahn-Holbrook, Jennifer; Koithan, Mary; Wann, Kathryn; Woods, Nancy F

2018-04-01

The objective of this study was to evaluate the regulatory dynamics between stress and fatigue experienced by women during the menopausal transition (MT) and early postmenopause (EPM). Fatigue and perceived stress are commonly experienced by women during the MT and EPM. We sought to discover relationships between these symptoms and to employ these symptoms as possible markers for resilience. Participants were drawn from the longitudinal Seattle Midlife Women's Health Study. Eligible women completed questionnaires on 60+ occasions (annual health reports and monthly health diaries) (n = 56 women). The total number of observations across the sample was 4,224. STRAW+10 criteria were used to stage women in either in late reproductive, early or late transition, or EPM stage. Change values were generated for fatigue and stress and analyzed with a multilevel structural equation model; slopes indicate how quickly a person returns to homeostasis after a perturbation. Coupling of stress and fatigue was modeled to evaluate resilience, the notion of maintaining stability during change. Eligible women were on average 35 years old (SD = 4.71), well educated, employed, married or partnered, and white. Fit indices suggested the model depicts the relationships of stress and fatigue (χ(9 df) = 7.638, P = 0.57, correction factor = 4.9244; root mean square error of approximation (RMSEA) 90% CI = 0.000 ≤ 0.000 ≤ 0.032; comparative fit index (CFI) = 1.00). A loss in model fit across stages suggests that the four stages differed in their dynamics (χΔ(12 df) = 21.181, P = .048). All stages showed fixed-point attractor dynamics: fatigue became less stable over time; stress generally became more stable over time. Coupling relationships of stress on fatigue show evidence for shifts in regulatory relationships with one another across the MT. Results are suggestive of general dysregulation via disruptions to coupling relationships of stress and fatigue across the MT. Findings support a holistic approach to understanding symptoms and supporting women during the MT.

A 2 × 2 taxonomy of multilevel latent contextual models: accuracy-bias trade-offs in full and partial error correction models.

PubMed

Lüdtke, Oliver; Marsh, Herbert W; Robitzsch, Alexander; Trautwein, Ulrich

2011-12-01

In multilevel modeling, group-level variables (L2) for assessing contextual effects are frequently generated by aggregating variables from a lower level (L1). A major problem of contextual analyses in the social sciences is that there is no error-free measurement of constructs. In the present article, 2 types of error occurring in multilevel data when estimating contextual effects are distinguished: unreliability that is due to measurement error and unreliability that is due to sampling error. The fact that studies may or may not correct for these 2 types of error can be translated into a 2 × 2 taxonomy of multilevel latent contextual models comprising 4 approaches: an uncorrected approach, partial correction approaches correcting for either measurement or sampling error (but not both), and a full correction approach that adjusts for both sources of error. It is shown mathematically and with simulated data that the uncorrected and partial correction approaches can result in substantially biased estimates of contextual effects, depending on the number of L1 individuals per group, the number of groups, the intraclass correlation, the number of indicators, and the size of the factor loadings. However, the simulation study also shows that partial correction approaches can outperform full correction approaches when the data provide only limited information in terms of the L2 construct (i.e., small number of groups, low intraclass correlation). A real-data application from educational psychology is used to illustrate the different approaches.

Multidisciplinary design and analytic approaches to advance prospective research on the multilevel determinants of child health.

PubMed

Johnson, Sara B; Little, Todd D; Masyn, Katherine; Mehta, Paras D; Ghazarian, Sharon R

2017-06-01

Characterizing the determinants of child health and development over time, and identifying the mechanisms by which these determinants operate, is a research priority. The growth of precision medicine has increased awareness and refinement of conceptual frameworks, data management systems, and analytic methods for multilevel data. This article reviews key methodological challenges in cohort studies designed to investigate multilevel influences on child health and strategies to address them. We review and summarize methodological challenges that could undermine prospective studies of the multilevel determinants of child health and ways to address them, borrowing approaches from the social and behavioral sciences. Nested data, variation in intervals of data collection and assessment, missing data, construct measurement across development and reporters, and unobserved population heterogeneity pose challenges in prospective multilevel cohort studies with children. We discuss innovations in missing data, innovations in person-oriented analyses, and innovations in multilevel modeling to address these challenges. Study design and analytic approaches that facilitate the integration across multiple levels, and that account for changes in people and the multiple, dynamic, nested systems in which they participate over time, are crucial to fully realize the promise of precision medicine for children and adolescents. Copyright © 2017 Elsevier Inc. All rights reserved.

Longitudinal analysis of the strengths and difficulties questionnaire scores of the Millennium Cohort Study children in England using M-quantile random-effects regression.

PubMed

Tzavidis, Nikos; Salvati, Nicola; Schmid, Timo; Flouri, Eirini; Midouhas, Emily

2016-02-01

Multilevel modelling is a popular approach for longitudinal data analysis. Statistical models conventionally target a parameter at the centre of a distribution. However, when the distribution of the data is asymmetric, modelling other location parameters, e.g. percentiles, may be more informative. We present a new approach, M -quantile random-effects regression, for modelling multilevel data. The proposed method is used for modelling location parameters of the distribution of the strengths and difficulties questionnaire scores of children in England who participate in the Millennium Cohort Study. Quantile mixed models are also considered. The analyses offer insights to child psychologists about the differential effects of risk factors on children's outcomes.

In situ stimulation of groundwater denitrification with formate to remediate nitrate contamination

USGS Publications Warehouse

Smith, R.L.; Miller, D.N.; Brooks, M.H.; Widdowson, M.A.; Killingstad, M.W.

2001-01-01

In situ stimulation of denitrification has been proposed as a mechanism to remediate groundwater nitrate contamination. In this study, sodium formate was added to a sand and gravel aquifer on Cape Cod, MA, to test whether formate could serve as a potential electron donor for subsurface denitrification. During 16- and 10-day trials, groundwater from an anoxic nitrate-containing zone (0.5-1.5 mM) was continuously withdrawn, amended with formate and bromide, and pumped back into the aquifer. Concentrations of groundwater constituents were monitored in multilevel samplers after up to 15 m of transport by natural gradient flow. Nitrate and formate concentrations were decreased 80-100% and 60-70%, respectively, with time and subsequent travel distance, while nitrite concentrations inversely increased. The field experiment breakthrough curves were simulated with a two-dimensional site-specific model that included transport, denitrification, and microbial growth. Initial values for model parameters were obtained from laboratory incubations with aquifer core material and then refined to fit field breakthrough curves. The model and the lab results indicated that formate-enhanced nitrite reduction was nearly 4-fold slower than nitrate reduction, but in the lab, nitrite was completely consumed with sufficient exposure time. Results of this study suggest that a long-term injection of formate is necessary to test the remediation potential of this approach for nitrate contamination and that adaptation to nitrite accumulation will be a key determinative factor.In situ stimulation of denitrification has been proposed as a mechanism to remediate groundwater nitrate contamination. In this study, sodium formate was added to a sand and gravel aquifer on Cape Cod, MA, to test whether formate could serve as a potential electron donor for subsurface denitrification. During 16- and 10-day trials, groundwater from an anoxic nitrate-containing zone (0.5-1.5 mM) was continuously withdrawn, amended with formate and bromide, and pumped back into the aquifer. Concentrations of groundwater constituents were monitored in multilevel samplers after up to 15 m of transport by natural gradient flow. Nitrate and formate concentrations were decreased 80-100% and 60-70%, respectively, with time and subsequent travel distance, while nitrite concentrations inversely increased. The field experiment breakthrough curves were simulated with a two-dimensional site-specific model that included transport, denitrification, and microbial growth. Initial values for model parameters were obtained from laboratory incubations with aquifer core material and then refined to fit field breakthrough curves. The model and the lab results indicated that formate-enhanced nitrite reduction was nearly 4-fold slower than nitrate reduction, but in the lab, nitrite was completely consumed with sufficient exposure time. Results of this study suggest that a long-term injection of formate is necessary to test the remediation potential of this approach for nitrate contamination and that adaptation to nitrite accumulation will be a key determinative factor.

Multilevel Latent Class Analysis for Large-Scale Educational Assessment Data: Exploring the Relation between the Curriculum and Students' Mathematical Strategies

ERIC Educational Resources Information Center

Fagginger Auer, Marije F.; Hickendorff, Marian; Van Putten, Cornelis M.; Béguin, Anton A.; Heiser, Willem J.

2016-01-01

A first application of multilevel latent class analysis (MLCA) to educational large-scale assessment data is demonstrated. This statistical technique addresses several of the challenges that assessment data offers. Importantly, MLCA allows modeling of the often ignored teacher effects and of the joint influence of teacher and student variables.…

Teachers' Self-Efficacy in Relation to Individual Students with a Variety of Social-Emotional Behaviors: A Multilevel Investigation

ERIC Educational Resources Information Center

Zee, Marjolein; de Jong, Peter F.; Koomen, Helma M. Y.

2016-01-01

The present study examined teachers' domain-specific self-efficacy (TSE) in relation to individual students with a variety of social-emotional behaviors in class. Using a sample of 526 third- to sixth-grade students and 69 teachers, multilevel modeling was conducted to examine students' externalizing, internalizing, and prosocial behaviors as…

Multilevel Analysis of the Effects of Antidiscrimination Policies on Earnings by Sexual Orientation

ERIC Educational Resources Information Center

Klawitter, Marieka

2011-01-01

This study uses the 2000 U.S. Census data to assess the impact of antidiscrimination policies for sexual orientation on earnings for gays and lesbians. Using a multilevel model allows estimation of the effects of state and local policies on earnings and of variation in the effects of sexual orientation across local labor markets. The results…

Fluid Intelligence as a Predictor of Learning: A Longitudinal Multilevel Approach Applied to Math

ERIC Educational Resources Information Center

Primi, Ricardo; Ferrao, Maria Eugenia; Almeida, Leandro S.

