Explanatory models of psychosis amongst British South Asians.

PubMed

Bhikha, Aqeela; Farooq, Saeed; Chaudhry, Nasim; Naeem, Farooq; Husain, Nusrat

2015-08-01

A strong interest in the understanding, exploring, and extracting explanatory models of psychosis has recently arisen. Explanatory models (EMs) offer justifications and propose explanations when coping with and treating illnesses. Therefore, they may be important predictors of clinical outcome. Explanatory models of psychosis have been explored in many non-Western countries. However, very little research has examined EMs of psychosis in the UK. We therefore, aimed to elicit and describe explanatory models of psychosis amongst British South Asians, using both quantitative and qualitative methods. EMs of psychosis were examined using the Short Explanatory Model Interview (SEMI) in a cross-sectional sample of 45 patients. Most patients (55.5%) attributed their illness to supernatural causes. Few patients cited a biological (4.4%) cause. Majority of patients held dual EMs of psychosis (77.7%), combining prescribed medication and seeing a traditional healer as a treatment method. Duration of Untreated Psychosis (DUP) was not significantly associated with EMs of psychosis. The results suggest that patients hold multi-explanatory models in order to make sense of their illness and these stem from deep rooted traditional beliefs. This highlights the importance of educational intervention, culturally adapted psychological interventions and possibly working together with traditional healers in the UK to provide a positive support system. Further work is required in order to fully understand the relationship between EMs of psychosis and DUP. Copyright © 2015 Elsevier B.V. All rights reserved.

A new standard model for milk yield in dairy cows based on udder physiology at the milking-session level.

PubMed

Gasqui, Patrick; Trommenschlager, Jean-Marie

2017-08-21

Milk production in dairy cow udders is a complex and dynamic physiological process that has resisted explanatory modelling thus far. The current standard model, Wood's model, is empirical in nature, represents yield in daily terms, and was published in 1967. Here, we have developed a dynamic and integrated explanatory model that describes milk yield at the scale of the milking session. Our approach allowed us to formally represent and mathematically relate biological features of known relevance while accounting for stochasticity and conditional elements in the form of explicit hypotheses, which could then be tested and validated using real-life data. Using an explanatory mathematical and biological model to explore a physiological process and pinpoint potential problems (i.e., "problem finding"), it is possible to filter out unimportant variables that can be ignored, retaining only those essential to generating the most realistic model possible. Such modelling efforts are multidisciplinary by necessity. It is also helpful downstream because model results can be compared with observed data, via parameter estimation using maximum likelihood and statistical testing using model residuals. The process in its entirety yields a coherent, robust, and thus repeatable, model.

Modelling sociocognitive aspects of students' learning

NASA Astrophysics Data System (ADS)

Koponen, I. T.; Kokkonen, T.; Nousiainen, M.

2017-03-01

We present a computational model of sociocognitive aspects of learning. The model takes into account a student's individual cognition and sociodynamics of learning. We describe cognitive aspects of learning as foraging for explanations in the epistemic landscape, the structure (set by instructional design) of which guides the cognitive development through success or failure in foraging. We describe sociodynamic aspects as an agent-based model, where agents (learners) compare and adjust their conceptions of their own proficiency (self-proficiency) and that of their peers (peer-proficiency) in using explanatory schemes of different levels. We apply the model here in a case involving a three-tiered system of explanatory schemes, which can serve as a generic description of some well-known cases studied in empirical research on learning. The cognitive dynamics lead to the formation of dynamically robust outcomes of learning, seen as a strong preference for a certain explanatory schemes. The effects of social learning, however, can account for half of one's success in adopting higher-level schemes and greater proficiency. The model also predicts a correlation of dynamically emergent interaction patterns between agents and the learning outcomes.

The Relationship of Explanatory Flexibility to Explanatory Style

ERIC Educational Resources Information Center

Moore, Michael T.; Fresco, David M.

2007-01-01

Traditional cognitive vulnerability-stress models regarding the etiology of depression emphasize the content of the depressed individual's thoughts. One important cognitive content index, explanatory style, represents the habitual way that individuals assign causes to events that occur in their lives. A more contemporary model, however, emphasizes…

Some methodological issues in the longitudinal analysis of demographic data.

PubMed

Krishinan, P

1982-12-01

Most demographic data are macro (or aggregate) in nature. Some relevant methodological issues are presented here in a time series study using aggregate data. The micro-macro distinction is relative. Time enters into the micro and macro variables in different ways. A simple micro model of rural-urban migration is given. Method 1 is to assume homogeneity in behavior. Method 2 is a Bayesian estimation. A discusssion of the results follows. Time series models of aggregate data are given. The nature of the model--predictive or explanatory--must be decided on. Explanatory models in longitudinal studies have been developed. Ways to go to the micro level from the macro are discussed. The aggregation-disaggregation problem in demography is not similar to that in econometrics. To understand small populations, separate micro level data have to be collected and analyzed and appropriate models developed. Both types of models have their uses.

Explanatory Models and Medication Adherence in Patients with Depression in South India

PubMed Central

Siddappa, Adarsh Lakkur; Raman, Rajesh; Hattur, Basavana Gowdappa

2017-01-01

Introduction Conceptualization of depression may have bearing on treatment seeking. It may affect adherence behaviour of the patients. Aim To find out the explanatory models and their relationship with socio-demographic variables and medication adherence in patients with depression. Materials and Methods Fifty-eight consecutive patients with depression in remission were recruited as per selection criteria. Socio-demographic details were collected. Patients were assessed using Mental Distress Explanatory Model Questionnaire (MDEMQ) and Morisky Medication Adherence Scale (MMAS). Results Significant scores were observed in all dimensions of explanatory models. In the Mann-Whitney U test the patient’s marital status (MU=113.500, p=0.05, sig≤0.05, 2-tailed), and family history of mental illness (MU=165.5, p=0.03, sig≤0.05, 2-tailed) had a statistically significant group difference in the score of MDEMQ. In linear regression analysis, four predictors (MDEMQ subscales Stress, Western physiology, Non-Western physiology and Supernatural) had significantly predicted the value of MMAS (R2=0.937, f=153.558, p<0.001). Conclusion Findings of this study suggested that patients with depression harbor multidimensional explanatory model. The levels of explanatory models are inversely associated with levels of medication adherence. PMID:28274025

Agricultural disturbance response models for invertebrate and algal metrics from streams at two spatial scales within the U.S.

USGS Publications Warehouse

Waite, Ian R.

2014-01-01

As part of the USGS study of nutrient enrichment of streams in agricultural regions throughout the United States, about 30 sites within each of eight study areas were selected to capture a gradient of nutrient conditions. The objective was to develop watershed disturbance predictive models for macroinvertebrate and algal metrics at national and three regional landscape scales to obtain a better understanding of important explanatory variables. Explanatory variables in models were generated from landscape data, habitat, and chemistry. Instream nutrient concentration and variables assessing the amount of disturbance to the riparian zone (e.g., percent row crops or percent agriculture) were selected as most important explanatory variable in almost all boosted regression tree models regardless of landscape scale or assemblage. Frequently, TN and TP concentration and riparian agricultural land use variables showed a threshold type response at relatively low values to biotic metrics modeled. Some measure of habitat condition was also commonly selected in the final invertebrate models, though the variable(s) varied across regions. Results suggest national models tended to account for more general landscape/climate differences, while regional models incorporated both broad landscape scale and more specific local-scale variables.

Locating the Social Origins of Mental Illness: The Explanatory Models of Mental Illness Among Clergy from Different Ethnic and Faith Backgrounds.

PubMed

Leavey, Gerard; Loewenthal, Kate; King, Michael

2016-10-01

Clergy have historically provided 'healing' through various spiritual and medical modalities and even in modern, developed welfare economies they may still be an important help-seeking resource. Partnerships between religion and psychiatry are regularly advocated, but there is scant research on clergy explanatory models of illness. This paper aimed to explore their relationship with psychiatry and to examine how clergy in various faith groups conceptualised mental health problems. In this qualitative study using in-depth interviews, these issues were explored with 32 practising clergy in the UK from a range of different Christian, Muslim and Jewish faith organisations and ethnic backgrounds. This paper presents findings related to clergy explanatory models of mental illness and, in particular, how the social factors involved in causation are tinged with spiritual influences and implications, and how the meanings of mental distress assume a social and moral significance in distinctive localised matters.

The Perfect Storm: Preterm Birth, Neurodevelopmental Mechanisms, and Autism Causation.

PubMed

Erdei, Carmina; Dammann, Olaf

2014-01-01

A unifying model of autism causation remains elusive, and thus well-designed explanatory models are needed to develop appropriate therapeutic and preventive interventions. This essay argues that autism is not a static disorder, but rather an ongoing process. We discuss the link between preterm birth and autism and briefly review the evidence supporting the link between immune system characteristics and both prematurity and autism. We then propose a causation process model of autism etiology and pathogenesis, in which both neurodevelopment and ongoing/prolonged neuroinflammation are necessary pathogenetic component mechanisms. We suggest that an existing model of sufficient cause and component causes can be interpreted as a mechanistic view of etiology and pathogenesis and can serve as an explanatory model for autism causal pathways.

Spatial regression analysis on 32 years of total column ozone data

NASA Astrophysics Data System (ADS)

Knibbe, J. S.; van der A, R. J.; de Laat, A. T. J.

2014-08-01

Multiple-regression analyses have been performed on 32 years of total ozone column data that was spatially gridded with a 1 × 1.5° resolution. The total ozone data consist of the MSR (Multi Sensor Reanalysis; 1979-2008) and 2 years of assimilated SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) ozone data (2009-2010). The two-dimensionality in this data set allows us to perform the regressions locally and investigate spatial patterns of regression coefficients and their explanatory power. Seasonal dependencies of ozone on regressors are included in the analysis. A new physically oriented model is developed to parameterize stratospheric ozone. Ozone variations on nonseasonal timescales are parameterized by explanatory variables describing the solar cycle, stratospheric aerosols, the quasi-biennial oscillation (QBO), El Niño-Southern Oscillation (ENSO) and stratospheric alternative halogens which are parameterized by the effective equivalent stratospheric chlorine (EESC). For several explanatory variables, seasonally adjusted versions of these explanatory variables are constructed to account for the difference in their effect on ozone throughout the year. To account for seasonal variation in ozone, explanatory variables describing the polar vortex, geopotential height, potential vorticity and average day length are included. Results of this regression model are compared to that of a similar analysis based on a more commonly applied statistically oriented model. The physically oriented model provides spatial patterns in the regression results for each explanatory variable. The EESC has a significant depleting effect on ozone at mid- and high latitudes, the solar cycle affects ozone positively mostly in the Southern Hemisphere, stratospheric aerosols affect ozone negatively at high northern latitudes, the effect of QBO is positive and negative in the tropics and mid- to high latitudes, respectively, and ENSO affects ozone negatively between 30° N and 30° S, particularly over the Pacific. The contribution of explanatory variables describing seasonal ozone variation is generally large at mid- to high latitudes. We observe ozone increases with potential vorticity and day length and ozone decreases with geopotential height and variable ozone effects due to the polar vortex in regions to the north and south of the polar vortices. Recovery of ozone is identified globally. However, recovery rates and uncertainties strongly depend on choices that can be made in defining the explanatory variables. The application of several trend models, each with their own pros and cons, yields a large range of recovery rate estimates. Overall these results suggest that care has to be taken in determining ozone recovery rates, in particular for the Antarctic ozone hole.

An Explanatory Model of Self-Service on the Internet

NASA Astrophysics Data System (ADS)

Oliver, Dave; Livermore, Celia Romm; Farag, Neveen Awad

This chapter describes research that identifies and classifies the dimensions of self-service activity enabled through the Internet. Self-service is effected by organizations providing ways and means whereby customers perform tasks related to the procurement of goods and services. We describe how an instrument used to measure Internet-based self-service was developed, validated and applied. The results from applying the instrument to a large number of Web sites, covering a range of industries, countries and cultures, are analyzed and discussed. The study presents a model in which type of industry, level of technological development, income and cultural factors are proposed as explanatory variables for Web-based self-service. We conclude with an assessment of this program of research’s achievements so far.

MODELING TREE LEVEL PROCESSES

EPA Science Inventory

An overview of three main types of simulation approach (explanatory, abstraction, and estimation) is presented, along with a discussion of their capabilities limitations, and the steps required for their validation. A process model being developed through the Forest Response Prog...

Predictive Modeling of a Fecal Indicator at a Subtropical Marine Beach

EPA Science Inventory

The Virtual Beach Model Builder (VBMB) is a software tool that can be used to develop predictive models at beaches based on microbial data and observations (explanatory variables) that describe hydrometeorological and biogeochemical conditions. During the summer of 2008, a study...

Body Fat Percentage Prediction Using Intelligent Hybrid Approaches

PubMed Central

Shao, Yuehjen E.

2014-01-01

Excess of body fat often leads to obesity. Obesity is typically associated with serious medical diseases, such as cancer, heart disease, and diabetes. Accordingly, knowing the body fat is an extremely important issue since it affects everyone's health. Although there are several ways to measure the body fat percentage (BFP), the accurate methods are often associated with hassle and/or high costs. Traditional single-stage approaches may use certain body measurements or explanatory variables to predict the BFP. Diverging from existing approaches, this study proposes new intelligent hybrid approaches to obtain fewer explanatory variables, and the proposed forecasting models are able to effectively predict the BFP. The proposed hybrid models consist of multiple regression (MR), artificial neural network (ANN), multivariate adaptive regression splines (MARS), and support vector regression (SVR) techniques. The first stage of the modeling includes the use of MR and MARS to obtain fewer but more important sets of explanatory variables. In the second stage, the remaining important variables are served as inputs for the other forecasting methods. A real dataset was used to demonstrate the development of the proposed hybrid models. The prediction results revealed that the proposed hybrid schemes outperformed the typical, single-stage forecasting models. PMID:24723804

Insight in psychosis: Standards, science, ethics and value judgment.