2010-01-01

The association between fluid intelligence and inter-individual differences was investigated using multilevel growth curve modeling applied to data measuring intra-individual improvement on math achievement tests. A sample of 166 students (88 boys and 78 girls), ranging in age from 11 to 14 (M = 12.3, SD = 0.64), was tested. These individuals took…

Examining the Rule of Thumb of Not Using Multilevel Modeling: The "Design Effect Smaller than Two" Rule

ERIC Educational Resources Information Center

Lai, Mark H. C.; Kwok, Oi-man

2015-01-01

Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models…

Health behavior change models for HIV prevention and AIDS care: practical recommendations for a multi-level approach.

PubMed

Kaufman, Michelle R; Cornish, Flora; Zimmerman, Rick S; Johnson, Blair T

2014-08-15

Despite increasing recent emphasis on the social and structural determinants of HIV-related behavior, empirical research and interventions lag behind, partly because of the complexity of social-structural approaches. This article provides a comprehensive and practical review of the diverse literature on multi-level approaches to HIV-related behavior change in the interest of contributing to the ongoing shift to more holistic theory, research, and practice. It has the following specific aims: (1) to provide a comprehensive list of relevant variables/factors related to behavior change at all points on the individual-structural spectrum, (2) to map out and compare the characteristics of important recent multi-level models, (3) to reflect on the challenges of operating with such complex theoretical tools, and (4) to identify next steps and make actionable recommendations. Using a multi-level approach implies incorporating increasing numbers of variables and increasingly context-specific mechanisms, overall producing greater intricacies. We conclude with recommendations on how best to respond to this complexity, which include: using formative research and interdisciplinary collaboration to select the most appropriate levels and variables in a given context; measuring social and institutional variables at the appropriate level to ensure meaningful assessments of multiple levels are made; and conceptualizing intervention and research with reference to theoretical models and mechanisms to facilitate transferability, sustainability, and scalability.

Design of shared unit-dose drug distribution network using multi-level particle swarm optimization.

PubMed

Chen, Linjie; Monteiro, Thibaud; Wang, Tao; Marcon, Eric

2018-03-01

Unit-dose drug distribution systems provide optimal choices in terms of medication security and efficiency for organizing the drug-use process in large hospitals. As small hospitals have to share such automatic systems for economic reasons, the structure of their logistic organization becomes a very sensitive issue. In the research reported here, we develop a generalized multi-level optimization method - multi-level particle swarm optimization (MLPSO) - to design a shared unit-dose drug distribution network. Structurally, the problem studied can be considered as a type of capacitated location-routing problem (CLRP) with new constraints related to specific production planning. This kind of problem implies that a multi-level optimization should be performed in order to minimize logistic operating costs. Our results show that with the proposed algorithm, a more suitable modeling framework, as well as computational time savings and better optimization performance are obtained than that reported in the literature on this subject.

Multilevel animal societies can emerge from cultural transmission

PubMed Central

Cantor, Maurício; Shoemaker, Lauren G.; Cabral, Reniel B.; Flores, César O.; Varga, Melinda; Whitehead, Hal

2015-01-01

Multilevel societies, containing hierarchically nested social levels, are remarkable social structures whose origins are unclear. The social relationships of sperm whales are organized in a multilevel society with an upper level composed of clans of individuals communicating using similar patterns of clicks (codas). Using agent-based models informed by an 18-year empirical study, we show that clans are unlikely products of stochastic processes (genetic or cultural drift) but likely originate from cultural transmission via biased social learning of codas. Distinct clusters of individuals with similar acoustic repertoires, mirroring the empirical clans, emerge when whales learn preferentially the most common codas (conformism) from behaviourally similar individuals (homophily). Cultural transmission seems key in the partitioning of sperm whales into sympatric clans. These findings suggest that processes similar to those that generate complex human cultures could not only be at play in non-human societies but also create multilevel social structures in the wild. PMID:26348688

A Social-Ecological Framework of Theory, Assessment, and Prevention of Suicide

PubMed Central

Cramer, Robert J.; Kapusta, Nestor D.

2017-01-01

The juxtaposition of increasing suicide rates with continued calls for suicide prevention efforts begs for new approaches. Grounded in the Centers for Disease Control and Prevention (CDC) framework for tackling health issues, this personal views work integrates relevant suicide risk/protective factor, assessment, and intervention/prevention literatures. Based on these components of suicide risk, we articulate a Social-Ecological Suicide Prevention Model (SESPM) which provides an integration of general and population-specific risk and protective factors. We also use this multi-level perspective to provide a structured approach to understanding current theories and intervention/prevention efforts concerning suicide. Following similar multi-level prevention efforts in interpersonal violence and Human Immunodeficiency Virus (HIV) domains, we offer recommendations for social-ecologically informed suicide prevention theory, training, research, assessment, and intervention programming. Although the SESPM calls for further empirical testing, it provides a suitable backdrop for tailoring of current prevention and intervention programs to population-specific needs. Moreover, the multi-level model shows promise to move suicide risk assessment forward (e.g., development of multi-level suicide risk algorithms or structured professional judgments instruments) to overcome current limitations in the field. Finally, we articulate a set of characteristics of social-ecologically based suicide prevention programs. These include the need to address risk and protective factors with the strongest degree of empirical support at each multi-level layer, incorporate a comprehensive program evaluation strategy, and use a variety of prevention techniques across levels of prevention. PMID:29062296

Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media

NASA Astrophysics Data System (ADS)

Crevillén-García, D.; Power, H.

2017-08-01

In this study, we apply four Monte Carlo simulation methods, namely, Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo and multilevel quasi-Monte Carlo to the problem of uncertainty quantification in the estimation of the average travel time during the transport of particles through random heterogeneous porous media. We apply the four methodologies to a model problem where the only input parameter, the hydraulic conductivity, is modelled as a log-Gaussian random field by using direct Karhunen-Loéve decompositions. The random terms in such expansions represent the coefficients in the equations. Numerical calculations demonstrating the effectiveness of each of the methods are presented. A comparison of the computational cost incurred by each of the methods for three different tolerances is provided. The accuracy of the approaches is quantified via the mean square error.

Affective Balance, Team Prosocial Efficacy and Team Trust: A Multilevel Analysis of Prosocial Behavior in Small Groups.

PubMed

Cuadrado, Esther; Tabernero, Carmen

2015-01-01

Little research has focused on how individual- and team-level characteristics jointly influence, via interaction, how prosocially individuals behave in teams and few studies have considered the potential influence of team context on prosocial behavior. Using a multilevel perspective, we examined the relationships between individual (affective balance) and group (team prosocial efficacy and team trust) level variables and prosocial behavior towards team members. The participants were 123 students nested in 45 small teams. A series of multilevel random models was estimated using hierarchical linear and nonlinear modeling. Individuals were more likely to behave prosocially towards in-group members when they were feeling good. Furthermore, the relationship between positive affective balance and prosocial behavior was stronger in teams with higher team prosocial efficacy levels as well as in teams with higher team trust levels. Finally, the relevance of team trust had a stronger influence on behavior than team prosocial efficacy.

Multilevel modeling: overview and applications to research in counseling psychology.

PubMed

Kahn, Jeffrey H

2011-04-01

Multilevel modeling (MLM) is rapidly becoming the standard method of analyzing nested data, for example, data from students within multiple schools, data on multiple clients seen by a smaller number of therapists, and even longitudinal data. Although MLM analyses are likely to increase in frequency in counseling psychology research, many readers of counseling psychology journals have had only limited exposure to MLM concepts. This paper provides an overview of MLM that blends mathematical concepts with examples drawn from counseling psychology. This tutorial is intended to be a first step in learning about MLM; readers are referred to other sources for more advanced explorations of MLM. In addition to being a tutorial for understanding and perhaps even conducting MLM analyses, this paper reviews recent research in counseling psychology that has adopted a multilevel framework, and it provides ideas for MLM approaches to future research in counseling psychology. 2011 APA, all rights reserved

Role Stress and Emotional Exhaustion Among Health Care Workers: The Buffering Effect of Supportive Coworker Climate in a Multilevel Perspective.

PubMed

Portoghese, Igor; Galletta, Maura; Burdorf, Alex; Cocco, Pierluigi; D'Aloja, Ernesto; Campagna, Marcello

2017-10-01

The aim of the study was to examine the relationship between role stress, emotional exhaustion, and a supportive coworker climate among health care workers, by adopting a multilevel perspective. Aggregated data of 738 health care workers nested within 67 teams of three Italian hospitals were collected. Multilevel regression analysis with a random intercept model was used. Hierarchical linear modeling showed that a lack of role clarity was significantly linked to emotional exhaustion at the individual level. At the unit level, the cross-level interaction revealed that a supportive coworker climate moderated the relationship between lack of role clarity and emotional exhaustion. This study supports previous results of single-level burnout studies, extending the existing literature with evidence on the multidimensional and cross-level interaction associations of a supportive coworker climate as a key aspect of job resources on burnout.

Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media.

PubMed

Crevillén-García, D; Power, H

2017-08-01

In this study, we apply four Monte Carlo simulation methods, namely, Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo and multilevel quasi-Monte Carlo to the problem of uncertainty quantification in the estimation of the average travel time during the transport of particles through random heterogeneous porous media. We apply the four methodologies to a model problem where the only input parameter, the hydraulic conductivity, is modelled as a log-Gaussian random field by using direct Karhunen-Loéve decompositions. The random terms in such expansions represent the coefficients in the equations. Numerical calculations demonstrating the effectiveness of each of the methods are presented. A comparison of the computational cost incurred by each of the methods for three different tolerances is provided. The accuracy of the approaches is quantified via the mean square error.

Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media

PubMed Central

Power, H.

2017-01-01

In this study, we apply four Monte Carlo simulation methods, namely, Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo and multilevel quasi-Monte Carlo to the problem of uncertainty quantification in the estimation of the average travel time during the transport of particles through random heterogeneous porous media. We apply the four methodologies to a model problem where the only input parameter, the hydraulic conductivity, is modelled as a log-Gaussian random field by using direct Karhunen–Loéve decompositions. The random terms in such expansions represent the coefficients in the equations. Numerical calculations demonstrating the effectiveness of each of the methods are presented. A comparison of the computational cost incurred by each of the methods for three different tolerances is provided. The accuracy of the approaches is quantified via the mean square error. PMID:28878974

The use of dental care facilities and oral health: a multilevel approach of schoolchildren in the Brazilian context.

PubMed

Antunes, José Leopoldo; Peres, Marco Aurélio; Jahn, Graciela Medeiros Jabôr; Levy, Bárbara Bianca da Silva

2006-01-01

To appraise the association between dental care utilisation and gingival status in the Brazilian context, controlling for covariates on socio-demographic characteristics and dentofacial anomalies (12-year-old children). A survey of oral health comprising 5780 schoolchildren in 35 towns of the state of São Paulo, Brazil, provided primary information regarding the assessment of the community periodontal index. The survey also provided information on socio-demographic characteristics and the dental aesthetic index of participants. The utilization of dental services was measured at the town-level, in terms of the dental care index (F/DMFT ratio). Multilevel models of logistic regression fitted the adjustment of covariates for gingival bleeding on probing and calculus. Almost 32% of the children examined presented unhealthy gingival conditions, with a significantly poorer profile for boys, black children and those enrolled in public schools than for their counterparts. Several dentofacial anomalies associated with unhealthy gingival status: crowding of the incisal segments, maxillary and mandibular irregularity, antero posterior molar relation, maxillary overjet and vertical anterior openbite. Towns with a higher dental care index presented a lower proportion of children with gingival bleeding and calculus. This study confirmed previous observations of boys, blacks and children enrolled in public schools as presenting poorer oral health status than their counterparts in the Brazilian context. The utilization of dental services was significantly associated with improved profile of gingival status of participating towns, and this association is unlikely to be due to insufficient control of confounding on socio-demographic characteristics and dentofacial anomalies.

Are tuition-free primary education policies associated with lower infant and neonatal mortality in low- and middle-income countries?

PubMed

Quamruzzaman, Amm; Mendoza Rodríguez, José M; Heymann, Jody; Kaufman, Jay S; Nandi, Arijit

2014-11-01

Robust evidence from low- and middle-income countries (LMICs) suggests that maternal education is associated with better child health outcomes. However, whether or not policies aimed at increasing access to education, including tuition-free education policies, contribute to lower infant and neonatal mortality has not been empirically tested. We joined country-level data on national education policies for 37 LMICs to information on live births to young mothers aged 15-21 years, who were surveyed as part of the population-based Demographic and Health Surveys. We used propensity scores to match births to mothers who were exposed to a tuition-free primary education policy with births to mothers who were not, based on individual-level, household, and country-level characteristics, including GDP per capita, urbanization, and health expenditures per capita. Multilevel logistic regression models, fitted using generalized estimating equations, were used to estimate the effect of exposure to tuition-free primary education policies on the risk of infant and neonatal mortality. We also tested whether this effect was modified by household socioeconomic status. The propensity score matched samples for analyses of infant and neonatal mortality comprised 24,396 and 36,030 births, respectively, from 23 countries. Multilevel regression analyses showed that, on average, exposure to a tuition-free education policy was associated with 15 (95% CI=-32, 1) fewer infant and 5 (95% CI=-13, 4) fewer neonatal deaths per 1000 live births. We found no strong evidence of heterogeneity of this effect by socioeconomic level. Copyright © 2014. Published by Elsevier Ltd.

Urban-rural differences in self-reported limiting long-term illness in Scotland.

PubMed

Levin, Kate A

2003-12-01

Previous research suggests that there are significant differences in health between urban and rural areas. The aim of this study is to describe the pattern and magnitude of urban-rural variation in health in Scotland and to examine the factors associated with health inequalities in urban and rural areas. The data used in this study were limiting long-term illness (LLTI) and socio-economic data collected by the 1991 Census. A rurality indicator was created using Scottish Household Survey rurality classifications. Multilevel Poisson regression modelling was carried out with LLTI as a health indicator for each type of rurality within Scotland. A variety of socio-economic factors were investigated for each rurality. Areas with the highest Standardized Illness Ratios (SIRs) (>125) are predominantly urban whereas the lowest SIRs (<75) are found in both urban and rural areas. Rural communities are more heterogeneous than urban areas in terms of their social make-up with relation to health; however, when these areas are split according to minor road length and different socio-economic factors are added, the model fit for each new model is improved and the reduction in total variation is comparable with that of the urban models. These findings suggest that rural areas should not be treated as a homogeneous group but should be subdivided into rural types.

Cumulative risk effects for the development of behaviour difficulties in children and adolescents with special educational needs and disabilities.

PubMed

Oldfield, Jeremy; Humphrey, Neil; Hebron, Judith

2015-01-01

Research has identified multiple risk factors for the development of behaviour difficulties. What have been less explored are the cumulative effects of exposure to multiple risks on behavioural outcomes, with no study specifically investigating these effects within a population of young people with special educational needs and disabilities (SEND). Furthermore, it is unclear whether a threshold or linear risk model better fits the data for this population. The sample included 2660 children and 1628 adolescents with SEND. Risk factors associated with increases in behaviour difficulties over an 18-month period were summed to create a cumulative risk score, with this explanatory variable being added into a multi-level model. A quadratic term was then added to test the threshold model. There was evidence of a cumulative risk effect, suggesting that exposure to higher numbers of risk factors, regardless of their exact nature, resulted in increased behaviour difficulties. The relationship between risk and behaviour difficulties was non-linear, with exposure to increasing risk having a disproportionate and detrimental impact on behaviour difficulties in child and adolescent models. Interventions aimed at reducing behaviour difficulties need to consider the impact of multiple risk variables. Tailoring interventions towards those exposed to large numbers of risks would be advantageous. Copyright © 2015 Elsevier Ltd. All rights reserved.

NOVA: A new multi-level logic simulator

NASA Technical Reports Server (NTRS)

Miles, L.; Prins, P.; Cameron, K.; Shovic, J.

1990-01-01

A new logic simulator that was developed at the NASA Space Engineering Research Center for VLSI Design was described. The simulator is multi-level, being able to simulate from the switch level through the functional model level. NOVA is currently in the Beta test phase and was used to simulate chips designed for the NASA Space Station and the Explorer missions. A new algorithm was devised to simulate bi-directional pass transistors and a preliminary version of the algorithm is presented. The usage of functional models in NOVA is also described and performance figures are presented.

A multilevel modelling approach to analysis of patient costs under managed care.

PubMed

Carey, K

2000-07-01

The growth of the managed care model of health care delivery in the USA has led to broadened interest in the performance of health care providers. This paper uses multilevel modelling to analyse the effects of managed care penetration on patient level costs for a sample of 24 medical centres operated by the Veterans Health Administration (VHA). The appropriateness of a two level approach to this problem over ordinary least squares (OLS) is demonstrated. Results indicate a modicum of difference in institutions' performance after controlling for patient effects. Facilities more heavily penetrated by the managed care model may be more effective at controlling costs of their sicker patients. Copyright 2000 John Wiley & Sons, Ltd.

Nurses' practice environment and satisfaction with schedule flexibility is related to intention to leave due to dissatisfaction: A multi-country, multilevel study.

PubMed

Leineweber, Constanze; Chungkham, Holendro Singh; Lindqvist, Rikard; Westerlund, Hugo; Runesdotter, Sara; Smeds Alenius, Lisa; Tishelman, Carol

2016-06-01

Nursing turnover is a major issue for health care managers, notably during the global nursing workforce shortage. Despite the often hierarchical structure of the data used in nursing studies, few studies have investigated the impact of the work environment on intention to leave using multilevel techniques. Also, differences between intentions to leave the current workplace or to leave the profession entirely have rarely been studied. The aim of the current study was to investigate how aspects of the nurse practice environment and satisfaction with work schedule flexibility measured at different organisational levels influenced the intention to leave the profession or the workplace due to dissatisfaction. Multilevel models were fitted using survey data from the RN4CAST project, which has a multi-country, multilevel, cross-sectional design. The data analysed here are based on a sample of 23,076 registered nurses from 2020 units in 384 hospitals in 10 European countries (overall response rate: 59.4%). Four levels were available for analyses: country, hospital, unit, and individual registered nurse. Practice environment and satisfaction with schedule flexibility were aggregated and studied at the unit level. Gender, experience as registered nurse, full vs. part-time work, as well as individual deviance from unit mean in practice environment and satisfaction with work schedule flexibility, were included at the individual level. Both intention to leave the profession and the hospital due to dissatisfaction were studied. Regarding intention to leave current workplace, there is variability at both country (6.9%) and unit (6.9%) level. However, for intention to leave the profession we found less variability at the country (4.6%) and unit level (3.9%). Intention to leave the workplace was strongly related to unit level variables. Additionally, individual characteristics and deviance from unit mean regarding practice environment and satisfaction with schedule flexibility were related to both outcomes. Major limitations of the study are its cross-sectional design and the fact that only turnover intention due to dissatisfaction was studied. We conclude that measures aiming to improve the practice environment and schedule flexibility would be a promising approach towards increased retention of registered nurses in both their current workplaces and the nursing profession as a whole and thus a way to counteract the nursing shortage across European countries. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

A Bayesian, generalized frailty model for comet assays.