PubMed

Jacob, K S

2017-06-01

The clinical assessment of insight solely employs biomedical perspectives and criteria to the complete exclusion of context and culture and to the disregard of values and value judgments. The aim of this discussion article is to examine recent research from India on insight and explanatory models in psychosis and re-examine the framework of assessment, diagnosis and management of insight and explanatory models. Recent research from India on insight in psychosis and explanatory models is reviewed. Recent research, which has used longitudinal data and adjusted for pretreatment variables, suggests that insight and explanatory models of illness at baseline do not predict course, outcome and treatment response in schizophrenia, which seem to be dependent on the severity and quality of the psychosis. It supports the view that people with psychosis simultaneously hold multiple and contradictory explanatory models of illness, which change over time and with the trajectory of the illness. It suggests that insight, like all explanatory models, is a narrative of the person's reality and a coping strategy to handle with the varied impact of the illness. This article argues that the assessment of insight necessarily involves value entailments, commitments and consequences. It supports a need for a broad-based approach to assess awareness, attribution and action related to mental illness and to acknowledge the role of values and value judgment in the evaluation of insight in psychosis.

Insight, psychopathology, explanatory models and outcome of schizophrenia in India: a prospective 5-year cohort study.

PubMed

Johnson, Shanthi; Sathyaseelan, Manoranjitham; Charles, Helen; Jeyaseelan, Visalakshi; Jacob, Kuruthukulangara Sebastian

2012-09-27

The sole focus of models of insight on bio-medical perspectives to the complete exclusion of local, non-medical and cultural constructs mandates review. This study attempted to investigate the impact of insight, psychopathology, explanatory models of illness on outcome of first episode schizophrenia. Patients diagnosed to have DSM IV schizophrenia (n = 131) were assessed prospectively for insight, psychopathology, explanatory models of illness at baseline, 6, 12 and 60 months using standard instruments. Multiple linear and logistic regression and generalized estimating equations (GEE) were employed to assess predictors of outcome. We could follow up 95 (72.5%) patients. Sixty-five of these patients (68.4%) achieved remission. There was a negative relationship between psychosis rating and insight scores. Urban residence, fluctuating course of the initial illness, and improvement in global functioning at 6 months and lower psychosis rating at 12 months were significantly related to remission at 5 years. Insight scores, number of non-medical explanatory models and individual explanatory models held during the later course of the illness were significantly associated with outcome. Analysis of longitudinal data using GEE showed that women, rural residence, insight scores and number of non-medical explanatory models of illness held were significantly associated with BPRS scores during the study period. Insight, the disease model and the number of non-medical model positively correlated with improvement in psychosis arguing for a complex interaction between the culture, context and illness variables. These finding argue that insight and explanatory models are secondary to psychopathology, course and outcome of the illness. The awareness of mental illness is a narrative act in which people make personal sense of the many challenges they face. The course and outcome of the illness, cultural context, acceptable cultural explanations and the prevalent social stigma interact to produce a complex and multifaceted understanding of the issues. This complexity calls for a nuanced framing of insight.

Explanatory models in patients with first episode depression: a study from north India.

PubMed

Grover, Sandeep; Kumar, Vineet; Chakrabarti, Subho; Hollikatti, Prabhakar; Singh, Pritpal; Tyagi, Shikha; Kulhara, Parmanand; Avasthi, Ajit

2012-09-01

The purpose of this work was to study the explanatory models of patients with first episode depression presenting to a tertiary care hospital located in North-western India. One hundred sixty four consecutive patients with diagnosis of first episode depression (except severe depression with psychotic symptoms) according to the International Classification of Diseases-10th Revision (ICD-10) and ≥18 years of age were evaluated for their explanatory models using the causal models section of Explanatory Model Interview Catalogue (EMIC). The most common explanations given were categorized into Karma-deed-heredity category (77.4%), followed by psychological explanations (62.2%), weakness (50%) and social causes (40.2%). Among the various specific causes the commonly reported explanations by at least one-fourth of the sample in decreasing order were: will of god (51.2%), fate/chance (40.9%), weakness of nerves (37.8%), general weakness (34.7%), bad deeds (26.2%), evil eye (24.4%) and family problems (21.9%). There was some influence of sociodemographic features on the explanations given by the patients. From the study, it can be concluded that patients with first episode depression have multiple explanatory models for their symptoms of depression which are slightly different than those reported in previous studies done from other parts of India. Understanding the multiple explanatory models for their symptoms of depression can have important treatment implications. Copyright © 2012 Elsevier B.V. All rights reserved.

Parental explanatory models of ADHD: gender and cultural variations.

PubMed

Bussing, Regina; Gary, Faye A; Mills, Terry L; Garvan, Cynthia Wilson

2003-10-01

This study describes parents' explanatory models of Attention Deficit Hyperactivity Disorder (ADHD) and examines model variation by child characteristics. Children with ADHD (N = 182) were identified from a school district population of elementary school students. A reliable coding system was developed for parental responses obtained in ethnographic interviews in order to convert qualitative into numerical data for quantitative analysis. African-American parents were less likely to connect the school system to ADHD problem identification, expressed fewer worries about ADHD-related school problems, and voiced fewer preferences for school interventions than Caucasian parents, pointing to a potential disconnect with the school system. More African-American than Caucasian parents were unsure about potential causes of and treatments for ADHD, indicating a need for culturally appropriate parent education approaches.

Explanatory models of diabetes in urban poor communities in Accra, Ghana.

PubMed

de-Graft Aikins, Ama; Awuah, Raphael Baffour; Pera, Tuula Anneli; Mendez, Montserrat; Ogedegbe, Gbenga

2015-01-01

The objective of the study was to examine explanatory models of diabetes and diabetes complications among urban poor Ghanaians living with diabetes and implications for developing secondary prevention strategies. Twenty adults with type 2 diabetes were recruited from three poor communities in Accra. Qualitative data were obtained using interviews that run between 40 and 90 minutes. The interviews were audio-taped, transcribed and analysed thematically, informed by the 'explanatory model of disease' concept. Respondents associated diabetes and its complications with diet, family history, lifestyle factors (smoking, excessive alcohol consumption and physical inactivity), psychological stress and supernatural factors (witchcraft and sorcery). These associations were informed by biomedical and cultural models of diabetes and disease. Subjective experience, through a process of 'body-listening,' constituted a third model on which respondents drew to theorise diabetes complications. Poverty was an important mediator of poor self-care practices, including treatment non-adherence. The biomedical model of diabetes was a major source of legitimate information for self-care practices. However, this was understood and applied through a complex framework of cultural theories of chronic disease, the biopsychological impact of everyday illness experience and the disempowering effects of poverty. An integrated biopsychosocial approach is proposed for diabetes intervention in this research community.

Anxiety Psychopathology in African American Adults: Literature Review and Development of an Empirically Informed Sociocultural Model

ERIC Educational Resources Information Center

Hunter, Lora Rose; Schmidt, Norman B.

2010-01-01

In this review, the extant literature concerning anxiety psychopathology in African American adults is summarized to develop a testable, explanatory framework with implications for future research. The model was designed to account for purported lower rates of anxiety disorders in African Americans compared to European Americans, along with other…

Consumer-operated service program members' explanatory models of mental illness and recovery.

PubMed

Hoy, Janet M

2014-10-01

Incorporating individuals' understandings and explanations of mental illness into service delivery offers benefits relating to increased service relevance and meaning. Existing research delineates explanatory models of mental illness held by individuals in home, outpatient, and hospital-based contexts; research on models held by those in peer-support contexts is notably absent. In this article, I describe themes identified within and across explanatory models of mental illness and recovery held by mental health consumers (N = 24) at one peer center, referred to as a consumer-operated service center (COSP). Participants held explanatory models inclusive of both developmental stressors and biomedical causes, consistent with a stress-diathesis model (although no participant explicitly referenced such). Explicit incorporation of stress-diathesis constructs into programming at this COSP offers the potential of increasing service meaning and relevance. Identifying and incorporating shared meanings across individuals' understandings of mental illness likewise can increase relevance and meaning for particular subgroups of service users. © The Author(s) 2014.

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…

An Explanatory Model of Teacher Movement within Ontario School Boards

ERIC Educational Resources Information Center

Sibbald, Timothy M.

2017-01-01

Teacher movement within school boards is examined using multiple case study. Emergent themes achieved theoretical saturation and are consistent with the research literature. In this paper, the relationships between the themes are used to develop a substantive theoretical model of teacher movement within school boards. The model uses a two-phase…

Explanatory Supplement to the Astronomical Almanac, Third Edition

NASA Astrophysics Data System (ADS)

Seidelmann, P. Kenneth; Urban, S. E.

2010-01-01

"The Explanatory Supplement to the Astronomical Almanac" (hereafter "The Explanatory Supplement") is a comprehensive reference book on the topic of positional astronomy, covering the theories and algorithms used to produce "The Astronomical Almanac" (AsA), an annual publication produced jointly by the Nautical Almanac Office of the US Naval Observatory (USNO) and Her Majesty's Nautical Almanac Office (HMNAO) of the UK Hydrographic Office. The first edition of The Explanatory Supplement appeared in 1961 and was reprinted with amendments during the 1970s. The second edition was printed in 1992 and reprinted until 2006. Since the second edition, several changes have taken place in positional astronomy regarding reference systems and internationally accepted models, data sets, and computational methods; these have been incorporated into the AsA. Additionally, the data presented in the AsA have been modified over the years, with new tables being added and some being discontinued. Given these changes, a new edition of The Explanatory Supplement is appropriate. The third edition has been in development for the last few years and will be available in 2010. The book is organized similarly to the second (1991) edition, with each chapter written by subject matter experts. Authors from USNO and HMNAO contributed to the majority of the book, but there are authors from Jet Propulsion Laboratory, Technical University of Dresden, National Geospatial-Intelligence Agency, University of Texas Austin, and University of Virginia. This paper will discuss this latest edition of the Explanatory Supplement.

Revising explanatory models to accommodate anomalous genetic phenomena: Problem solving in the context of discovery

NASA Astrophysics Data System (ADS)

Hafner, Robert; Stewart, Jim

Past problem-solving research has provided a basis for helping students structure their knowledge and apply appropriate problem-solving strategies to solve problems for which their knowledge (or mental models) of scientific phenomena is adequate (model-using problem solving). This research examines how problem solving in the domain of Mendelian genetics proceeds in situations where solvers' mental models are insufficient to solve problems at hand (model-revising problem solving). Such situations require solvers to use existing models to recognize anomalous data and to revise those models to accommodate the data. The study was conducted in the context of 9-week high school genetics course and addressed: the heuristics charactenstic of successful model-revising problem solving: the nature of the model revisions, made by students as well as the nature of model development across problem types; and the basis upon which solvers decide that a revised model is sufficient (that t has both predictive and explanatory power).

Understanding burnout according to individual differences: ongoing explanatory power evaluation of two models for measuring burnout types

PubMed Central

2012-01-01

Background The classic determination of burnout is by means of the dimensions exhaustion, cynicism and inefficacy. A new definition of the syndrome is based on clinical subtypes, consisting of “frenetic” (involved, ambitious, overloaded), “underchallenged” (indifferent, bored, with lack of personal development) and “worn-out” (neglectful, unacknowledged, with little control). The dimensions of overload, lack of development and neglect form a shortened version of this perspective. The aims of this study were to estimate and to compare the explanatory power of both typological models, short and long, with the standard measurement. Methods This was a cross-sectional survey with a randomly sample of university employees (n=409). Multivariate linear regression models were constructed between the “Maslach Burnout Inventory General Survey” (MBI-GS) dimensions, as dependent variables, and the “Burnout Clinical Subtype Questionnaire” (BCSQ-36 and BCSQ-12) dimensions, as independent variables. Results The BCSQ-36 subscales together explained 53% of ‘exhaustion’ (p<0.001), 59% of ‘cynicism’ (p<0.001) and 37% of ‘efficacy’ (p<0.001), while BCSQ-12 subscales explained 44% of ‘exhaustion’ (p<0.001), 44% of ‘cynicism’ (p<0.001), and 30% of ‘efficacy’ (p<0.001). The difference in the explanatory power of both models was significant for ‘exhaustion’ (p<0.001), and for ‘cynicism’ (p<0.001) and ‘efficacy (p<0.001). Conclusions Both BCSQ-36 and BCSQ-12 demonstrate great explanatory power over the standard MBI-GS, while offering a useful characterization of the syndrome for the evaluation and design of interventions tailored to the characteristics of each individual. The BCSQ-36 may be very useful in mental health services, given that it provides a good deal of information, while the BCSQ-12 could be used as a screening measure in primary care consultations owing to its simplicity and functional nature. PMID:23110723

Pathological Left-Handedness: An Explanatory Model.

ERIC Educational Resources Information Center

Satz, Paul

Reported was an explanatory conceptual model for pathological left-handedness (PLH) and related hypotheses, some of which could not be tested empirically due to lack of information. The model was reported to provide an explanation for the relationship between handedness and specific learning disability, and handedness and cerebral dominance for…

Learning Molecular Behaviour May Improve Student Explanatory Models of the Greenhouse Effect

ERIC Educational Resources Information Center

Harris, Sara E.; Gold, Anne U.

2018-01-01

We assessed undergraduates' representations of the greenhouse effect, based on student-generated concept sketches, before and after a 30-min constructivist lesson. Principal component analysis of features in student sketches revealed seven distinct and coherent explanatory models including a new "Molecular Details" model. After the…

Modelling Analysis of Students' Processes of Generating Scientific Explanatory Hypotheses

ERIC Educational Resources Information Center

Park, Jongwon

2006-01-01

It has recently been determined that generating an explanatory hypothesis to explain a discrepant event is important for students' conceptual change. The purpose of this study is to investigate how students' generate new explanatory hypotheses. To achieve this goal, questions are used to identify students prior ideas related to electromagnetic…

Use of real-time monitoring to predict concentrations of select constituents in the Menomonee River drainage basin, Southeast Wisconsin, 2008-9

USGS Publications Warehouse

Baldwin, Austin K.; Graczyk, David J.; Robertson, Dale M.; Saad, David A.; Magruder, Christopher

2012-01-01

The models to estimate chloride concentrations all used specific conductance as the explanatory variable, except for the model for the Little Menomonee River near Freistadt, which used both specific conductance and turbidity as explanatory variables. Adjusted R2 values for the chloride models ranged from 0.74 to 0.97. Models to estimate total suspended solids and total phosphorus used turbidity as the only explanatory variable. Adjusted R2 values ranged from 0.77 to 0.94 for the total suspended solids models and from 0.55 to 0.75 for the total phosphorus models. Models to estimate indicator bacteria used water temperature and turbidity as the explanatory variables, with adjusted R2 values from 0.54 to 0.69 for Escherichia coli bacteria models and from 0.54 to 0.74 for fecal coliform bacteria models. Dissolved oxygen was not used in any of the final models. These models may help managers measure the effects of land-use changes and improvement projects, establish total maximum daily loads, estimate important water-quality indicators such as bacteria concentrations, and enable informed decision making in the future.

Development and Application of Regression Models for Estimating Nutrient Concentrations in Streams of the Conterminous United States, 1992-2001

USGS Publications Warehouse

Spahr, Norman E.; Mueller, David K.; Wolock, David M.; Hitt, Kerie J.; Gronberg, JoAnn M.

2010-01-01

Data collected for the U.S. Geological Survey National Water-Quality Assessment program from 1992-2001 were used to investigate the relations between nutrient concentrations and nutrient sources, hydrology, and basin characteristics. Regression models were developed to estimate annual flow-weighted concentrations of total nitrogen and total phosphorus using explanatory variables derived from currently available national ancillary data. Different total-nitrogen regression models were used for agricultural (25 percent or more of basin area classified as agricultural land use) and nonagricultural basins. Atmospheric, fertilizer, and manure inputs of nitrogen, percent sand in soil, subsurface drainage, overland flow, mean annual precipitation, and percent undeveloped area were significant variables in the agricultural basin total nitrogen model. Significant explanatory variables in the nonagricultural total nitrogen model were total nonpoint-source nitrogen input (sum of nitrogen from manure, fertilizer, and atmospheric deposition), population density, mean annual runoff, and percent base flow. The concentrations of nutrients derived from regression (CONDOR) models were applied to drainage basins associated with the U.S. Environmental Protection Agency (USEPA) River Reach File (RF1) to predict flow-weighted mean annual total nitrogen concentrations for the conterminous United States. The majority of stream miles in the Nation have predicted concentrations less than 5 milligrams per liter. Concentrations greater than 5 milligrams per liter were predicted for a broad area extending from Ohio to eastern Nebraska, areas spatially associated with greater application of fertilizer and manure. Probabilities that mean annual total-nitrogen concentrations exceed the USEPA regional nutrient criteria were determined by incorporating model prediction uncertainty. In all nutrient regions where criteria have been established, there is at least a 50 percent probability of exceeding the criteria in more than half of the stream miles. Dividing calibration sites into agricultural and nonagricultural groups did not improve the explanatory capability for total phosphorus models. The group of explanatory variables that yielded the lowest model error for mean annual total phosphorus concentrations includes phosphorus input from manure, population density, amounts of range land and forest land, percent sand in soil, and percent base flow. However, the large unexplained variability and associated model error precluded the use of the total phosphorus model for nationwide extrapolations.

Separating a Mixture

ERIC Educational Resources Information Center

Lotter, Christine; Taylor, Laurie

2016-01-01

In the 2 day lesson presented in this article, students explain how ionic substances interact in solutions by developing and revising their own explanatory models. The lesson engaged students in three-dimensional learning through creating and revising their own models to explain the interaction of ionic substances and polar molecules in a closed…

Establishing an Explanatory Model for Mathematics Identity

ERIC Educational Resources Information Center

Cribbs, Jennifer D.; Hazari, Zahra; Sonnert, Gerhard; Sadler, Philip M.

2015-01-01

This article empirically tests a previously developed theoretical framework for mathematics identity based on students' beliefs. The study employs data from more than 9,000 college calculus students across the United States to build a robust structural equation model. While it is generally thought that students' beliefs about their own competence…

Bayesian Adaptive Lasso for Ordinal Regression with Latent Variables

ERIC Educational Resources Information Center

Feng, Xiang-Nan; Wu, Hao-Tian; Song, Xin-Yuan

2017-01-01

We consider an ordinal regression model with latent variables to investigate the effects of observable and latent explanatory variables on the ordinal responses of interest. Each latent variable is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a Bayesian adaptive lasso procedure to conduct…

Linking Adverse Childhood Effects and Attachment: A Theory of Etiology for Sexual Offending.

PubMed

Grady, Melissa D; Levenson, Jill S; Bolder, Tess

2016-01-25

Sexual violence continues to be a significant public health problem affecting significant portions of the population. Unfortunately, an agreed upon theory of etiology remains elusive leading to challenges in developing effective prevention and treatment interventions. Recently, there is a growing body of literature examining the role of adverse childhood experiences (ACEs) in the development of sexually violent behavior. This research has begun to explore the rates of various types of child maltreatments and family dysfunction in individuals who have been convicted of a sexual crime. These empirical inquiries have been primarily descriptive in nature and have not yet provided a cohesive theoretical model as to why the presence of ACEs might contribute to sexually abusive behavior. This article suggests that attachment theory offers an explanatory link between early adversity and sexually abusive behavior in adulthood. We first summarize important attachment theory concepts, then integrate them with research in the area of developmental psychopathology and ACEs, and finally propose a model by which attachment can be used as an explanatory theory for subsequent sexualized coping and sexually abusive behaviors. Finally, this article explores the implications for practice, policy, and research using this explanatory theory as a framework for understanding sexual violence. © The Author(s) 2016.

Cognitive Modeling for Agent-Based Simulation of Child Maltreatment

NASA Astrophysics Data System (ADS)

Hu, Xiaolin; Puddy, Richard

This paper extends previous work to develop cognitive modeling for agent-based simulation of child maltreatment (CM). The developed model is inspired from parental efficacy, parenting stress, and the theory of planned behavior. It provides an explanatory, process-oriented model of CM and incorporates causality relationship and feedback loops from different factors in the social ecology in order for simulating the dynamics of CM. We describe the model and present simulation results to demonstrate the features of this model.

Belief models in first episode schizophrenia in South India.

PubMed

Saravanan, Balasubramanian; Jacob, K S; Johnson, Shanthi; Prince, Martin; Bhugra, Dinesh; David, Anthony S

2007-06-01

Existing evidence indicates that dissonance between patients' and professionals' explanatory models affects engagement of patients with psychiatric services in Western and non-Western countries. To assess qualitatively the explanatory models (EMs) of psychosis and their association with clinical variables in a representative sample of first episode patients with schizophrenia in South India. One hundred and thirty one patients with schizophrenia presenting consecutively were assessed. Measures included the patient's explanatory models, and clinician ratings of insight, symptoms of psychosis, and functioning on standard scales. The majority of patients (70%) considered spiritual and mystical factors as the cause of their predicament; 22% held multiple models of illness. Patients who held a biomedical concept of disease had significantly higher scores on the insight scale compared to those who held non-medical beliefs. Multivariate analyses identified three factors associated with holding of spiritual/mystical models (female sex, low education and visits to traditional healers); and a single factor (high level of insight) for the endorsement of biological model. Patients with schizophrenia in this region of India hold a variety of non-medical belief models, which influence patterns of health seeking. Those holding non-medical explanatory models are likey to be rated as having less insight.

Explanatory Model for Sound Amplification in a Stethoscope

ERIC Educational Resources Information Center

Eshach, H.; Volfson, A.

2015-01-01

In the present paper we suggest an original physical explanatory model that explains the mechanism of the sound amplification process in a stethoscope. We discuss the amplification of a single pulse, a continuous wave of certain frequency, and finally we address the resonant frequencies. It is our belief that this model may provide students with…

On the predictive ability of mechanistic models for the Haitian cholera epidemic.

PubMed

Mari, Lorenzo; Bertuzzo, Enrico; Finger, Flavio; Casagrandi, Renato; Gatto, Marino; Rinaldo, Andrea

2015-03-06

Predictive models of epidemic cholera need to resolve at suitable aggregation levels spatial data pertaining to local communities, epidemiological records, hydrologic drivers, waterways, patterns of human mobility and proxies of exposure rates. We address the above issue in a formal model comparison framework and provide a quantitative assessment of the explanatory and predictive abilities of various model settings with different spatial aggregation levels and coupling mechanisms. Reference is made to records of the recent Haiti cholera epidemics. Our intensive computations and objective model comparisons show that spatially explicit models accounting for spatial connections have better explanatory power than spatially disconnected ones for short-to-intermediate calibration windows, while parsimonious, spatially disconnected models perform better with long training sets. On average, spatially connected models show better predictive ability than disconnected ones. We suggest limits and validity of the various approaches and discuss the pathway towards the development of case-specific predictive tools in the context of emergency management. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

A model for field toxicity tests

USGS Publications Warehouse

Kaiser, Mark S.; Finger, Susan E.

1996-01-01

Toxicity tests conducted under field conditions present an interesting challenge for statistical modelling. In contrast to laboratory tests, the concentrations of potential toxicants are not held constant over the test. In addition, the number and identity of toxicants that belong in a model as explanatory factors are not known and must be determined through a model selection process. We present one model to deal with these needs. This model takes the record of mortalities to form a multinomial distribution in which parameters are modelled as products of conditional daily survival probabilities. These conditional probabilities are in turn modelled as logistic functions of the explanatory factors. The model incorporates lagged values of the explanatory factors to deal with changes in the pattern of mortalities over time. The issue of model selection and assessment is approached through the use of generalized information criteria and power divergence goodness-of-fit tests. These model selection criteria are applied in a cross-validation scheme designed to assess the ability of a model to both fit data used in estimation and predict data deleted from the estimation data set. The example presented demonstrates the need for inclusion of lagged values of the explanatory factors and suggests that penalized likelihood criteria may not provide adequate protection against overparameterized models in model selection.

Prediction of 222Rn in Danish dwellings using geology and house construction information from central databases.

PubMed

Andersen, Claus E; Raaschou-Nielsen, Ole; Andersen, Helle Primdal; Lind, Morten; Gravesen, Peter; Thomsen, Birthe L; Ulbak, Kaare

2007-01-01

A linear regression model has been developed for the prediction of indoor (222)Rn in Danish houses. The model provides proxy radon concentrations for about 21,000 houses in a Danish case-control study on the possible association between residential radon and childhood cancer (primarily leukaemia). The model was calibrated against radon measurements in 3116 houses. An independent dataset with 788 house measurements was used for model performance assessment. The model includes nine explanatory variables, of which the most important ones are house type and geology. All explanatory variables are available from central databases. The model was fitted to log-transformed radon concentrations and it has an R(2) of 40%. The uncertainty associated with individual predictions of (untransformed) radon concentrations is about a factor of 2.0 (one standard deviation). The comparison with the independent test data shows that the model makes sound predictions and that errors of radon predictions are only weakly correlated with the estimates themselves (R(2) = 10%).

Dose-Response Calculator for ArcGIS

USGS Publications Warehouse

Hanser, Steven E.; Aldridge, Cameron L.; Leu, Matthias; Nielsen, Scott E.

2011-01-01

The Dose-Response Calculator for ArcGIS is a tool that extends the Environmental Systems Research Institute (ESRI) ArcGIS 10 Desktop application to aid with the visualization of relationships between two raster GIS datasets. A dose-response curve is a line graph commonly used in medical research to examine the effects of different dosage rates of a drug or chemical (for example, carcinogen) on an outcome of interest (for example, cell mutations) (Russell and others, 1982). Dose-response curves have recently been used in ecological studies to examine the influence of an explanatory dose variable (for example, percentage of habitat cover, distance to disturbance) on a predicted response (for example, survival, probability of occurrence, abundance) (Aldridge and others, 2008). These dose curves have been created by calculating the predicted response value from a statistical model at different levels of the explanatory dose variable while holding values of other explanatory variables constant. Curves (plots) developed using the Dose-Response Calculator overcome the need to hold variables constant by using values extracted from the predicted response surface of a spatially explicit statistical model fit in a GIS, which include the variation of all explanatory variables, to visualize the univariate response to the dose variable. Application of the Dose-Response Calculator can be extended beyond the assessment of statistical model predictions and may be used to visualize the relationship between any two raster GIS datasets (see example in tool instructions). This tool generates tabular data for use in further exploration of dose-response relationships and a graph of the dose-response curve.