PubMed

Ghebretinsae, Aklilu Habteab; Faes, Christel; Molenberghs, Geert; De Boeck, Marlies; Geys, Helena

2013-05-01

This paper proposes a flexible modeling approach for so-called comet assay data regularly encountered in preclinical research. While such data consist of non-Gaussian outcomes in a multilevel hierarchical structure, traditional analyses typically completely or partly ignore this hierarchical nature by summarizing measurements within a cluster. Non-Gaussian outcomes are often modeled using exponential family models. This is true not only for binary and count data, but also for, example, time-to-event outcomes. Two important reasons for extending this family are for (1) the possible occurrence of overdispersion, meaning that the variability in the data may not be adequately described by the models, which often exhibit a prescribed mean-variance link, and (2) the accommodation of a hierarchical structure in the data, owing to clustering in the data. The first issue is dealt with through so-called overdispersion models. Clustering is often accommodated through the inclusion of random subject-specific effects. Though not always, one conventionally assumes such random effects to be normally distributed. In the case of time-to-event data, one encounters, for example, the gamma frailty model (Duchateau and Janssen, 2007 ). While both of these issues may occur simultaneously, models combining both are uncommon. Molenberghs et al. ( 2010 ) proposed a broad class of generalized linear models accommodating overdispersion and clustering through two separate sets of random effects. Here, we use this method to model data from a comet assay with a three-level hierarchical structure. Although a conjugate gamma random effect is used for the overdispersion random effect, both gamma and normal random effects are considered for the hierarchical random effect. Apart from model formulation, we place emphasis on Bayesian estimation. Our proposed method has an upper hand over the traditional analysis in that it (1) uses the appropriate distribution stipulated in the literature; (2) deals with the complete hierarchical nature; and (3) uses all information instead of summary measures. The fit of the model to the comet assay is compared against the background of more conventional model fits. Results indicate the toxicity of 1,2-dimethylhydrazine dihydrochloride at different dose levels (low, medium, and high).

The Importance of Isomorphism for Conclusions about Homology: A Bayesian Multilevel Structural Equation Modeling Approach with Ordinal Indicators.

PubMed

Guenole, Nigel

2016-01-01

We describe a Monte Carlo study examining the impact of assuming item isomorphism (i.e., equivalent construct meaning across levels of analysis) on conclusions about homology (i.e., equivalent structural relations across levels of analysis) under varying degrees of non-isomorphism in the context of ordinal indicator multilevel structural equation models (MSEMs). We focus on the condition where one or more loadings are higher on the between level than on the within level to show that while much past research on homology has ignored the issue of psychometric isomorphism, psychometric isomorphism is in fact critical to valid conclusions about homology. More specifically, when a measurement model with non-isomorphic items occupies an exogenous position in a multilevel structural model and the non-isomorphism of these items is not modeled, the within level exogenous latent variance is under-estimated leading to over-estimation of the within level structural coefficient, while the between level exogenous latent variance is overestimated leading to underestimation of the between structural coefficient. When a measurement model with non-isomorphic items occupies an endogenous position in a multilevel structural model and the non-isomorphism of these items is not modeled, the endogenous within level latent variance is under-estimated leading to under-estimation of the within level structural coefficient while the endogenous between level latent variance is over-estimated leading to over-estimation of the between level structural coefficient. The innovative aspect of this article is demonstrating that even minor violations of psychometric isomorphism render claims of homology untenable. We also show that posterior predictive p-values for ordinal indicator Bayesian MSEMs are insensitive to violations of isomorphism even when they lead to severely biased within and between level structural parameters. We highlight conditions where poor estimation of even correctly specified models rules out empirical examination of isomorphism and homology without taking precautions, for instance, larger Level-2 sample sizes, or using informative priors.

The Importance of Isomorphism for Conclusions about Homology: A Bayesian Multilevel Structural Equation Modeling Approach with Ordinal Indicators

PubMed Central

Guenole, Nigel

2016-01-01

We describe a Monte Carlo study examining the impact of assuming item isomorphism (i.e., equivalent construct meaning across levels of analysis) on conclusions about homology (i.e., equivalent structural relations across levels of analysis) under varying degrees of non-isomorphism in the context of ordinal indicator multilevel structural equation models (MSEMs). We focus on the condition where one or more loadings are higher on the between level than on the within level to show that while much past research on homology has ignored the issue of psychometric isomorphism, psychometric isomorphism is in fact critical to valid conclusions about homology. More specifically, when a measurement model with non-isomorphic items occupies an exogenous position in a multilevel structural model and the non-isomorphism of these items is not modeled, the within level exogenous latent variance is under-estimated leading to over-estimation of the within level structural coefficient, while the between level exogenous latent variance is overestimated leading to underestimation of the between structural coefficient. When a measurement model with non-isomorphic items occupies an endogenous position in a multilevel structural model and the non-isomorphism of these items is not modeled, the endogenous within level latent variance is under-estimated leading to under-estimation of the within level structural coefficient while the endogenous between level latent variance is over-estimated leading to over-estimation of the between level structural coefficient. The innovative aspect of this article is demonstrating that even minor violations of psychometric isomorphism render claims of homology untenable. We also show that posterior predictive p-values for ordinal indicator Bayesian MSEMs are insensitive to violations of isomorphism even when they lead to severely biased within and between level structural parameters. We highlight conditions where poor estimation of even correctly specified models rules out empirical examination of isomorphism and homology without taking precautions, for instance, larger Level-2 sample sizes, or using informative priors. PMID:26973580

A surface temperature and moisture parameterization for use in mesoscale numerical models

NASA Technical Reports Server (NTRS)

Tremback, C. J.; Kessler, R.

1985-01-01

A modified multi-level soil moisture and surface temperature model is presented for use as in defining lower boundary conditions in mesoscale weather models. Account is taken of the hydraulic and thermal diffusion properties of the soil, their variations with soil type, and the mixing ratio at the surface. Techniques are defined for integrating the surface input into the multi-level scheme. Sample simulation runs were performed with the modified model and the original model defined by Pielke, et al. (1977, 1981). The models were applied to regional weather forecasting over soils composed of sand and clay loam. The new form of the model avoided iterations necessary in the earlier version of the model and achieved convergence at reasonable profiles for surface temperature and moisture in regions where the earlier version of the model failed.

Multilevel Resistance Programming in Conductive Bridge Resistive Memory

NASA Astrophysics Data System (ADS)

Mahalanabis, Debayan

This work focuses on the existence of multiple resistance states in a type of emerging non-volatile resistive memory device known commonly as Programmable Metallization Cell (PMC) or Conductive Bridge Random Access Memory (CBRAM), which can be important for applications such as multi-bit memory as well as non-volatile logic and neuromorphic computing. First, experimental data from small signal, quasi-static and pulsed mode electrical characterization of such devices are presented which clearly demonstrate the inherent multi-level resistance programmability property in CBRAM devices. A physics based analytical CBRAM compact model is then presented which simulates the ion-transport dynamics and filamentary growth mechanism that causes resistance change in such devices. Simulation results from the model are fitted to experimental dynamic resistance switching characteristics. The model designed using Verilog-a language is computation-efficient and can be integrated with industry standard circuit simulation tools for design and analysis of hybrid circuits involving both CMOS and CBRAM devices. Three main circuit applications for CBRAM devices are explored in this work. Firstly, the susceptibility of CBRAM memory arrays to single event induced upsets is analyzed via compact model simulation and experimental heavy ion testing data that show possibility of both high resistance to low resistance and low resistance to high resistance transitions due to ion strikes. Next, a non-volatile sense amplifier based flip-flop architecture is proposed which can help make leakage power consumption negligible by allowing complete shutdown of power supply while retaining its output data in CBRAM devices. Reliability and energy consumption of the flip-flop circuit for different CBRAM low resistance levels and supply voltage values are analyzed and compared to CMOS designs. Possible extension of this architecture for threshold logic function computation using the CBRAM devices as re-configurable resistive weights is also discussed. Lastly, Spike timing dependent plasticity (STDP) based gradual resistance change behavior in CBRAM device fabricated in back-end-of-line on a CMOS die containing integrate and fire CMOS neuron circuits is demonstrated for the first time which indicates the feasibility of using CBRAM devices as electronic synapses in spiking neural network hardware implementations for non-Boolean neuromorphic computing.

Small area variation in diabetes prevalence in Puerto Rico

PubMed Central

Tierney, Edward F.; Burrows, Nilka R.; Barker, Lawrence E.; Beckles, Gloria L.; Boyle, James P.; Cadwell, Betsy L.; Kirtland, Karen A.; Thompson, Theodore J.

2015-01-01

Objective To estimate the 2009 prevalence of diagnosed diabetes in Puerto Rico among adults ≥ 20 years of age in order to gain a better understanding of its geographic distribution so that policymakers can more efficiently target prevention and control programs. Methods A Bayesian multilevel model was fitted to the combined 2008–2010 Behavioral Risk Factor Surveillance System and 2009 United States Census data to estimate diabetes prevalence for each of the 78 municipios (counties) in Puerto Rico. Results The mean unadjusted estimate for all counties was 14.3% (range by county, 9.9%–18.0%). The average width of the confidence intervals was 6.2%. Adjusted and unadjusted estimates differed little. Conclusions These 78 county estimates are higher on average and showed less variability (i.e., had a smaller range) than the previously published estimates of the 2008 diabetes prevalence for all United States counties (mean, 9.9%; range, 3.0%–18.2%). PMID:23939364

Commitment language and homework completion in a behavioral employment program for gang-affiliated youth.

PubMed

Smith, Caitlin; Huey, Stanley J; McDaniel, Dawn D

2015-05-01

Research with substance-abusing samples suggests that eliciting commitment language during treatment may improve motivation to change, increase treatment engagement, and promote positive treatment outcomes. However, the relationship between in-session client language and treatment success is not well-understood for youth offender populations. This study evaluated the relationship between commitment language, treatment engagement (i.e., homework completion), and weekly employment outcomes for six gang-affiliated juvenile offenders participating in an employment counseling intervention. Weekly counseling sessions were audio-recorded, transcribed, and coded for commitment language strength. Multilevel models were fit to the data to examine the relationship between commitment language and counseling homework or employment outcomes within participants over time. Commitment language strength predicted subsequent homework completion but not weekly employment. These findings imply that gang-affiliated delinquent youth who express motivation to change during employment counseling will be more likely to comply with counselor-initiated homework. Further research on counselor techniques for promoting commitment language among juvenile gang offenders is needed. © The Author(s) 2013.

Detecting a Change in School Performance: A Bayesian Analysis for a Multilevel Join Point Problem. CSE Technical Report 542.