Validation of an Evaluation Model for Learning Management Systems

ERIC Educational Resources Information Center

Kim, S. W.; Lee, M. G.

2008-01-01

This study aims to validate a model for evaluating learning management systems (LMS) used in e-learning fields. A survey of 163 e-learning experts, regarding 81 validation items developed through literature review, was used to ascertain the importance of the criteria. A concise list of explanatory constructs, including two principle factors, was…

Assessing Students' Deep Conceptual Understanding in Physical Sciences: An Example on Sinking and Floating

ERIC Educational Resources Information Center

Shen, Ji; Liu, Ou Lydia; Chang, Hsin-Yi

2017-01-01

This paper presents a transformative modeling framework that guides the development of assessment to measure students' deep understanding in physical sciences. The framework emphasizes 3 types of connections that students need to make when learning physical sciences: (1) linking physical states, processes, and explanatory models, (2) integrating…

Evaluation of alternative model selection criteria in the analysis of unimodal response curves using CART

USGS Publications Warehouse

Ribic, C.A.; Miller, T.W.

1998-01-01

We investigated CART performance with a unimodal response curve for one continuous response and four continuous explanatory variables, where two variables were important (ie directly related to the response) and the other two were not. We explored performance under three relationship strengths and two explanatory variable conditions: equal importance and one variable four times as important as the other. We compared CART variable selection performance using three tree-selection rules ('minimum risk', 'minimum risk complexity', 'one standard error') to stepwise polynomial ordinary least squares (OLS) under four sample size conditions. The one-standard-error and minimum-risk-complexity methods performed about as well as stepwise OLS with large sample sizes when the relationship was strong. With weaker relationships, equally important explanatory variables and larger sample sizes, the one-standard-error and minimum-risk-complexity rules performed better than stepwise OLS. With weaker relationships and explanatory variables of unequal importance, tree-structured methods did not perform as well as stepwise OLS. Comparing performance within tree-structured methods, with a strong relationship and equally important explanatory variables, the one-standard-error-rule was more likely to choose the correct model than were the other tree-selection rules 1) with weaker relationships and equally important explanatory variables; and 2) under all relationship strengths when explanatory variables were of unequal importance and sample sizes were lower.

A Mixed Methods Investigation of Caregiver Coaching in an Early Intervention Model: Differences in Providers for Children with Hearing Loss

ERIC Educational Resources Information Center

King, Alison R.

2017-01-01

The purpose of this research is to investigate the relationship between early intervention providers' backgrounds, and their perceptions of caregiver coaching and auditory skill development, to develop professional development programs. An explanatory sequential design was used with participants of varying backgrounds and experience. In the first…

Child Psychopathy: Theories, Measurement, and Relations with the Development and Persistence of Conduct Problems

ERIC Educational Resources Information Center

Kotler, Julie S.; McMahon, Robert J.

2005-01-01

To develop more accurate explanatory and predictive models of child and adolescent conduct problems, interest has grown in examining psychopathic traits in youth. The presence or absence of these traits may help to identify unique etiological pathways in the development of antisocial behavior. The current review provides a detailed summary and…

Explanatory Models of Illness: A Study of Within-Culture Variation

ERIC Educational Resources Information Center

Lynch, Elizabeth; Medin, Douglas

2006-01-01

The current studies explore causal models of heart attack and depression generated from American healers whom use distinct explanatory frameworks. Causal chains leading to two illnesses, heart attack and depression, were elicited from participant groups: registered nurses (RNs), energy healers, RN energy healers, and undergraduates. The…

Analyzing Science Teaching: A Case Study Based on Three Philosophical Models of Teaching. The Explanatory Modes Project, Background Paper No. 5.

ERIC Educational Resources Information Center

Munby, A. Hugh

The development of a category scheme for the systematic analysis of science classroom discourse is described. Three teaching models are discussed: the Impression Model, which depicts the mind of a student as receiving and storing external impressions; the Insight Model, which denies the possibility that ideas or knowledge can be conveyed by…

Helping Lower Secondary Students Develop Conceptual Understanding of Electrostatic Forces

ERIC Educational Resources Information Center

Moynihan, Richard; van Kampen, Paul; Finlayson, Odilla; McLoughlin, Eilish

2016-01-01

This article describes the development of a lesson sequence that supports secondary-level students to construct an explanatory model for electrostatic attraction using a guided enquiry method. The students examine electrostatic interactions at a macro level and explain the phenomena at the atomic level. Pre-tests, post-tests, homework assignments…

An Analysis of the Relationship between the Organizational Culture and the Performance of Staff Work Groups in Schools and the Development of an Explanatory Model

ERIC Educational Resources Information Center

James, Chris; Connolly, Michael

2009-01-01

This article analyses the concept of organizational culture and the relationship between the organizational culture and the performance of staff work groups in schools. The article draws upon a study of 12 schools in Wales, UK, which despite being in disadvantaged settings have high levels of pupil attainment. A model is developed linking the…

Analysis of low flows and selected methods for estimating low-flow characteristics at partial-record and ungaged stream sites in western Washington

USGS Publications Warehouse

Curran, Christopher A.; Eng, Ken; Konrad, Christopher P.

2012-01-01

Regional low-flow regression models for estimating Q7,10 at ungaged stream sites are developed from the records of daily discharge at 65 continuous gaging stations (including 22 discontinued gaging stations) for the purpose of evaluating explanatory variables. By incorporating the base-flow recession time constant τ as an explanatory variable in the regression model, the root-mean square error for estimating Q7,10 at ungaged sites can be lowered to 72 percent (for known values of τ), which is 42 percent less than if only basin area and mean annual precipitation are used as explanatory variables. If partial-record sites are included in the regression data set, τ must be estimated from pairs of discharge measurements made during continuous periods of declining low flows. Eight measurement pairs are optimal for estimating τ at partial-record sites, and result in a lowering of the root-mean square error by 25 percent. A low-flow survey strategy that includes paired measurements at partial-record sites requires additional effort and planning beyond a standard strategy, but could be used to enhance regional estimates of τ and potentially reduce the error of regional regression models for estimating low-flow characteristics at ungaged sites.

A Philosophical Perspective on Evolutionary Systems Biology

PubMed Central

Soyer, Orkun S.; Siegal, Mark L.

2015-01-01

Evolutionary systems biology (ESB) is an emerging hybrid approach that integrates methods, models, and data from evolutionary and systems biology. Drawing on themes that arose at a cross-disciplinary meeting on ESB in 2013, we discuss in detail some of the explanatory friction that arises in the interaction between evolutionary and systems biology. These tensions appear because of different modeling approaches, diverse explanatory aims and strategies, and divergent views about the scope of the evolutionary synthesis. We locate these discussions in the context of long-running philosophical deliberations on explanation, modeling, and theoretical synthesis. We show how many of the issues central to ESB’s progress can be understood as general philosophical problems. The benefits of addressing these philosophical issues feed back into philosophy too, because ESB provides excellent examples of scientific practice for the development of philosophy of science and philosophy of biology. PMID:26085823

Parental Explanatory Models of Child's Intellectual Disability: A Q Methodology Study

ERIC Educational Resources Information Center

John, Aesha; Montgomery, Diane

2016-01-01

This study with families caring for an individual with an intellectual disability in a mid-sized Indian city explored the diverse explanatory models that parents constructed of causes, preferred treatment approaches and perceived social effects of their child's intellectual disability. Seventeen mothers and three fathers rank ordered 48 disability…

Predictive and Explanatory Relationship Model between Procrastination, Motivation, Anxiety and Academic Achievement

ERIC Educational Resources Information Center

Akpur, Ugur

2017-01-01

Purpose: The purpose of this study is to determine the predictive and explanatory relationship model between procrastination, motivation, anxiety and academic achievement of university students. Research Methods: In this study, a causal research design was used. The study group consisted of 211 participants. In order to determine their motivation…

Using Vygotsky's Zone of Proximal Development to Propose and Test an Explanatory Model for Conceptualising Coteaching in Pre-Service Science Teacher Education

ERIC Educational Resources Information Center

Murphy, Colette; Scantlebury, Kathryn; Milne, Catherine

2015-01-01

Coteaching offers a model for the school-placement element of pre-service science teacher education, based on its demonstrated positive impacts on lessening classroom anxiety, supporting inquiry-based science teaching, improving students' attitudes, and addressing diversity effectively in science classrooms. Coteaching between pre-service and…

The use of process models to inform and improve statistical models of nitrate occurrence, Great Miami River Basin, southwestern Ohio

USGS Publications Warehouse

Walter, Donald A.; Starn, J. Jeffrey

2013-01-01

Statistical models of nitrate occurrence in the glacial aquifer system of the northern United States, developed by the U.S. Geological Survey, use observed relations between nitrate concentrations and sets of explanatory variables—representing well-construction, environmental, and source characteristics— to predict the probability that nitrate, as nitrogen, will exceed a threshold concentration. However, the models do not explicitly account for the processes that control the transport of nitrogen from surface sources to a pumped well and use area-weighted mean spatial variables computed from within a circular buffer around the well as a simplified source-area conceptualization. The use of models that explicitly represent physical-transport processes can inform and, potentially, improve these statistical models. Specifically, groundwater-flow models simulate advective transport—predominant in many surficial aquifers— and can contribute to the refinement of the statistical models by (1) providing for improved, physically based representations of a source area to a well, and (2) allowing for more detailed estimates of environmental variables. A source area to a well, known as a contributing recharge area, represents the area at the water table that contributes recharge to a pumped well; a well pumped at a volumetric rate equal to the amount of recharge through a circular buffer will result in a contributing recharge area that is the same size as the buffer but has a shape that is a function of the hydrologic setting. These volume-equivalent contributing recharge areas will approximate circular buffers in areas of relatively flat hydraulic gradients, such as near groundwater divides, but in areas with steep hydraulic gradients will be elongated in the upgradient direction and agree less with the corresponding circular buffers. The degree to which process-model-estimated contributing recharge areas, which simulate advective transport and therefore account for local hydrologic settings, would inform and improve the development of statistical models can be implicitly estimated by evaluating the differences between explanatory variables estimated from the contributing recharge areas and the circular buffers used to develop existing statistical models. The larger the difference in estimated variables, the more likely that statistical models would be changed, and presumably improved, if explanatory variables estimated from contributing recharge areas were used in model development. Comparing model predictions from the two sets of estimated variables would further quantify—albeit implicitly—how an improved, physically based estimate of explanatory variables would be reflected in model predictions. Differences between the two sets of estimated explanatory variables and resultant model predictions vary spatially; greater differences are associated with areas of steep hydraulic gradients. A direct comparison, however, would require the development of a separate set of statistical models using explanatory variables from contributing recharge areas. Area-weighted means of three environmental variables—silt content, alfisol content, and depth to water from the U.S. Department of Agriculture State Soil Geographic (STATSGO) data—and one nitrogen-source variable (fertilizer-application rate from county data mapped to Enhanced National Land Cover Data 1992 (NLCDe 92) agricultural land use) can vary substantially between circular buffers and volume-equivalent contributing recharge areas and among contributing recharge areas for different sets of well variables. The differences in estimated explanatory variables are a function of the same factors affecting the contributing recharge areas as well as the spatial resolution and local distribution of the underlying spatial data. As a result, differences in estimated variables between circular buffers and contributing recharge areas are complex and site specific as evidenced by differences in estimated variables for circular buffers and contributing recharge areas of existing public-supply and network wells in the Great Miami River Basin. Large differences in areaweighted mean environmental variables are observed at the basin scale, determined by using the network of uniformly spaced hypothetical wells; the differences have a spatial pattern that generally is similar to spatial patterns in the underlying STATSGO data. Generally, the largest differences were observed for area-weighted nitrogen-application rate from county and national land-use data; the basin-scale differences ranged from -1,600 (indicating a larger value from within the volume-equivalent contributing recharge area) to 1,900 kilograms per year (kg/yr); the range in the underlying spatial data was from 0 to 2,200 kg/yr. Silt content, alfisol content, and nitrogen-application rate are defined by the underlying spatial data and are external to the groundwater system; however, depth to water is an environmental variable that can be estimated in more detail and, presumably, in a more physically based manner using a groundwater-flow model than using the spatial data. Model-calculated depths to water within circular buffers in the Great Miami River Basin differed substantially from values derived from the spatial data and had a much larger range. Differences in estimates of area-weighted spatial variables result in corresponding differences in predictions of nitrate occurrence in the aquifer. In addition to the factors affecting contributing recharge areas and estimated explanatory variables, differences in predictions also are a function of the specific set of explanatory variables used and the fitted slope coefficients in a given model. For models that predicted the probability of exceeding 1 and 4 milligrams per liter as nitrogen (mg/L as N), predicted probabilities using variables estimated from circular buffers and contributing recharge areas generally were correlated but differed significantly at the local and basin scale. The scale and distribution of prediction differences can be explained by the underlying differences in the estimated variables and the relative weight of the variables in the statistical models. Differences in predictions of exceeding 1 mg/L as N, which only includes environmental variables, generally correlated with the underlying differences in STATSGO data, whereas differences in exceeding 4 mg/L as N were more spatially extensive because that model included environmental and nitrogen-source variables. Using depths to water from within circular buffers derived from the spatial data and depths to water within the circular buffers calculated from the groundwater-flow model, restricted to the same range, resulted in large differences in predicted probabilities. The differences in estimated explanatory variables between contributing recharge areas and circular buffers indicate incorporation of physically based contributing recharge area likely would result in a different set of explanatory variables and an improved set of statistical models. The use of a groundwater-flow model to improve representations of source areas or to provide more-detailed estimates of specific explanatory variables includes a number of limitations and technical considerations. An assumption in these analyses is that (1) there is a state of mass balance between recharge and pumping, and (2) transport to a pumped well is under a steady state flow field. Comparison of volumeequivalent contributing recharge areas under steady-state and transient transport conditions at a location in the southeastern part of the basin shows the steady-state contributing recharge area is a reasonable approximation of the transient contributing recharge area after between 10 and 20 years of pumping. The first assumption is a more important consideration for this analysis. A gradient effect refers to a condition where simulated pumping from a well is less than recharge through the corresponding contributing recharge area. This generally takes place in areas with steep hydraulic gradients, such as near discharge locations, and can be mitigated using a finer model discretization. A boundary effect refers to a condition where recharge through the contributing recharge area is less than pumping. This indicates other sources of water to the simulated well and could reflect a real hydrologic process. In the Great Miami River Basin, large gradient and boundary effects—defined as the balance between pumping and recharge being less than half—occurred in 5 and 14 percent of the basin, respectively. The agreement between circular buffers and volume-equivalent contributing recharge areas, differences in estimated variables, and the effect on statisticalmodel predictions between the population of wells with a balance between pumping and recharge within 10 percent and the population of all wells were similar. This indicated process-model limitations did not affect the overall findings in the Great Miami River Basin; however, this would be model specific, and prudent use of a process model needs to entail a limitations analysis and, if necessary, alterations to the model.

Measuring change for a multidimensional test using a generalized explanatory longitudinal item response model.

PubMed

Cho, Sun-Joo; Athay, Michele; Preacher, Kristopher J

2013-05-01

Even though many educational and psychological tests are known to be multidimensional, little research has been done to address how to measure individual differences in change within an item response theory framework. In this paper, we suggest a generalized explanatory longitudinal item response model to measure individual differences in change. New longitudinal models for multidimensional tests and existing models for unidimensional tests are presented within this framework and implemented with software developed for generalized linear models. In addition to the measurement of change, the longitudinal models we present can also be used to explain individual differences in change scores for person groups (e.g., learning disabled students versus non-learning disabled students) and to model differences in item difficulties across item groups (e.g., number operation, measurement, and representation item groups in a mathematics test). An empirical example illustrates the use of the various models for measuring individual differences in change when there are person groups and multiple skill domains which lead to multidimensionality at a time point. © 2012 The British Psychological Society.

Constructing and De-Constructing Cultural Values: An Explanatory Model of Teaching Behaviours.

ERIC Educational Resources Information Center

Boufoy-Bastick, Beatrice

This paper presents an explanatory model of cultural behaviors, which resulted from a 4-year ethnographic study of the different academic attainments in English of indigenous Fijians and the Indo-Fijians in the Fiji Islands. Fiji is a natural laboratory for investigating differential cultural behaviors because of these two culturally distinct main…

Why Do Adolescents Use Drugs? A Common Sense Explanatory Model from the Social Actor's Perspective

ERIC Educational Resources Information Center

Nuno-Gutierrez, Bertha Lidia; Rodriguez-Cerda, Oscar; Alvarez-Nemegyei, Jose

2006-01-01

Analysis was made of the common sense explanations of 60 Mexican teenage illicit drug users in rehabilitation to determine their drug use debut. The explanatory model was separated into three blocks, two of which contained common sense aspects: interaction between subject's plane and the collectivity; and relationship between subject's interior…

Divergent Explanatory Production (DEP): The Relationship between Resilience and Creativity

ERIC Educational Resources Information Center

Hernández, Óscar Sánchez; Méndez, Francisco Xavier; Garber, Judy

2015-01-01

Introduction: The aim of the study is to describe and analyze a new test and construct, Divergent Explanatory Production (DEP), defined as the ability to observe adverse situations from various points of view. At the theoretical level, it is a bridge between the reformulated model of learned helplessness (as a resilience model), and creative…

Using Students' Explanatory Models as Sources of Feedback: Conceptualizing Ocean Acidification and Its Impacts

NASA Astrophysics Data System (ADS)

Sezen-Barrie, A.; Stapleton, M.; Wolfson, J.

2017-12-01

This qualitative study focuses on students evidence-based explanatory models on how ocean acidification impacts oysters. Explanatory models are the crucial components of scientific endeavors as it helps scientists explain how the natural world functions and the reasons for the ways it functions. Moreover, these models assemble individual practices to understand how they work together to reach clear conclusions through scientific investigations. Due to their critical roles in making sense of authentic science, recent studies in science education suggest that these models should be part of the curriculum aligned with new science standards, i.e. Next Generation Science Standards, which stress the importance of engaging students in scientific practices. By collecting data from 400 secondary school students in Maryland, we aim to respond to the question: How can we use secondary school students' explanatory models to provide students with constructive feedback for more comprehensive learning of ocean acidification (the related evidence, causes and impact)? The data were analyzed through discourse analysis method. We highlighted and coded students' inscriptions (e.g., drawings, writings, and representations) that are signs of students' understanding (or lack thereof) of ocean acidification. These signs included explanations of pH levels, drawings of oyster growth, and inclusions of relevant data. The findings showed that the explanatory models can be critical forms of feedback as they reveal a) students' alternative conceptions on how ocean acidification impacts oysters or how acidification works in general; b) students' interpretations of oceans' (non)connectedness to Earth system; c) the choice of scientific representations and their sources; and d) the way students' integrate evidence or data from the investigations. Our work tackles an understanding of one of the most vital signs of modern climatic changes. Recent scientific evidence shows that if the change in ocean pH becomes too extreme, many organisms may not be able to adjust to this change. Based on our findings, we suggest that teachers can use explanatory models as sources of feedback to recognize how well their students conceptualize ocean acidification, integrate scientific practices, and use cultural artifacts of doing science.

Soil Cd, Cr, Cu, Ni, Pb and Zn sorption and retention models using SVM: Variable selection and competitive model.

PubMed

González Costa, J J; Reigosa, M J; Matías, J M; Covelo, E F

2017-09-01

The aim of this study was to model the sorption and retention of Cd, Cu, Ni, Pb and Zn in soils. To that extent, the sorption and retention of these metals were studied and the soil characterization was performed separately. Multiple stepwise regression was used to produce multivariate models with linear techniques and with support vector machines, all of which included 15 explanatory variables characterizing soils. When the R-squared values are represented, two different groups are noticed. Cr, Cu and Pb sorption and retention show a higher R-squared; the most explanatory variables being humified organic matter, Al oxides and, in some cases, cation-exchange capacity (CEC). The other group of metals (Cd, Ni and Zn) shows a lower R-squared, and clays are the most explanatory variables, including a percentage of vermiculite and slime. In some cases, quartz, plagioclase or hematite percentages also show some explanatory capacity. Support Vector Machine (SVM) regression shows that the different models are not as regular as in multiple regression in terms of number of variables, the regression for nickel adsorption being the one with the highest number of variables in its optimal model. On the other hand, there are cases where the most explanatory variables are the same for two metals, as it happens with Cd and Cr adsorption. A similar adsorption mechanism is thus postulated. These patterns of the introduction of variables in the model allow us to create explainability sequences. Those which are the most similar to the selectivity sequences obtained by Covelo (2005) are Mn oxides in multiple regression and change capacity in SVM. Among all the variables, the only one that is explanatory for all the metals after applying the maximum parsimony principle is the percentage of sand in the retention process. In the competitive model arising from the aforementioned sequences, the most intense competitiveness for the adsorption and retention of different metals appears between Cr and Cd, Cu and Zn in multiple regression; and between Cr and Cd in SVM regression. Copyright © 2017 Elsevier B.V. All rights reserved.

Classification of natural and supernatural causes of mental distress. Development of a Mental Distress Explanatory Model Questionnaire.

PubMed

Eisenbruch, M

1990-11-01

This paper describes the background and development of a Mental Distress Explanatory Model Questionnaire designed to explore how people from different cultures explain mental distress. A 45-item questionnaire was developed with items derived from the Murdock et al. categories, with additional items covering western notions of physiological causation and stress. The questionnaire was administered to 261 people, mostly college students. Multi-dimensional scaling analysis shows four clusters of mental distress: a) stress; b) western physiological; c) nonwestern physiological; and d) supernatural. These clusters form two dimensions: western physiological vs. supernatural and impersonal vs. personalistic explanations. Natural and stress items are separated from supernatural and nonwestern physiological items along the first dimension. Brain damage, physical illness, and genetic defects have the greatest separation along the first dimension. Being hot, the body being out of balance, and wind currents passing through the body most strongly represent the non-western physiological category. The questionnaire has the potential to be used for community health screening and for monitoring patient care, as well as with students in the health sciences and with health practitioners.

Analysing the relationships between students and mathematics: a tale of two paradigms

NASA Astrophysics Data System (ADS)

Jorgensen, Robyn; Larkin, Kevin

2017-03-01

In this article, we argue the need to use inter-disciplinary paradigms to make sense of a range of findings from a research project. We developed a methodology using iPad diaries to uncover young students' thinking—mathematical, social and affective—so as to better understand their experiences of mathematics. These students, predominantly from year 3 to year 6, were drawn from economically and socially distinct schools in Queensland and New South Wales, Australia. This article builds on previous research, where we outlined the unique methodology that we developed over three iterations to collect student attitudinal comments regarding mathematics. The comments we collected gave significant insights into the experiences of, and possibilities for, the mathematics education of young learners. Here, we use these findings to explore the value of two paradigms to explain student experiences towards mathematics among primary school students from different social backgrounds. In so doing, we develop an explanatory model for the socially differentiated outcomes in students' responses and then use this explanatory model to analyse student responses from the two most socially disparate schools in our research.

Developing a Model and Applications for Probabilities of Student Success: A Case Study of Predictive Analytics

ERIC Educational Resources Information Center

Calvert, Carol Elaine

2014-01-01

This case study relates to distance learning students on open access courses. It demonstrates the use of predictive analytics to generate a model of the probabilities of success and retention at different points, or milestones, in a student journey. A core set of explanatory variables has been established and their varying relative importance at…

Using a Market Ratio Factor in Faculty Salary Equity Studies. Professional File Number 103, Spring 2007

ERIC Educational Resources Information Center

Luna, Andrew L.

2007-01-01

This study used two multiple regression analyses to develop an explanatory model to determine which model might best explain faculty salaries. The central purpose of the study was to determine if using a single market ratio variable was a stronger predictor for faculty salaries than the use of dummy variables representing various disciplines.…

Mechanisms of eyewitness suggestibility: tests of the explanatory role hypothesis.

PubMed

Rindal, Eric J; Chrobak, Quin M; Zaragoza, Maria S; Weihing, Caitlin A

2017-10-01

In a recent paper, Chrobak and Zaragoza (Journal of Experimental Psychology: General, 142(3), 827-844, 2013) proposed the explanatory role hypothesis, which posits that the likelihood of developing false memories for post-event suggestions is a function of the explanatory function the suggestion serves. In support of this hypothesis, they provided evidence that participant-witnesses were especially likely to develop false memories for their forced fabrications when their fabrications helped to explain outcomes they had witnessed. In three experiments, we test the generality of the explanatory role hypothesis as a mechanism of eyewitness suggestibility by assessing whether this hypothesis can predict suggestibility errors in (a) situations where the post-event suggestions are provided by the experimenter (as opposed to fabricated by the participant), and (b) across a variety of memory measures and measures of recollective experience. In support of the explanatory role hypothesis, participants were more likely to subsequently freely report (E1) and recollect the suggestions as part of the witnessed event (E2, source test) when the post-event suggestion helped to provide a causal explanation for a witnessed outcome than when it did not serve this explanatory role. Participants were also less likely to recollect the suggestions as part of the witnessed event (on measures of subjective experience) when their explanatory strength had been reduced by the presence of an alternative explanation that could explain the same outcome (E3, source test + warning). Collectively, the results provide strong evidence that the search for explanatory coherence influences people's tendency to misremember witnessing events that were only suggested to them.

Analysis of the Explanatory Variables of the Differences in Perceptions of Cyberbullying: A Role-Based-Model Approach.

PubMed

Fernández-Antelo, Inmaculada; Cuadrado-Gordillo, Isabel

2018-04-01

The controversies that exist regarding the delimitation of the cyberbullying construct demonstrate the need for further research focused on determining the criteria that shape the structure of the perceptions that adolescents have of this phenomenon and on seeking explanations of this behavior. The objectives of this study were to (a) construct possible explanatory models of the perception of cyberbullying from identifying and relating the criteria that form this construct and (b) analyze the influence of previous cyber victimization and cyber aggression experiences in the construction of explanatory models of the perception of cyberbullying. The sample consisted of 2,148 adolescents (49.1% girls; SD = 0.5) aged from 12 to 16 years ( M = 13.9 years; SD = 1.2). The results have shown that previous cyber victimization and cyber aggression experiences lead to major differences in the explanatory models to interpret cyber-abusive behavior as cyberbullying episodes, or as social relationship mechanisms, or as a revenge reaction. We note that the aggressors' explanatory model is based primarily on a strong reciprocal relationship between the imbalance of power and intentionality, that it functions as a link promoting indirect causal relationships of the anonymity and repetition factors with the cyberbullying construct. The victims' perceptual structure is based on three criteria-imbalance of power, intentionality, and publicity-where the key factor in this structure is the intention to harm. These results allow to design more effective measures of prevention and intervention closely tailored to addressing directly the factors that are considered to be predictors of risk.