ERIC Educational Resources Information Center

Thum, Yeow Meng; Bhattacharya, Suman Kumar

To better describe individual behavior within a system, this paper uses a sample of longitudinal test scores from a large urban school system to consider hierarchical Bayes estimation of a multilevel linear regression model in which each individual regression slope of test score on time switches at some unknown point in time, "kj."…

Developing Students' Understanding of Co-Opetition and Multilevel Inventory Management Strategies in Supply Chains: An In-Class Spreadsheet Simulation Exercise

ERIC Educational Resources Information Center

Fetter, Gary; Shockley, Jeff

2014-01-01

Instructors look for ways to explain to students how supply chains can be constructed so that competing suppliers can work together to improve inventory management performance (i.e., a phenomenon known as co-opetition). An Excel spreadsheet-driven simulation is presented that models a complete multilevel supply chain system--customer, retailer,…

Level and Change of Bullying Behavior during High School: A Multilevel Growth Curve Analysis

ERIC Educational Resources Information Center

Nocentini, Annalaura; Menesini, Ersilia; Salmivalli, Christina

2013-01-01

The development of bullying behavior was examined across three years in a sample of 515 adolescents (46% females) from 41 classrooms. At time 1, the students were in grades 9 and 10 (mean age = 14.5 years; SD = 0.54). Results of a multilevel growth model showed that both baseline level and change of bullying varied significantly across individuals…

A Multilevel Multivariate Analysis of Academic Performances in College Based on NCAA Student-Athletes

ERIC Educational Resources Information Center

McArdle, John J.; Paskus, Thomas S.; Boker, Steven M.

2013-01-01

This is an application of contemporary multilevel regression modeling to the prediction of academic performances of 1st-year college students. At a first level of analysis, the data come from N greater than 16,000 students who were college freshman in 1994-1995 and who were also participants in high-level college athletics. At a second level of…

Psychosocial Working Conditions, School Sense of Coherence and Subjective Health Complaints. A Multilevel Analysis of Ninth Grade Pupils in the Stockholm Area

ERIC Educational Resources Information Center

Modin, Bitte; Ostberg, Viveca; Toivanen, Susanna; Sundell, Knut

2011-01-01

This study explores the psychosocial working conditions of 7930 Swedish 9th grade students, distributed over 475 classes and 130 schools, in relation to their subjective health using multilevel modeling. At the individual level, students with "strained" working conditions in school (i.e. those experiencing a high level of demands in…

A Multilevel Analysis of Diverse Learners Playing Life Science Video Games: Interactions between Game Content, Learning Disability Status, Reading Proficiency, and Gender

ERIC Educational Resources Information Center

Israel, Maya; Wang, Shuai; Marino, Matthew T.

2016-01-01

Extant research reports differential effects related to the efficacy of video games as a means to enhance science instruction. However, there are very few studies examining differences in learning outcomes across student-level independent variables. This study used multilevel modeling to examine the effects of three video game-enhanced life…

National and School Policies on Restrictions of Teacher Smoking: A Multilevel Analysis of Student Exposure to Teacher Smoking in Seven European Countries

ERIC Educational Resources Information Center

Wold, Bente; Torsheim, Torbjorn; Currie, Candace; Roberts, Chris

2004-01-01

The paper examines the association between restrictions on teacher tobacco smoking at school and student exposure to teachers who smoke during school hours. The data are taken from a European Commission-funded study "Control of Adolescent Smoking" (the CAS study) in seven European countries. Multilevel modelling analyses were applied to…

Retention of Children and Their Families in the Longitudinal Outcome Study of the Comprehensive Community Mental Health Services for Children and Their Families Program: A Multilevel Analysis

ERIC Educational Resources Information Center

Gebreselassie, Tesfayi; Stephens, Robert L.; Maples, Connie J.; Johnson, Stacy F.; Tucker, Alyce L.

2014-01-01

Predictors of retention of participants in a longitudinal study and heterogeneity between communities were investigated using a multilevel logistic regression model. Data from the longitudinal outcome study of the national evaluation of the Comprehensive Community Mental Health Services for Children and Their Families program and information on…

Equity in the Turkish Education System: A Multilevel Analysis of Social Background Influences on the Mathematics Performance of 15-Year-Old Students

ERIC Educational Resources Information Center

Özdemir, Caner

2016-01-01

This paper aims to discover the level of equity in the Turkish education system using maths outcomes of 15-year-old students in the Programme for International Student Assessment (PISA) exam. In order to do that, associations between various social background variables and student performance are analysed via multilevel models. Female pupils,…

A Bayesian Multilevel Model for Microcystin Prediction in ...

EPA Pesticide Factsheets

The frequency of cyanobacteria blooms in North American lakes is increasing. A major concern with rising cyanobacteria blooms is microcystin, a common cyanobacterial hepatotoxin. To explore the conditions that promote high microcystin concentrations, we analyzed the US EPA National Lake Assessment (NLA) dataset collected in the summer of 2007. The NLA dataset is reported for nine eco-regions. We used the results of random forest modeling as a means ofvariable selection from which we developed a Bayesian multilevel model of microcystin concentrations. Model parameters under a multilevel modeling framework are eco-region specific, butthey are also assumed to be exchangeable across eco-regions for broad continental scaling. The exchangeability assumption ensures that both the common patterns and eco-region specific features will be reflected in the model. Furthermore, the method incorporates appropriate estimates of uncertainty. Our preliminary results show associations between microcystin and turbidity, total nutrients, and N:P ratios. Upon release of a comparable 2012 NLA dataset, we will apply Bayesian updating. The results will help develop management strategies to alleviate microcystin impacts and improve lake quality. This work provides a probabilistic framework for predicting microcystin presences in lakes. It would allow for insights to be made about how changes in nutrient concentrations could potentially change toxin levels.

Joint modelling compared with two stage methods for analysing longitudinal data and prospective outcomes: A simulation study of childhood growth and BP.

PubMed

Sayers, A; Heron, J; Smith, Adac; Macdonald-Wallis, C; Gilthorpe, M S; Steele, F; Tilling, K

2017-02-01

There is a growing debate with regards to the appropriate methods of analysis of growth trajectories and their association with prospective dependent outcomes. Using the example of childhood growth and adult BP, we conducted an extensive simulation study to explore four two-stage and two joint modelling methods, and compared their bias and coverage in estimation of the (unconditional) association between birth length and later BP, and the association between growth rate and later BP (conditional on birth length). We show that the two-stage method of using multilevel models to estimate growth parameters and relating these to outcome gives unbiased estimates of the conditional associations between growth and outcome. Using simulations, we demonstrate that the simple methods resulted in bias in the presence of measurement error, as did the two-stage multilevel method when looking at the total (unconditional) association of birth length with outcome. The two joint modelling methods gave unbiased results, but using the re-inflated residuals led to undercoverage of the confidence intervals. We conclude that either joint modelling or the simpler two-stage multilevel approach can be used to estimate conditional associations between growth and later outcomes, but that only joint modelling is unbiased with nominal coverage for unconditional associations.

Design Principles as a Guide for Constraint Based and Dynamic Modeling: Towards an Integrative Workflow.

PubMed

Sehr, Christiana; Kremling, Andreas; Marin-Sanguino, Alberto

2015-10-16

During the last 10 years, systems biology has matured from a fuzzy concept combining omics, mathematical modeling and computers into a scientific field on its own right. In spite of its incredible potential, the multilevel complexity of its objects of study makes it very difficult to establish a reliable connection between data and models. The great number of degrees of freedom often results in situations, where many different models can explain/fit all available datasets. This has resulted in a shift of paradigm from the initially dominant, maybe naive, idea of inferring the system out of a number of datasets to the application of different techniques that reduce the degrees of freedom before any data set is analyzed. There is a wide variety of techniques available, each of them can contribute a piece of the puzzle and include different kinds of experimental information. But the challenge that remains is their meaningful integration. Here we show some theoretical results that enable some of the main modeling approaches to be applied sequentially in a complementary manner, and how this workflow can benefit from evolutionary reasoning to keep the complexity of the problem in check. As a proof of concept, we show how the synergies between these modeling techniques can provide insight into some well studied problems: Ammonia assimilation in bacteria and an unbranched linear pathway with end-product inhibition.

Is job a viable unit of analysis? A multilevel analysis of demand-control-support models.

PubMed

Morrison, David; Payne, Roy L; Wall, Toby D

2003-07-01

The literature has ignored the fact that the demand-control (DC) and demand-control-support (DCS) models of stress are about jobs and not individuals' perceptions of their jobs. Using multilevel modeling, the authors report results of individual- and job-level analyses from a study of over 6,700 people in 81 different jobs. Support for additive versions of the models came when individuals were the unit of analysis. DC and DCS models are only helpful for understanding the effects of individual perceptions of jobs and their relationship to psychological states. When job perceptions are aggregated and their relationship to the collective experience of jobholders is assessed, the models prove of little value. Role set may be a better unit of analysis.

Clarifying the Use of Aggregated Exposures in Multilevel Models: Self-Included vs. Self-Excluded Measures

PubMed Central

Suzuki, Etsuji; Yamamoto, Eiji; Takao, Soshi; Kawachi, Ichiro; Subramanian, S. V.

2012-01-01

Background Multilevel analyses are ideally suited to assess the effects of ecological (higher level) and individual (lower level) exposure variables simultaneously. In applying such analyses to measures of ecologies in epidemiological studies, individual variables are usually aggregated into the higher level unit. Typically, the aggregated measure includes responses of every individual belonging to that group (i.e. it constitutes a self-included measure). More recently, researchers have developed an aggregate measure which excludes the response of the individual to whom the aggregate measure is linked (i.e. a self-excluded measure). In this study, we clarify the substantive and technical properties of these two measures when they are used as exposures in multilevel models. Methods Although the differences between the two aggregated measures are mathematically subtle, distinguishing between them is important in terms of the specific scientific questions to be addressed. We then show how these measures can be used in two distinct types of multilevel models—self-included model and self-excluded model—and interpret the parameters in each model by imposing hypothetical interventions. The concept is tested on empirical data of workplace social capital and employees' systolic blood pressure. Results Researchers assume group-level interventions when using a self-included model, and individual-level interventions when using a self-excluded model. Analytical re-parameterizations of these two models highlight their differences in parameter interpretation. Cluster-mean centered self-included models enable researchers to decompose the collective effect into its within- and between-group components. The benefit of cluster-mean centering procedure is further discussed in terms of hypothetical interventions. Conclusions When investigating the potential roles of aggregated variables, researchers should carefully explore which type of model—self-included or self-excluded—is suitable for a given situation, particularly when group sizes are relatively small. PMID:23251609

Predicting Depression among Patients with Diabetes Using Longitudinal Data. A Multilevel Regression Model.