Individual and contextual factors associated to the self-perception of oral health in Brazilian adults.

PubMed

Silva, Janmille Valdivino da; Oliveira, Angelo Giuseppe Roncalli da Costa

2018-04-09

To analyze how individual characteristics and the social context, together, are associated with self-perception of the oral health. A multilevel cross-sectional study with data from the Brazilian National Health Survey 2013, the United Nations Development Program, and the National Registry of Health Establishments. The explanatory variables for the "oral health perception" outcome were grouped, according to the study framework, into biological characteristics (sex, color, age), proximal social determinants (literacy, household crowding, and socioeconomic stratification), and distal (years of schooling expectancy at age 18, GINI, Human Development Index, and per capita income). The described analysis was performed, along with bivariate Poisson analysis and multilevel Poisson analysis for the construction of the explanatory model of oral health perception. All analyzes considered the sample weights. Both the biological characteristics and the proximal and distal social determinants were associated with the perception of oral health in the bivariate analysis. A higher prevalence of bad oral health was associated to lower years of schooling expectancy (PR = 1.31), lower per capita income (PR = 1.45), higher income concentration (PR = 1.41), and worse human development (PR = 1.45). Inversely, oral health services in both primary and secondary care were negatively associated with oral health perception. All the biological and individual social characteristics, except reading and writing, made up the final explanatory model along with the distal social determinants of the Human Development Index and coverage of basic care in the multilevel analysis. Biological factors, individual and contextual social determinants were associate synergistically with the population's perception of oral health. It is necessary to improve individual living conditions and the implementation of public social policies to improve the oral health of the population.

Sharpening method of satellite thermal image based on the geographical statistical model

NASA Astrophysics Data System (ADS)

Qi, Pengcheng; Hu, Shixiong; Zhang, Haijun; Guo, Guangmeng

2016-04-01

To improve the effectiveness of thermal sharpening in mountainous regions, paying more attention to the laws of land surface energy balance, a thermal sharpening method based on the geographical statistical model (GSM) is proposed. Explanatory variables were selected from the processes of land surface energy budget and thermal infrared electromagnetic radiation transmission, then high spatial resolution (57 m) raster layers were generated for these variables through spatially simulating or using other raster data as proxies. Based on this, the local adaptation statistical relationship between brightness temperature (BT) and the explanatory variables, i.e., the GSM, was built at 1026-m resolution using the method of multivariate adaptive regression splines. Finally, the GSM was applied to the high-resolution (57-m) explanatory variables; thus, the high-resolution (57-m) BT image was obtained. This method produced a sharpening result with low error and good visual effect. The method can avoid the blind choice of explanatory variables and remove the dependence on synchronous imagery at visible and near-infrared bands. The influences of the explanatory variable combination, sampling method, and the residual error correction on sharpening results were analyzed deliberately, and their influence mechanisms are reported herein.

Whole Trait Theory

PubMed Central

Fleeson, William; Jayawickreme, Eranda

2014-01-01

Personality researchers should modify models of traits to include mechanisms of differential reaction to situations. Whole Trait Theory does so via five main points. First, the descriptive side of traits should be conceptualized as density distributions of states. Second, it is important to provide an explanatory account of the Big 5 traits. Third, adding an explanatory account to the Big 5 creates two parts to traits, an explanatory part and a descriptive part, and these two parts should be recognized as separate entities that are joined into whole traits. Fourth, Whole Trait Theory proposes that the explanatory side of traits consists of social-cognitive mechanisms. Fifth, social-cognitive mechanisms that produce Big-5 states should be identified. PMID:26097268

Explanatory models concerning the effects of small-area characteristics on individual health.

PubMed

Voigtländer, Sven; Vogt, Verena; Mielck, Andreas; Razum, Oliver

2014-06-01

Material and social living conditions at the small-area level are assumed to have an effect on individual health. We review existing explanatory models concerning the effects of small-area characteristics on health and describe the gaps future research should try to fill. Systematic literature search for, and analysis of, studies that propose an explanatory model of the relationship between small-area characteristics and health. Fourteen studies met our inclusion criteria. Using various theoretical approaches, almost all of the models are based on a three-tier structure linking social inequalities (posited at the macro-level), small-area characteristics (posited at the meso-level) and individual health (micro-level). No study explicitly defines the geographical borders of the small-area context. The health impact of the small-area characteristics is explained by specific pathways involving mediating factors (psychological, behavioural, biological). These pathways tend to be seen as uni-directional; often, causality is implied. They may be modified by individual factors. A number of issues need more attention in research on explanatory models concerning small-area effects on health. Among them are the (geographical) definition of the small-area context; the systematic description of pathways comprising small-area contextual as well as compositional factors; questions of direction of association and causality; and the integration of a time dimension.

Differentiated Success: Combining Theories to Explain Learning

ERIC Educational Resources Information Center

Jorgensen, Robyn; Larkin, Kevin

2015-01-01

This paper explores the value of different paradigms to explain dispositions towards mathematics among primary school students from different social backgrounds. As part of a larger project designed to elicit students' thinking and attitudes towards mathematics, we seek to develop an explanatory model for the socially-differentiated outcomes in…

Explanation in Biology: Reduction, Pluralism, and Explanatory Aims

ERIC Educational Resources Information Center

Brigandt, Ingo

2013-01-01

This essay analyzes and develops recent views about explanation in biology. Philosophers of biology have parted with the received deductive-nomological model of scientific explanation primarily by attempting to capture actual biological theorizing and practice. This includes an endorsement of different kinds of explanation (e.g., mathematical and…

Defining conservation priorities using fragmentation forecasts

Treesearch

David Wear; John Pye; Kurt H. Riitters

2004-01-01

Methods are developed for forecasting the effects of population and economic growth on the distribution of interior forest habitat. An application to the southeastern United States shows that models provide significant explanatory power with regard to the observed distribution of interior forest. Estimates for economic and biophysical variables are significant and...

The Rhetoric of Explanation.

ERIC Educational Resources Information Center

Connors, Robert J.

As background to an argument for purposive reintegration of discourse study, this paper examines the concept of explanatory discourse as it developed within the Western rhetorical tradition. Following a discussion of the rise of a rhetoric of explanation, the first section cites the roots of the explanatory pedagogy developing during the first…

Mental health assessment: Inference, explanation, and coherence.

PubMed

Thagard, Paul; Larocque, Laurette

2018-06-01

Mental health professionals such as psychiatrists and psychotherapists assess their patients by identifying disorders that explain their symptoms. This assessment requires an inference to the best explanation that compares different disorders with respect to how well they explain the available evidence. Such comparisons are captured by the theory of explanatory coherence that states 7 principles for evaluating competing hypotheses in the light of evidence. The computational model ECHO shows how explanatory coherence can be efficiently computed. We show the applicability of explanatory coherence to mental health assessment by modelling a case of psychiatric interviewing and a case of psychotherapeutic evaluation. We argue that this approach is more plausible than Bayesian inference and hermeneutic interpretation. © 2018 John Wiley & Sons, Ltd.

Why the Difference Between Explanation and Argument Matters to Science Education

NASA Astrophysics Data System (ADS)

Brigandt, Ingo

2016-05-01

Contributing to the recent debate on whether or not explanations ought to be differentiated from arguments, this article argues that the distinction matters to science education. I articulate the distinction in terms of explanations and arguments having to meet different standards of adequacy. Standards of explanatory adequacy are important because they correspond to what counts as a good explanation in a science classroom, whereas a focus on evidence-based argumentation can obscure such standards of what makes an explanation explanatory. I provide further reasons for the relevance of not conflating explanations with arguments (and having standards of explanatory adequacy in view). First, what guides the adoption of the particular standards of explanatory adequacy that are relevant in a scientific case is the explanatory aim pursued in this context. Apart from explanatory aims being an important aspect of the nature of science, including explanatory aims in classroom instruction also promotes students seeing explanations as more than facts, and engages them in developing explanations as responses to interesting explanatory problems. Second, it is of relevance to science curricula that science aims at intervening in natural processes, not only for technological applications, but also as part of experimental discovery. Not any argument enables intervention in nature, as successful intervention specifically presupposes causal explanations. Students can fruitfully explore in the classroom how an explanatory account suggests different options for intervention.

Reporting and Methodology of Multivariable Analyses in Prognostic Observational Studies Published in 4 Anesthesiology Journals: A Methodological Descriptive Review.

PubMed

Guglielminotti, Jean; Dechartres, Agnès; Mentré, France; Montravers, Philippe; Longrois, Dan; Laouénan, Cedric

2015-10-01

Prognostic research studies in anesthesiology aim to identify risk factors for an outcome (explanatory studies) or calculate the risk of this outcome on the basis of patients' risk factors (predictive studies). Multivariable models express the relationship between predictors and an outcome and are used in both explanatory and predictive studies. Model development demands a strict methodology and a clear reporting to assess its reliability. In this methodological descriptive review, we critically assessed the reporting and methodology of multivariable analysis used in observational prognostic studies published in anesthesiology journals. A systematic search was conducted on Medline through Web of Knowledge, PubMed, and journal websites to identify observational prognostic studies with multivariable analysis published in Anesthesiology, Anesthesia & Analgesia, British Journal of Anaesthesia, and Anaesthesia in 2010 and 2011. Data were extracted by 2 independent readers. First, studies were analyzed with respect to reporting of outcomes, design, size, methods of analysis, model performance (discrimination and calibration), model validation, clinical usefulness, and STROBE (i.e., Strengthening the Reporting of Observational Studies in Epidemiology) checklist. A reporting rate was calculated on the basis of 21 items of the aforementioned points. Second, they were analyzed with respect to some predefined methodological points. Eighty-six studies were included: 87.2% were explanatory and 80.2% investigated a postoperative event. The reporting was fairly good, with a median reporting rate of 79% (75% in explanatory studies and 100% in predictive studies). Six items had a reporting rate <36% (i.e., the 25th percentile), with some of them not identified in the STROBE checklist: blinded evaluation of the outcome (11.9%), reason for sample size (15.1%), handling of missing data (36.0%), assessment of colinearity (17.4%), assessment of interactions (13.9%), and calibration (34.9%). When reported, a few methodological shortcomings were observed, both in explanatory and predictive studies, such as an insufficient number of events of the outcome (44.6%), exclusion of cases with missing data (93.6%), or categorization of continuous variables (65.1%.). The reporting of multivariable analysis was fairly good and could be further improved by checking reporting guidelines and EQUATOR Network website. Limiting the number of candidate variables, including cases with missing data, and not arbitrarily categorizing continuous variables should be encouraged.

Examining school effectiveness at the fourth grade: A hierarchical analysis of the Third International Mathematics and Science Study (TIMSS)

NASA Astrophysics Data System (ADS)

Stemler, Steven Edward

This study explored school effectiveness in mathematics and science at the fourth grade using data from IEA's Third International Mathematics and Science Study (TIMSS). Fourteen of the 26 countries participating in TIMSS at the fourth grade possessed sufficient between-school variability in mathematics achievement to justify the creation of explanatory models of school effectiveness while 13 countries possessed sufficient between-school variability in science achievement. Exploratory models were developed using variables drawn from student, teacher, and school questionnaires. The variables were chosen to represent the domains of student involvement, instructional methods, classroom organization, school climate, and school structure. Six explanatory models for each subject were analyzed using two-level hierarchical linear modeling (HLM) and were compared to models using only school mean SES as an explanatory variable. The amount of variability in student achievement in mathematics attributable to differences between schools ranged from 16% in Cyprus to 56% in Latvia, while the amount of between-school variance in science achievement ranged from 12% in Korea to 59% in Latvia. In general, about one-quarter of the variability in mathematics and science achievement was found to lie between schools. The research findings revealed that after adjusting for differences in student backgrounds across schools, the most effective schools in mathematics and science had students who reported seeing a positive relationship between hard work, belief in their own abilities, and achievement. In addition, more effective schools had students who reported less frequent use of computers and calculators in the classroom. These relationships were found to be stable across explanatory models, cultural contexts, and subject areas. This study has contributed a unique element to the literature by examining school effectiveness at the fourth grade across two subject areas and across 14 different countries. The results indicate that further exploration of the relationship between school effectiveness and student locus of control warrants serious consideration. Future research on school effectiveness is recommended, perhaps using trend data and looking at different grade levels.

Modification of the Integrated Sasang Constitutional Diagnostic Model

PubMed Central

Nam, Jiho

2017-01-01

In 2012, the Korea Institute of Oriental Medicine proposed an objective and comprehensive physical diagnostic model to address quantification problems in the existing Sasang constitutional diagnostic method. However, certain issues have been raised regarding a revision of the proposed diagnostic model. In this paper, we propose various methodological approaches to address the problems of the previous diagnostic model. Firstly, more useful variables are selected in each component. Secondly, the least absolute shrinkage and selection operator is used to reduce multicollinearity without the modification of explanatory variables. Thirdly, proportions of SC types and age are considered to construct individual diagnostic models and classify the training set and the test set for reflecting the characteristics of the entire dataset. Finally, an integrated model is constructed with explanatory variables of individual diagnosis models. The proposed integrated diagnostic model significantly improves the sensitivities for both the male SY type (36.4% → 62.0%) and the female SE type (43.7% → 64.5%), which were areas of limitation of the previous integrated diagnostic model. The ideas of these new algorithms are expected to contribute not only to the scientific development of Sasang constitutional medicine in Korea but also to that of other diagnostic methods for traditional medicine. PMID:29317897

Analysis of injury severity of drivers involved in single- and two-vehicle crashes on highways in Ontario.