PubMed

Jin, H; Wu, S; Vidyanti, I; Di Capua, P; Wu, B

2015-01-01

This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Depression is a common and often undiagnosed condition for patients with diabetes. It is also a condition that significantly impacts healthcare outcomes, use, and cost as well as elevating suicide risk. Therefore, a model to predict depression among diabetes patients is a promising and valuable tool for providers to proactively assess depressive symptoms and identify those with depression. This study seeks to develop a generalized multilevel regression model, using a longitudinal data set from a recent large-scale clinical trial, to predict depression severity and presence of major depression among patients with diabetes. Severity of depression was measured by the Patient Health Questionnaire PHQ-9 score. Predictors were selected from 29 candidate factors to develop a 2-level Poisson regression model that can make population-average predictions for all patients and subject-specific predictions for individual patients with historical records. Newly obtained patient records can be incorporated with historical records to update the prediction model. Root-mean-square errors (RMSE) were used to evaluate predictive accuracy of PHQ-9 scores. The study also evaluated the classification ability of using the predicted PHQ-9 scores to classify patients as having major depression. Two time-invariant and 10 time-varying predictors were selected for the model. Incorporating historical records and using them to update the model may improve both predictive accuracy of PHQ-9 scores and classification ability of the predicted scores. Subject-specific predictions (for individual patients with historical records) achieved RMSE about 4 and areas under the receiver operating characteristic (ROC) curve about 0.9 and are better than population-average predictions. The study developed a generalized multilevel regression model to predict depression and demonstrated that using generalized multilevel regression based on longitudinal patient records can achieve high predictive ability.

The design of multi-core DSP parallel model based on message passing and multi-level pipeline

NASA Astrophysics Data System (ADS)

Niu, Jingyu; Hu, Jian; He, Wenjing; Meng, Fanrong; Li, Chuanrong

2017-10-01

Currently, the design of embedded signal processing system is often based on a specific application, but this idea is not conducive to the rapid development of signal processing technology. In this paper, a parallel processing model architecture based on multi-core DSP platform is designed, and it is mainly suitable for the complex algorithms which are composed of different modules. This model combines the ideas of multi-level pipeline parallelism and message passing, and summarizes the advantages of the mainstream model of multi-core DSP (the Master-Slave model and the Data Flow model), so that it has better performance. This paper uses three-dimensional image generation algorithm to validate the efficiency of the proposed model by comparing with the effectiveness of the Master-Slave and the Data Flow model.

Multilevel en bloc spondylectomy for tumors of the thoracic and lumbar spine is challenging but rewarding.

PubMed

Luzzati, Alessandro Davide; Shah, Sambhav; Gagliano, Fabio; Perrucchini, Giuseppe; Scotto, Gennaro; Alloisio, Marco

2015-03-01

Over the years, en bloc spondylectomy has proven its efficacy in controlling spinal tumors and improving survival rates. However, there are few reports of large series that critically evaluate the results of multilevel en bloc spondylectomies for spinal neoplasms. Using data from a large spine tumor center, we answered the following questions: (1) Does multilevel total en bloc spondylectomy result in acceptable function, survival rates, and local control in spinal neoplasms? (2) Is reconstruction after this procedure feasible? (3) What complications are associated with this procedure? (4) is it possible to achieve adequate surgical margins with this procedure? We retrospectively investigated 38 patients undergoing multilevel total en bloc spondylectomy by a single surgeon (AL) from 1994 to 2011. Indications for this procedure were primary spinal sarcomas, solitary metastases, and aggressive primary benign tumors involving multiple segments of the thoracic or lumbar spine. Patients had to be medically fit and have no visceral metastases. Analysis was by chart and radiographic review. Margin quality was classified into intralesional, marginal, and wide. Radiographs, MR images, and CT scans were studied for local recurrence. Graft healing and instrumentation failures at subsequent followup were assessed. Complications were divided into major or minor and further classified as intraoperative and early and late postoperative. We evaluated the oncologic status using cumulative disease-specific and metastases-free survival analysis. Minimum followup was 24 months (mean, 39 months; range, 24-124 months). Of the 38 patients, 34 (89%) were alive and walking without support at final followup. Thirty-one (81%) had no evidence of disease. Two patients died postoperatively and another two died of systemic disease (without local recurrence). Only three patients (8%) had a local recurrence. There were 14 major complications and 22 minor complications in 25 patients (65%). Only one patient required revision of implants secondary to mechanical failure. Two cases of cage subsidence were noted but had no clinical significance. Wide margins were achieved in nine patients (23%), marginal in 25 (66%), and intralesional in four (11%). In patients with multisegmental spinal tumors, oncologic resections were achieved by multilevel en bloc spondylectomy and led to an acceptable survival rate with reasonable local control. Multilevel en bloc surgery was associated with a high complication rate; however, most patients recovered from their complications. Although the surgical procedure is challenging, our encouraging mid-term results clearly favor and validate this technique. Level IV, therapeutic study. See Instructions for Authors for a complete description of levels of evidence.

Nonresonant interaction of ultrashort electromagnetic pulses with multilevel quantum systems

NASA Technical Reports Server (NTRS)

Belenov, E.; Isakov, V.; Nazarkin, A.

1994-01-01

Some features of the excitation of multilevel quantum systems under the action of electromagnetic pulses which are shorter than the inverse frequency of interlevel transitions are considered. It is shown that the interaction is characterized by a specific type of selectivity which is not connected with the resonant absorption of radiation. The simplest three-level model displays the inverse population of upper levels. The effect of an ultrashort laser pulse on a multilevel molecule was regarded as an instant reception of the oscillation velocity by the oscillator and this approach showed an effective excitation and dissociation of the molecule. The estimations testify to the fact that these effects can be observed using modern femtosecond lasers.

Identification of the Appropriate Boundary Size to Use When Measuring the Food Retail Environment Surrounding Schools

PubMed Central

Seliske, Laura; Pickett, William; Rosu, Andrei; Janssen, Ian

2012-01-01

This study included 6,971 students in grades 9 and 10 (ages 13 to 16 years) from 158schools who participated in the 2009/2010 Health Behaviour in School-aged Children Study. Students provided information on where they typically ate lunch. The number of food retailers was obtained for six road network buffer sizes (500, 750, 1,000, 1,500, 2,000, and 5,000 meters) surrounding schools. Associations between the presence of food retailers near schools and students’ lunchtime eating behaviours were examined using multilevel logistic regression. Comparisons of model fit statistics indicated that the 1,000 m buffer provided the best fit. At this distance, students with ≥3 food retailers near their schools had a 3.42 times greater relative odds (95% CI: 2.12–5.52) of eating their lunchtime meal at a food retailer compared to students with no nearby food retailers. Students who had ≥2 food retailers within 750 m of their schools had a 2.74 times greater relative odds (95% CI: 1.75–4.29), while those who had ≥1 food retailer within 500 m of their schools had 2.27 times greater relative odds of eating at food retailer (95% CI: 1.46–3.52) compared to those with no nearby food retailers. For distances greater than 1,000 m, no consistent relationships were found. PMID:23066392

A Multilevel Model for Comorbid Outcomes: Obesity and Diabetes in the US

PubMed Central

Congdon, Peter

2010-01-01

Multilevel models are overwhelmingly applied to single health outcomes, but when two or more health conditions are closely related, it is important that contextual variation in their joint prevalence (e.g., variations over different geographic settings) is considered. A multinomial multilevel logit regression approach for analysing joint prevalence is proposed here that includes subject level risk factors (e.g., age, race, education) while also taking account of geographic context. Data from a US population health survey (the 2007 Behavioral Risk Factor Surveillance System or BRFSS) are used to illustrate the method, with a six category multinomial outcome defined by diabetic status and weight category (obese, overweight, normal). The influence of geographic context is partly represented by known geographic variables (e.g., county poverty), and partly by a model for latent area influences. In particular, a shared latent variable (common factor) approach is proposed to measure the impact of unobserved area influences on joint weight and diabetes status, with the latent variable being spatially structured to reflect geographic clustering in risk. PMID:20616977

Estimation of river and stream temperature trends under haphazard sampling

USGS Publications Warehouse

Gray, Brian R.; Lyubchich, Vyacheslav; Gel, Yulia R.; Rogala, James T.; Robertson, Dale M.; Wei, Xiaoqiao

2015-01-01

Long-term temporal trends in water temperature in rivers and streams are typically estimated under the assumption of evenly-spaced space-time measurements. However, sampling times and dates associated with historical water temperature datasets and some sampling designs may be haphazard. As a result, trends in temperature may be confounded with trends in time or space of sampling which, in turn, may yield biased trend estimators and thus unreliable conclusions. We address this concern using multilevel (hierarchical) linear models, where time effects are allowed to vary randomly by day and date effects by year. We evaluate the proposed approach by Monte Carlo simulations with imbalance, sparse data and confounding by trend in time and date of sampling. Simulation results indicate unbiased trend estimators while results from a case study of temperature data from the Illinois River, USA conform to river thermal assumptions. We also propose a new nonparametric bootstrap inference on multilevel models that allows for a relatively flexible and distribution-free quantification of uncertainties. The proposed multilevel modeling approach may be elaborated to accommodate nonlinearities within days and years when sampling times or dates typically span temperature extremes.

Using Cross-Classified Multilevel Models to Disentangle School and Neighborhood Effects: An Example Focusing on Smoking Behaviors among Adolescents in the United States

PubMed Central

Dunn, Erin C.; Richmond, Tracy K.; Milliren, Carly E.; Subramanian, S.V.