PubMed

Lee, Chris; Li, Xuancheng

2014-10-01

This study analyzes driver's injury severity in single- and two-vehicle crashes and compares the effects of explanatory variables among various types of crashes. The study identified factors affecting injury severity and their effects on severity levels using 5-year crash records for provincial highways in Ontario, Canada. Considering heteroscedasticity in the effects of explanatory variables on injury severity, the heteroscedastic ordered logit (HOL) models were developed for single- and two-vehicle crashes separately. The results of the models show that there exists heteroscedasticity for young drivers (≤30), safety equipment and ejection in the single-vehicle crash model, and female drivers, safety equipment and head-on collision in the two-vehicle crash models. The results also show that young car drivers have opposite effects between single-car and car-car crashes, and sideswipe crashes have opposite effects between car-car and truck-truck crashes. The study demonstrates that separate HOL models for single-vehicle and different types of two-vehicle crashes can identify differential effects of factors on driver's injury severity. Copyright © 2014 Elsevier Ltd. All rights reserved.

Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable.

PubMed

Austin, Peter C; Steyerberg, Ewout W

2012-06-20

When outcomes are binary, the c-statistic (equivalent to the area under the Receiver Operating Characteristic curve) is a standard measure of the predictive accuracy of a logistic regression model. An analytical expression was derived under the assumption that a continuous explanatory variable follows a normal distribution in those with and without the condition. We then conducted an extensive set of Monte Carlo simulations to examine whether the expressions derived under the assumption of binormality allowed for accurate prediction of the empirical c-statistic when the explanatory variable followed a normal distribution in the combined sample of those with and without the condition. We also examine the accuracy of the predicted c-statistic when the explanatory variable followed a gamma, log-normal or uniform distribution in combined sample of those with and without the condition. Under the assumption of binormality with equality of variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the product of the standard deviation of the normal components (reflecting more heterogeneity) and the log-odds ratio (reflecting larger effects). Under the assumption of binormality with unequal variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the standardized difference of the explanatory variable in those with and without the condition. In our Monte Carlo simulations, we found that these expressions allowed for reasonably accurate prediction of the empirical c-statistic when the distribution of the explanatory variable was normal, gamma, log-normal, and uniform in the entire sample of those with and without the condition. The discriminative ability of a continuous explanatory variable cannot be judged by its odds ratio alone, but always needs to be considered in relation to the heterogeneity of the population.

Comparison of stream invertebrate response models for bioassessment metric

USGS Publications Warehouse

Waite, Ian R.; Kennen, Jonathan G.; May, Jason T.; Brown, Larry R.; Cuffney, Thomas F.; Jones, Kimberly A.; Orlando, James L.

2012-01-01

We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R2 from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R2 values than RICHTOL for the two regions tested. Modeled O/E R2 values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.

Theoretical Models for Aircraft Availability: Classical Approach to Identification of Trends, Seasonality, and System Constraints in the Development of Realized Models

DTIC Science & Technology

2004-03-01

predicting future events ( Heizer and Render , 1999). Forecasting techniques fall into two major categories, qualitative and quantitative methods...Globemaster III.” Excerpt from website. www.globalsecurity.org/military /systems/ aircraft/c-17-history.htm. 2003. Heizer , Jay, and Barry Render ...of the past data used to make the forecast ( Heizer , et. al., 1999). Explanatory forecasting models assume that the variable being forecasted

Impact of multicollinearity on small sample hydrologic regression models

NASA Astrophysics Data System (ADS)

Kroll, Charles N.; Song, Peter

2013-06-01

Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.

Perceived Risks and Normative Beliefs as Explanatory Models for College Student Alcohol Involvement: An Assessment of a Campus with Conventional Alcohol Control Policies and Enforcement Practices

ERIC Educational Resources Information Center

Lewis, Todd F.; Thombs, Dennis L.

2005-01-01

The aim of this study was to conduct a multivariate assessment of college student drinking motivations at a campus with conventional alcohol control policies and enforcement practices, including the establishment and dissemination of alcohol policies and the use of warnings to arouse fear of sanctions. Two explanatory models were compared:…

Explanatory Power of Multi-scale Physical Descriptors in Modeling Benthic Indices Across Nested Ecoregions of the Pacific Northwest

NASA Astrophysics Data System (ADS)

Holburn, E. R.; Bledsoe, B. P.; Poff, N. L.; Cuhaciyan, C. O.

2005-05-01

Using over 300 R/EMAP sites in OR and WA, we examine the relative explanatory power of watershed, valley, and reach scale descriptors in modeling variation in benthic macroinvertebrate indices. Innovative metrics describing flow regime, geomorphic processes, and hydrologic-distance weighted watershed and valley characteristics are used in multiple regression and regression tree modeling to predict EPT richness, % EPT, EPT/C, and % Plecoptera. A nested design using seven ecoregions is employed to evaluate the influence of geographic scale and environmental heterogeneity on the explanatory power of individual and combined scales. Regression tree models are constructed to explain variability while identifying threshold responses and interactions. Cross-validated models demonstrate differences in the explanatory power associated with single-scale and multi-scale models as environmental heterogeneity is varied. Models explaining the greatest variability in biological indices result from multi-scale combinations of physical descriptors. Results also indicate that substantial variation in benthic macroinvertebrate response can be explained with process-based watershed and valley scale metrics derived exclusively from common geospatial data. This study outlines a general framework for identifying key processes driving macroinvertebrate assemblages across a range of scales and establishing the geographic extent at which various levels of physical description best explain biological variability. Such information can guide process-based stratification to avoid spurious comparison of dissimilar stream types in bioassessments and ensure that key environmental gradients are adequately represented in sampling designs.

Explanatory model for sound amplification in a stethoscope

NASA Astrophysics Data System (ADS)

Eshach, H.; Volfson, A.

2015-01-01

In the present paper we suggest an original physical explanatory model that explains the mechanism of the sound amplification process in a stethoscope. We discuss the amplification of a single pulse, a continuous wave of certain frequency, and finally we address the resonant frequencies. It is our belief that this model may provide students with opportunities to not only better understand the amplification mechanism of a stethoscope, but also to strengthen their understanding of sound, pressure, waves, resonance modes, etc.

Uni- and multi-variable modelling of flood losses: experiences gained from the Secchia river inundation event.

NASA Astrophysics Data System (ADS)

Carisi, Francesca; Domeneghetti, Alessio; Kreibich, Heidi; Schröter, Kai; Castellarin, Attilio

2017-04-01

Flood risk is function of flood hazard and vulnerability, therefore its accurate assessment depends on a reliable quantification of both factors. The scientific literature proposes a number of objective and reliable methods for assessing flood hazard, yet it highlights a limited understanding of the fundamental damage processes. Loss modelling is associated with large uncertainty which is, among other factors, due to a lack of standard procedures; for instance, flood losses are often estimated based on damage models derived in completely different contexts (i.e. different countries or geographical regions) without checking its applicability, or by considering only one explanatory variable (i.e. typically water depth). We consider the Secchia river flood event of January 2014, when a sudden levee-breach caused the inundation of nearly 200 km2 in Northern Italy. In the aftermath of this event, local authorities collected flood loss data, together with additional information on affected private households and industrial activities (e.g. buildings surface and economic value, number of company's employees and others). Based on these data we implemented and compared a quadratic-regression damage function, with water depth as the only explanatory variable, and a multi-variable model that combines multiple regression trees and considers several explanatory variables (i.e. bagging decision trees). Our results show the importance of data collection revealing that (1) a simple quadratic regression damage function based on empirical data from the study area can be significantly more accurate than literature damage-models derived for a different context and (2) multi-variable modelling may outperform the uni-variable approach, yet it is more difficult to develop and apply due to a much higher demand of detailed data.

An Explanatory Mixed Method Study on Pre-Service Language Teachers' Communication Apprehension towards Their Instructors

ERIC Educational Resources Information Center

Kavanoz, Suzan

2017-01-01

Promoting higher communication efficiency among teacher candidates and acting as models are among the main professional responsibilities of teacher educators. This makes the task of teachers even more important in language education classes where students are prospective language teachers and their development as language teachers highly depend on…

School Climate and Academic Achievement in Suburban Schools

ERIC Educational Resources Information Center

Sulak, Tracey N.

2016-01-01

School climate research has indicated a relationship between the climate of a school and academic achievement. The majority of explanatory models have been developed in urban schools with less attention given to suburban schools. Due to the process of formation of suburban schools, there is a likelihood these campuses differ from the traditional…

Reliability, Sensitivity to Measuring Change, and Construct Validity of a Measure of Counselor Adaptability.

ERIC Educational Resources Information Center

Gabbard, Clinton E.; And Others

1986-01-01

Adaptive Counseling and Therapy (ACT) is an integrative, metatheoretical model for selecting an appropriate therapeutic style based on the task-relevant development maturity of the client. The Counselor Behavior Analysis (CBA) Scale measures the central explanatory construct of ACT theory: counselor adaptability. Three studies designed to assess…

Parental Control in Latino Families: An Integrated Review of the Literature

ERIC Educational Resources Information Center

Halgunseth, Linda C.; Ispa, Jean M.; Rudy, Duane

2006-01-01

Using social information processing and cultural change models as explanatory frameworks, this article reviews the literature on Latino parental control and its implications for child development. It is argued that the use of parental control in Latino families may have motivational roots in cultural childrearing goals such as "familismo"…

Using a Market Ratio Factor in Faculty Salary Equity Studies. AIR Professional File. Number 103, Spring 2007

ERIC Educational Resources Information Center

Luna, Andrew L.

2007-01-01

The purpose of this study was to determine if a market ratio factor was a better predictor of faculty salaries than the use of k-1 dummy variables representing the various disciplines. This study used two multiple regression analyses to develop an explanatory model to determine which model might best explain faculty salaries. A total of 20 out of…

Focus on Success: An Explanatory Embedded Multiple-Case Study on How Youth Successfully Navigate Workforce Development Programs in Southern Nevada

ERIC Educational Resources Information Center

Villalobos, Ricardo

2017-01-01

This explanatory qualitative study investigated the perspectives of participant's and practitioner's perceived barriers to success and the necessary navigational expertise for overcoming the identified barriers. This multiple-case study research design examined three WIA out-of-school youth workforce development programs in Southern Nevada, with…

The role of patients' explanatory models and daily-lived experience in hypertension self-management.

PubMed

Bokhour, Barbara G; Cohn, Ellen S; Cortés, Dharma E; Solomon, Jeffrey L; Fix, Gemmae M; Elwy, A Rani; Mueller, Nora; Katz, Lois A; Haidet, Paul; Green, Alexander R; Borzecki, Ann M; Kressin, Nancy R

2012-12-01

Uncontrolled hypertension remains a significant problem for many patients. Few interventions to improve patients' hypertension self-management have had lasting effects. Previous work has focused largely on patients' beliefs as predictors of behavior, but little is understood about beliefs as they are embedded in patients' social contexts. This study aims to explore how patients' "explanatory models" of hypertension (understandings of the causes, mechanisms or pathophysiology, course of illness, symptoms and effects of treatment) and social context relate to their reported daily hypertension self-management behaviors. Semi-structured qualitative interviews with a diverse group of patients at two large urban Veterans Administration Medical centers. PARTICIPANTS (OR PATIENTS OR SUBJECTS): African-American, white and Latino Veterans Affairs (VA) primary care patients with uncontrolled blood pressure. We conducted thematic analysis using tools of grounded theory to identify key themes surrounding patients' explanatory models, social context and hypertension management behaviors. Patients' perceptions of the cause and course of hypertension, experiences of hypertension symptoms, and beliefs about the effectiveness of treatment were related to different hypertension self-management behaviors. Moreover, patients' daily-lived experiences, such as an isolated lifestyle, serious competing health problems, a lack of habits and routines, barriers to exercise and prioritizing lifestyle choices, also interfered with optimal hypertension self-management. Designing interventions to improve patients' hypertension self-management requires consideration of patients' explanatory models and their daily-lived experience. We propose a new conceptual model - the dynamic model of hypertension self-management behavior - which incorporates these key elements of patients' experiences.

Diagnosis-Based Risk Adjustment for Medicare Capitation Payments

PubMed Central

Ellis, Randall P.; Pope, Gregory C.; Iezzoni, Lisa I.; Ayanian, John Z.; Bates, David W.; Burstin, Helen; Ash, Arlene S.

1996-01-01

Using 1991-92 data for a 5-percent Medicare sample, we develop, estimate, and evaluate risk-adjustment models that utilize diagnostic information from both inpatient and ambulatory claims to adjust payments for aged and disabled Medicare enrollees. Hierarchical coexisting conditions (HCC) models achieve greater explanatory power than diagnostic cost group (DCG) models by taking account of multiple coexisting medical conditions. Prospective models predict average costs of individuals with chronic conditions nearly as well as concurrent models. All models predict medical costs far more accurately than the current health maintenance organization (HMO) payment formula. PMID:10172666

A comparison of the physical and anthropometric qualities explanatory of talent in the elite junior Australian football development pathway.