2015-01-01

Background Despite much interest in understanding the influence of contexts on health, most research has focused on one context at a time despite the reality that individuals have simultaneous memberships in multiple settings. Method Using the example of smoking behavior among adolescents in the National Longitudinal Study of Adolescent Health, we applied cross-classified multilevel modeling (CCMM) to examine fixed and random effects for schools and neighborhoods. We compared the CCMM results with those obtained from a traditional multilevel model (MLM) focused on either the school and neighborhood separately. Results In the MLMs, 5.2% of the variation in smoking was due to differences between neighborhoods (when schools were ignored) and 6.3% to differences between schools (when neighborhoods were ignored). However in the CCMM examining neighborhood and school variation simultaneously, the neighborhood-level variation was reduced to 0.4%. Conclusion Results suggest that using MLM, instead of CCMM, could lead to overestimating the importance of certain contexts and could ultimately lead to targeting interventions or policies to the wrong settings. PMID:25579227

The Role of Individual Correlates and Class Norms in Defending and Passive Bystanding Behavior in Bullying: A Multilevel Analysis

ERIC Educational Resources Information Center

Pozzoli, Tiziana; Gini, Gianluca; Vieno, Alessio

2012-01-01

This study investigates possible individual and class correlates of defending and passive bystanding behavior in bullying, in a sample of 1,825 Italian primary school (mean age = 10 years 1 month) and middle school (mean age = 13 years 2 months) students. The findings of a series of multilevel regression models show that both individual (e.g.,…

Estimation of Indirect Effects in the Presence of Unmeasured Confounding for the Mediator-Outcome Relationship in a Multilevel 2-1-1 Mediation Model

ERIC Educational Resources Information Center

Talloen, Wouter; Moerkerke, Beatrijs; Loeys, Tom; De Naeghel, Jessie; Van Keer, Hilde; Vansteelandt, Stijn

2016-01-01

To assess the direct and indirect effect of an intervention, multilevel 2-1-1 studies with intervention randomized at the upper (class) level and mediator and outcome measured at the lower (student) level are frequently used in educational research. In such studies, the mediation process may flow through the student-level mediator (the within…

The Epistemic Representation of Information Flow Security in Probabilistic Systems

DTIC Science & Technology

1995-06-01

The new characterization also means that our security crite- rion is expressible in a simpler logic and model. 1 Introduction Multilevel security is...ber generator) during its execution. Such probabilistic choices are useful in a multilevel security context for Supported by grants HKUST 608/94E from... 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and

Multilevel model of safety climate for furniture industries.

PubMed

Rodrigues, Matilde A; Arezes, Pedro M; Leão, Celina P

2015-01-01

Furniture companies can analyze their safety status using quantitative measures. However, the data needed are not always available and the number of accidents is under-reported. Safety climate scales may be an alternative. However, there are no validated Portuguese scales that account for the specific attributes of the furniture sector. The current study aims to develop and validate an instrument that uses a multilevel structure to measure the safety climate of the Portuguese furniture industry. The Safety Climate in Wood Industries (SCWI) model was developed and applied to the safety climate analysis using three different scales: organizational, group and individual. A multilevel exploratory factor analysis was performed to analyze the factorial structure. The studied companies' safety conditions were also analyzed. Different factorial structures were found between and within levels. In general, the results show the presence of a group-level safety climate. The scores of safety climates are directly and positively related to companies' safety conditions; the organizational scale is the one that best reflects the actual safety conditions. The SCWI instrument allows for the identification of different safety climates in groups that comprise the same furniture company and it seems to reflect those groups' safety conditions. The study also demonstrates the need for a multilevel analysis of the studied instrument.

A multi-level model accounting for the effects of JAK2-STAT5 signal modulation in erythropoiesis.

PubMed

Lai, Xin; Nikolov, Svetoslav; Wolkenhauer, Olaf; Vera, Julio

2009-08-01

We develop a multi-level model, using ordinary differential equations, based on quantitative experimental data, accounting for murine erythropoiesis. At the sub-cellular level, the model includes a description of the regulation of red blood cell differentiation through Epo-stimulated JAK2-STAT5 signalling activation, while at the cell population level the model describes the dynamics of (STAT5-mediated) red blood cell differentiation from their progenitors. Furthermore, the model includes equations depicting the hypoxia-mediated regulation of hormone erythropoietin blood levels. Take all together, the model constitutes a multi-level, feedback loop-regulated biological system, involving processes in different organs and at different organisational levels. We use our model to investigate the effect of deregulation in the proteins involved in the JAK2-STAT5 signalling pathway in red blood cells. Our analysis results suggest that down-regulation in any of the three signalling system components affects the hematocrit level in an individual considerably. In addition, our analysis predicts that exogenous Epo injection (an already existing treatment for several blood diseases) may compensate the effects of single down-regulation of Epo hormone level, STAT5 or EpoR/JAK2 expression level, and that it may be insufficient to counterpart a combined down-regulation of all the elements in the JAK2-STAT5 signalling cascade.

Multilevel Models for Intensive Longitudinal Data with Heterogeneous Autoregressive Errors: The Effect of Misspecification and Correction with Cholesky Transformation

PubMed Central

Jahng, Seungmin; Wood, Phillip K.

2017-01-01

Intensive longitudinal studies, such as ecological momentary assessment studies using electronic diaries, are gaining popularity across many areas of psychology. Multilevel models (MLMs) are most widely used analytical tools for intensive longitudinal data (ILD). Although ILD often have individually distinct patterns of serial correlation of measures over time, inferences of the fixed effects, and random components in MLMs are made under the assumption that all variance and autocovariance components are homogenous across individuals. In the present study, we introduced a multilevel model with Cholesky transformation to model ILD with individually heterogeneous covariance structure. In addition, the performance of the transformation method and the effects of misspecification of heterogeneous covariance structure were investigated through a Monte Carlo simulation. We found that, if individually heterogeneous covariances are incorrectly assumed as homogenous independent or homogenous autoregressive, MLMs produce highly biased estimates of the variance of random intercepts and the standard errors of the fixed intercept and the fixed effect of a level 2 covariate when the average autocorrelation is high. For intensive longitudinal data with individual specific residual covariance, the suggested transformation method showed lower bias in those estimates than the misspecified models when the number of repeated observations within individuals is 50 or more. PMID:28286490

The longitudinal relationship between motor competence and measures of fatness and fitness from childhood into adolescence.

PubMed

Lima, Rodrigo Antunes; Bugge, Anna; Ersbøll, Annette K; Stodden, David F; Andersen, Lars B

2018-05-18

To examine longitudinal (seven years) relationships among cardiorespiratory fitness (VO 2peak ), body fatness, and motor competence. Data were collected as part of the Copenhagen School Child Intervention Study (CoSCIS). Body fatness was assessed by the sum of four skinfolds. VO 2peak was measured directly in a continuous running protocol. Motor competence was assessed using the Körperkoordinationtest für Kinder. This study used multilevel linear mixed models to evaluate the reciprocal longitudinal association between body fatness, VO 2peak , and motor competence. All regressions were stratified by sex and adjusted by intervention and pubertal status. All variable coefficients were standardized. A reciprocal relationship was observed between children's motor competence with body fatness and VO 2peak at the seven-year follow-up (6-13 years of age). Children with higher motor competence at baseline had a lower risk of having higher body fatness (β boys =-0.45, 95% CI: -0.52 to -0.38; β girls =-0.35, 95% CI: -0.42 to -0.28) and higher VO 2peak (β boys =0.34, 95% CI: 0.27-0.40; β girls =0.27, 95% CI: 0.20-0.33) during childhood. Alternatively, higher body fatness or lower levels of VO 2peak at baseline were associated with lower motor competence during childhood. These data suggest motor competence, body fatness, and VO 2peak demonstrate reciprocal relationships across childhood (6-13 years of age). Interventions addressing motor competence, cardiorespiratory fitness, and body fatness in early childhood are recommended, as intervention effects are likely to be enhanced because of the mutual reciprocal associations between these three variables. Copyright © 2018. Published by Elsevier Editora Ltda.

Preferences for the sex-composition of children in Europe: a multilevel examination of its effect on progression to a third child.

PubMed

Mills, Melinda; Begall, Katia

2010-03-01

Comparative research on the preferred sex of children in Western societies has generally focused on women only and ignored the role of gender equity and the need for children's economic support in old age. A multilevel analysis extends existing research by examining, for both men and women and across 24 European countries, the effect of the preferred sex-composition of offspring on whether parents have or intend to have a third child. Using the European Social Survey (2004/5), a multilevel (random coefficient) ordered logit regression of that intention (N = 3,323) and a binary logistic multilevel model of the transition to a third child (N = 6,502) demonstrate the presence of a mixed-sex preference. In countries with a high risk of poverty in old age, a preference for sons is found, particularly for men. In societies where there is lower gender equity, both men and women have a significant preference for boys.

Selfish genetic elements and the gene’s-eye view of evolution

PubMed Central

2016-01-01

During the last few decades, we have seen an explosion in the influx of details about the biology of selfish genetic elements. Ever since the early days of the field, the gene’s-eye view of Richard Dawkins, George Williams, and others, has been instrumental to make sense of new empirical observations and to the generation of new hypotheses. However, the close association between selfish genetic elements and the gene’s-eye view has not been without critics and several other conceptual frameworks have been suggested. In particular, proponents of multilevel selection models have used selfish genetic elements to criticize the gene’s-eye view. In this paper, I first trace the intertwined histories of the study of selfish genetic elements and the gene’s-eye view and then discuss how their association holds up when compared with other proposed frameworks. Next, using examples from transposable elements and the major transitions, I argue that different models highlight separate aspects of the evolution of selfish genetic elements and that the productive way forward is to maintain a plurality of perspectives. Finally, I discuss how the empirical study of selfish genetic elements has implications for other conceptual issues associated with the gene’s-eye view, such as agential thinking, adaptationism, and the role of fitness maximizing models in evolution. PMID:29491953

Physical, policy, and sociocultural characteristics of the primary school environment are positively associated with children's physical activity during class time.

PubMed

Martin, Karen; Bremner, Alexandra; Salmon, Jo; Rosenberg, Michael; Giles-Corti, Billie

2014-03-01

The objective of this study was to develop a multidomain model to identify key characteristics of the primary school environment associated with children's physical activity (PA) during class-time. Accelerometers were used to calculate time spent in moderate-to-vigorous physical activity during class-time (CMVPA) of 408 sixth-grade children (mean ± SD age 11.1 ± 0.43 years) attending 27 metropolitan primary schools in Perth Western Australia. Child and staff self-report instruments and a school physical environment scan administered by the research team were used to collect data about children and the class and school environments. Hierarchical modeling identified key variables associated with CMVPA. The final multilevel model explained 49% of CMVPA. A physically active physical education (PE) coordinator, fitness sessions incorporated into PE sessions and either a trained PE specialist, classroom teacher or nobody coordinating PE in the school, rather than the deputy principal, were associated with higher CMVPA. The amount of grassed area per student and sporting apparatus on grass were also associated with higher CMVPA. These results highlight the relevance of the school's sociocultural, policy and physical environments in supporting class-based PA. Interventions testing optimization of the school physical, sociocultural and policy environments to support physical activity are warranted.

Individual, social and environmental correlates of physical activity in overweight and obese African American and Hispanic women: A structural equation model analysis.

PubMed

Mama, Scherezade K; Diamond, Pamela M; McCurdy, Sheryl A; Evans, Alexandra E; McNeill, Lorna H; Lee, Rebecca E

Ecologic frameworks account for multilevel factors related to physical activity (PA) and may be used to develop effective interventions for women. The purpose of this study was to examine the influence of individual, social and environmental factors on PA among African American and Hispanic women using structural equation modeling. Overweight and obese women ( N =164, 65.9% African American) completed a 7-day accelerometer protocol, a physical assessment, and questionnaires on body image, self-efficacy, motivational readiness, social support, home environment for physical activity and perceived environment. Trained assessors evaluated each participant's neighborhood and collected objective measures of physical activity resources and the pedestrian environment. Assessments were completed between 2006 and 2008. Structural model fit was acceptable (RMSEA=.030). Body composition and image was negatively associated with PA, and motivational readiness had an indirect effect on PA through body composition and image. PA resources and the pedestrian environment operated through the perceived environment to positively influence neighborhood cohesion, which was positively associated with body composition and image. PA is more heavily influenced by intrapersonal factors related to weight. Improving intrapersonal factors related to weight and perceptions of the environment may lead to increased PA in African American and Hispanic women.

An adaptive multi-level simulation algorithm for stochastic biological systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lester, C., E-mail: lesterc@maths.ox.ac.uk; Giles, M. B.; Baker, R. E.

2015-01-14

Discrete-state, continuous-time Markov models are widely used in the modeling of biochemical reaction networks. Their complexity often precludes analytic solution, and we rely on stochastic simulation algorithms (SSA) to estimate system statistics. The Gillespie algorithm is exact, but computationally costly as it simulates every single reaction. As such, approximate stochastic simulation algorithms such as the tau-leap algorithm are often used. Potentially computationally more efficient, the system statistics generated suffer from significant bias unless tau is relatively small, in which case the computational time can be comparable to that of the Gillespie algorithm. The multi-level method [Anderson and Higham, “Multi-level Montemore » Carlo for continuous time Markov chains, with applications in biochemical kinetics,” SIAM Multiscale Model. Simul. 10(1), 146–179 (2012)] tackles this problem. A base estimator is computed using many (cheap) sample paths at low accuracy. The bias inherent in this estimator is then reduced using a number of corrections. Each correction term is estimated using a collection of paired sample paths where one path of each pair is generated at a higher accuracy compared to the other (and so more expensive). By sharing random variables between these paired paths, the variance of each correction estimator can be reduced. This renders the multi-level method very efficient as only a relatively small number of paired paths are required to calculate each correction term. In the original multi-level method, each sample path is simulated using the tau-leap algorithm with a fixed value of τ. This approach can result in poor performance when the reaction activity of a system changes substantially over the timescale of interest. By introducing a novel adaptive time-stepping approach where τ is chosen according to the stochastic behaviour of each sample path, we extend the applicability of the multi-level method to such cases. We demonstrate the efficiency of our method using a number of examples.« less

Multilevel Molecular Modeling Approach for a Rational Design of Ionic Current Sensors for Nanofluidics.

PubMed

Kirch, Alexsandro; de Almeida, James M; Miranda, Caetano R

2018-05-10

The complexity displayed by nanofluidic-based systems involves electronic and dynamic aspects occurring across different size and time scales. To properly model such kind of system, we introduced a top-down multilevel approach, combining molecular dynamics simulations (MD) with first-principles electronic transport calculations. The potential of this technique was demonstrated by investigating how the water and ionic flow through a (6,6) carbon nanotube (CNT) influences its electronic transport properties. We showed that the confinement on the CNT favors the partially hydrated Na, Cl, and Li ions to exchange charge with the nanotube. This leads to a change in the electronic transmittance, allowing for the distinguishing of cations from anions. Such an ionic trace may handle an indirect measurement of the ionic current that is recorded as a sensing output. With this case study, we are able to show the potential of this top-down multilevel approach, to be applied on the design of novel nanofluidic devices.

Affective Balance, Team Prosocial Efficacy and Team Trust: A Multilevel Analysis of Prosocial Behavior in Small Groups

PubMed Central

Cuadrado, Esther; Tabernero, Carmen

2015-01-01

Little research has focused on how individual- and team-level characteristics jointly influence, via interaction, how prosocially individuals behave in teams and few studies have considered the potential influence of team context on prosocial behavior. Using a multilevel perspective, we examined the relationships between individual (affective balance) and group (team prosocial efficacy and team trust) level variables and prosocial behavior towards team members. The participants were 123 students nested in 45 small teams. A series of multilevel random models was estimated using hierarchical linear and nonlinear modeling. Individuals were more likely to behave prosocially towards in-group members when they were feeling good. Furthermore, the relationship between positive affective balance and prosocial behavior was stronger in teams with higher team prosocial efficacy levels as well as in teams with higher team trust levels. Finally, the relevance of team trust had a stronger influence on behavior than team prosocial efficacy. PMID:26317608

Observation and quantification of the quantum dynamics of a strong-field excited multi-level system.

PubMed

Liu, Zuoye; Wang, Quanjun; Ding, Jingjie; Cavaletto, Stefano M; Pfeifer, Thomas; Hu, Bitao

2017-01-04

The quantum dynamics of a V-type three-level system, whose two resonances are first excited by a weak probe pulse and subsequently modified by another strong one, is studied. The quantum dynamics of the multi-level system is closely related to the absorption spectrum of the transmitted probe pulse and its modification manifests itself as a modulation of the absorption line shape. Applying the dipole-control model, the modulation induced by the second strong pulse to the system's dynamics is quantified by eight intensity-dependent parameters, describing the self and inter-state contributions. The present study opens the route to control the quantum dynamics of multi-level systems and to quantify the quantum-control process.

The effects of autonomy and empowerment on employee turnover: test of a multilevel model in teams.

PubMed

Liu, Dong; Zhang, Shu; Wang, Lei; Lee, Thomas W

2011-11-01

Extending research on voluntary turnover in the team setting, this study adopts a multilevel self-determination theoretical approach to examine the unique roles of individual and social-contextual motivational precursors, autonomy orientation and autonomy support, in reducing team member voluntary turnover. Analysis of multilevel time-lagged data collected from 817 employees on 115 teams indicates that psychological empowerment mediates the main effect of autonomy orientation and the interactive effect of autonomy support and its differentiation on a team member's voluntary turnover. The findings have meaningful implications for the turnover and self-determination literatures as well as for managers who endeavor to prevent voluntary turnover in teams. (c) 2011 APA, all rights reserved.

Measuring Latent Quantities

ERIC Educational Resources Information Center

McDonald, Roderick P.

2011-01-01

A distinction is proposed between measures and predictors of latent variables. The discussion addresses the consequences of the distinction for the true-score model, the linear factor model, Structural Equation Models, longitudinal and multilevel models, and item-response models. A distribution-free treatment of calibration and…

Social Context and Dental Pain in Adults of Colombian Ethnic Minority Groups: A Multilevel Cross-Sectional Study.

PubMed

Ardila, Carlos M; Agudelo-Suárez, Andrés A

2016-01-01

To estimate the effect of social context on dental pain in adults of Colombian ethnic minority groups (CEGs). Information from 34,843 participants was used. A multilevel model was constructed that had ethnic groups (ie, CEGs and non-CEGs) at level 1 and Colombian states at level 2. Contextual variables included gross domestic product (GDP), Human Development Index (HDI), and Unmet Basic Needs Index (UBNI). Dental pain was observed in 12.3% of 6,440 CEGs. In an unadjusted logistic regression model, dental pain was associated with being a CEG (odds ratio [95% confidence interval], 1.34 [1.22-1.46]; P = .0001). This association remained significant after adjusting for possible confounding variables. An unconditional multilevel analysis showed that the variance in dental pain was statistically significant at the ethnic group level (β = 0.047 ± 0.015; P = .0009) and at the state level (β = 0.038 ± 0.019; P = .02) and that the variation between ethnic groups was higher than the variation between states (55% vs 45%, respectively). In a multivariate model, the variance in dental pain was also statistically significant at the ethnic group level (β = 0.029 ± 0.012; P = .007) and the state level (β = 0.042 ± .019; P = .01), but the variation between states was higher (40% vs 60%). The results of multilevel multivariate analyses showed that dental pain was associated with increasing age (β = 0.009 ± 0.001; P = .0001), lower education level (β = 0.302 ± 0.103; P = .0001), female sex (β = 0.031 ± 0.069; P = .003), GDP (β = 5.136 ± 2.009; P = .002) and HDI (β = 6.862 ± 5.550; P = .004); however, UBNI was not associated with dental pain. The variance in dental pain was higher between states than between ethnic groups in the multivariate multilevel model. Dental pain in CEGs was associated with contextual and individual factors. Considering contextual factors, GDP and HDI may play a major role in dental pain prevalence.