PubMed

Woods, Carl T; Cripps, Ashley; Hopper, Luke; Joyce, Christopher

2017-07-01

To compare the physical and anthropometric qualities explanatory of talent at two developmental levels in junior Australian football (AF). Cross-sectional observational. From a total of 134 juniors, two developmental levels were categorised; U16 (n=50; 15.6±0.3 y), U18 (n=84; 17.4±0.5 y). Within these levels, two groups were a priori defined; talent identified (U16; n=25; 15.7±0.2 y; U18 n=42; 17.5±0.4 y), non-talent identified (U16; n=25; 15.6±0.4 y; U18; n=42; 17.3±0.6 y). Players completed seven physical and anthropometric assessments commonly utilised for talent identification in AF. Binary logistic regression models were built to identify the qualities most explanatory of talent at each level. A combination of standing height, dominant leg dynamic vertical jump height and 20m sprint time provided the most parsimonious explanation of talent at the U16 level (AICc=60.05). At the U18 level, it was a combination of body mass and 20m sprint time that provided the most parsimonious explanation of talent (AICc=111.27). Despite similarities, there appears to be distinctive differences in physical and anthropometric qualities explanatory of talent at the U16 and U18 level. Coaches may view physical and anthropometric qualities more (or less) favourably at different levels of the AF developmental pathway. Given these results, future work should implement a longitudinal design, as physical and/or anthropometric qualities may deteriorate (or emerge) as junior AF players develop. Copyright © 2016 Sports Medicine Australia. All rights reserved.

Model for the separate collection of packaging waste in Portuguese low-performing recycling regions.

PubMed

Oliveira, V; Sousa, V; Vaz, J M; Dias-Ferreira, C

2018-06-15

Separate collection of packaging waste (glass; plastic/metals; paper/cardboard), is currently a widespread practice throughout Europe. It enables the recovery of good quality recyclable materials. However, separate collection performance are quite heterogeneous, with some countries reaching higher levels than others. In the present work, separate collection of packaging waste has been evaluated in a low-performance recycling region in Portugal in order to investigate which factors are most affecting the performance in bring-bank collection system. The variability of separate collection yields (kg per inhabitant per year) among 42 municipalities was scrutinized for the year 2015 against possible explanatory factors. A total of 14 possible explanatory factors were analysed, falling into two groups: socio-economic/demographic and waste collection service related. Regression models were built in an attempt to evaluate the individual effect of each factor on separate collection yields and predict changes on the collection yields by acting on those factors. The best model obtained is capable to explain 73% of the variation found in the separate collection yields. The model includes the following statistically significant indicators affecting the success of separate collection yields: i) inhabitants per bring-bank; ii) relative accessibility to bring-banks; iii) degree of urbanization; iv) number of school years attended; and v) area. The model presented in this work was developed specifically for the bring-bank system, has an explanatory power and quantifies the impact of each factor on separate collection yields. It can therefore be used as a support tool by local and regional waste management authorities in the definition of future strategies to increase collection of recyclables of good quality and to achieve national and regional targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

That Is Not Where that Element Goes ... Ah, the Nature of Science

ERIC Educational Resources Information Center

Nargund, Vanashri; Rogers, Meredith A. Park

2009-01-01

Learning how the periodic table has developed over time can provide an important foundation for students' future science learning, as they begin to explore the explanatory power of other models in science. In this activity, students are given the opportunity to investigate the generation of the modern periodic table, through a process of creating…

Legitimate Techniques for Improving the R-Square and Related Statistics of a Multiple Regression Model

DTIC Science & Technology

1981-01-01

explanatory variable has been ommitted. Ramsey (1974) has developed a rather interesting test for detecting specification errors using estimates of the...Peter. (1979) A Guide to Econometrics , Cambridge, MA: The MIT Press. Ramsey , J.B. (1974), "Classical Model Selection Through Specification Error... Tests ," in P. Zarembka, Ed. Frontiers in Econometrics , New York: Academia Press. Theil, Henri. (1971), Principles of Econometrics , New York: John Wiley

Explanatory Style in Patients with Rheumatoid Arthritis: An Unrecognized Predictor of Mortality

PubMed Central

Crowson, Aaron D.; Colligan, Robert C.; Matteson, Eric L.; Davis, John M.; Crowson, Cynthia S.

2016-01-01

Objective To determine whether pessimistic explanatory style altered the risk for and mortality of rheumatoid arthritis (RA) patients. Methods The study included subjects from a population-based cohort with incident RA and non-RA comparison cohort who completed the Minnesota Multiphasic Personality Inventory (MMPI). Results Among 148 RA and 135 non-RA subjects, pessimism was associated with development of rheumatoid factor positive (RF+) RA. Pessimism was associated with an increased risk of mortality (hazard ratio [HR]:2.88 with similar magnitude to RF+ (HR:2.28). Conclusion Pessimistic explanatory style was associated with an increased risk of developing RA and increased mortality rate in patients with RA. PMID:28148754

Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable

PubMed Central

2012-01-01

Background When outcomes are binary, the c-statistic (equivalent to the area under the Receiver Operating Characteristic curve) is a standard measure of the predictive accuracy of a logistic regression model. Methods An analytical expression was derived under the assumption that a continuous explanatory variable follows a normal distribution in those with and without the condition. We then conducted an extensive set of Monte Carlo simulations to examine whether the expressions derived under the assumption of binormality allowed for accurate prediction of the empirical c-statistic when the explanatory variable followed a normal distribution in the combined sample of those with and without the condition. We also examine the accuracy of the predicted c-statistic when the explanatory variable followed a gamma, log-normal or uniform distribution in combined sample of those with and without the condition. Results Under the assumption of binormality with equality of variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the product of the standard deviation of the normal components (reflecting more heterogeneity) and the log-odds ratio (reflecting larger effects). Under the assumption of binormality with unequal variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the standardized difference of the explanatory variable in those with and without the condition. In our Monte Carlo simulations, we found that these expressions allowed for reasonably accurate prediction of the empirical c-statistic when the distribution of the explanatory variable was normal, gamma, log-normal, and uniform in the entire sample of those with and without the condition. Conclusions The discriminative ability of a continuous explanatory variable cannot be judged by its odds ratio alone, but always needs to be considered in relation to the heterogeneity of the population. PMID:22716998

Temporal Trends and Hydrological Controls of Fisheries Production in the Madeira River (Brazil)

NASA Astrophysics Data System (ADS)

Kaplan, D. A.; Lima, M. A.; Doria, C.

2016-12-01

Amazonian river systems are characterized by a strongly seasonal flood pulse and important hydrologic effects have been observed in the dynamics of fish stocks and fishing yields. Changes in the Amazon's freshwater ecosystems from hydropower development will have a cascade of physical, ecological, and social effects and impacts on fish and fisheries are expected to be potentially irreversible. In this work we investigate shared trends and causal factors driving fish catch in the Madeira River (a major tributary of the Amazon) before dam construction to derive relationships between catch and natural hydrologic dynamics. We applied Dynamic Factor Analysis to investigate dynamics in fish catch across ten commercially important fish species in the Madeira River using daily fish landings data including species and total weight and daily hydrological data obtained from the Brazilian Geological Service. Total annual catch averaged over the 18-yr period (1990-2007) was 849 tons yr-1. Species with the highest catch included curimatã, dourada/filhote and pacu, highlighting the importance of medium and long-distance migratory species for fisheries production. We found a four-trend dynamic factor model (DFM) to best fit the observed data, assessed using the Akaike Information Criteria. Model goodness of fit was fair (R2=0.51) but highly variable across species (0.16 ≤ R2 ≤ 0.95). Fitted trends exhibited strong and regular year-to-year variation representative of the seasonal hydrologic pulsing observed on the Madeira River. Next, we considered 11 candidate explanatory time series and found the best DFM used four explanatory variables and only one common trend. While the model fit with explanatory variables was lower (R2=0.31) it removed much reliance on unknown common trends. The most important explanatory variable in this model was maximum water level followed by days flooded, river flow of the previous year and increment. We found unique responses to hydrological variations across the ten species, suggesting that dam operating rules need to closely mimic natural hydrologic regime in order to maintain the dynamics of these ecosystems. Future multidisciplinary analyses to understand the complex social-ecological effects of dams are needed to improve management practices and support sustainable livelihoods.

Axial cervical vertebrae-based multivariate regression model for the estimation of skeletal-maturation status.

PubMed

Yang, Y-M; Lee, J; Kim, Y-I; Cho, B-H; Park, S-B

2014-08-01

This study aimed to determine the viability of using axial cervical vertebrae (ACV) as biological indicators of skeletal maturation and to build models that estimate ossification level with improved explanatory power over models based only on chronological age. The study population comprised 74 female and 47 male patients with available hand-wrist radiographs and cone-beam computed tomography images. Generalized Procrustes analysis was used to analyze the shape, size, and form of the ACV regions of interest. The variabilities of these factors were analyzed by principal component analysis. Skeletal maturation was then estimated using a multiple regression model. Separate models were developed for male and female participants. For the female estimation model, the adjusted R(2) explained 84.8% of the variability of the Sempé maturation level (SML), representing a 7.9% increase in SML explanatory power over that using chronological age alone (76.9%). For the male estimation model, the adjusted R(2) was over 90%, representing a 1.7% increase relative to the reference model. The simplest possible ACV morphometric information provided a statistically significant explanation of the portion of skeletal-maturation variability not dependent on chronological age. These results verify that ACV is a strong biological indicator of ossification status. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Writing Development in Secondary/Post Secondary Language Learning: Integrating Multiple Motivating Factors, Explanatory Feedback, and Explanatory Writing Tools to Increase Competence and Confidence in Writing

ERIC Educational Resources Information Center

Jefferson, Trevina

2013-01-01

Background: This study discusses data-driven results of newly-developed writing tools that are objective, easy, and less time-consuming than standard classroom writing strategies; additionally, multiple motivation triggers and peer evaluation are evaluated together with these new, modernized writing tools. The results are explained separately and…

Human vs. Computer Diagnosis of Students' Natural Selection Knowledge: Testing the Efficacy of Text Analytic Software

ERIC Educational Resources Information Center

Nehm, Ross H.; Haertig, Hendrik

2012-01-01

Our study examines the efficacy of Computer Assisted Scoring (CAS) of open-response text relative to expert human scoring within the complex domain of evolutionary biology. Specifically, we explored whether CAS can diagnose the explanatory elements (or Key Concepts) that comprise undergraduate students' explanatory models of natural selection with…

Real-time predictive seasonal influenza model in Catalonia, Spain

PubMed Central

Basile, Luca; Oviedo de la Fuente, Manuel; Torner, Nuria; Martínez, Ana; Jané, Mireia

2018-01-01

Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010–2011 to 2013–2014) was created. A pilot test was conducted during the 2014–2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015–2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015–2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included. PMID:29513710

"Head take you": causal attributions of mental illness in Jamaica.

PubMed

Arthur, Carlotta M; Whitley, Rob

2015-02-01

Causal attributions are a key factor in explanatory models of illness; however, little research on causal attributions of mental illness has been conducted in developing nations in the Caribbean, including Jamaica. Explanatory models of mental illness may be important in understanding illness experience and be a crucial factor in mental health service seeking and utilization. We explored causal attributions of mental illness in Jamaica by conducting 20 focus groups, including 16 community samples, 2 patient samples, and 2 samples of caregivers of patients, with a total of 159 participants. The 5 most commonly endorsed causal attributions of mental illness are discussed: (a) drug-related causes, including ganja (marijuana); (b) biological causes, such as chemical imbalance, familial transmission, and "blood"; (c) psychological causes, including stress and thinking too much; (d) social causes, such as relationship problems and job loss; and (e) spiritual or religious causes, including Obeah. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Parents' explanatory models and hopes for outcomes of occupational therapy using a sensory integration approach.

PubMed

Cohn, Ellen S; Kramer, Jessica; Schub, Jamie A; May-Benson, Teresa

2014-01-01

PURPOSE. To describe parents' concerns and hopes for their children who would be receiving occupational therapy using a sensory integration approach. METHOD. Content analysis of 275 parental responses to three open-ended questions on developmental-sensory history intake forms. FINDINGS. Parents' descriptions of why they sought for their children were categorized into four overarching concerns about their children's challenges: self-regulation, interacting with peers, participating in skilled motor activities, and self-confidence. Parents often linked these concerns together, revealing explanatory models of how they make sense of potential relationships among their children's challenges and how these challenges affect occupational performance. Parents hoped occupational therapy would help their children develop self-understanding and frustration tolerance to self-regulate their behavior in socially acceptable ways. IMPLICATIONS. Assessment and intervention should explicitly focus on links among self-regulation, social participation, skills, and perceived competence to address parents' expectations. Copyright © 2014 by the American Occupational Therapy Association, Inc.

Spatially Resolved Measurements Of Plasma Density Irregularities In The Ionosphere F Region For Scintillation Studies.

NASA Astrophysics Data System (ADS)

Spencer, E. A.; Russ, S.; Clark, D. C.; Latif, S.; Montalvo, C.

2016-12-01

This qualitative study focuses on students evidence-based explanatory models on how ocean acidification impacts oysters. Explanatory models are the crucial components of scientific endeavors as it helps scientists explain how the natural world functions and the reasons for the ways it functions. Moreover, these models assemble individual practices to understand how they work together to reach clear conclusions through scientific investigations. Due to their critical roles in making sense of authentic science, recent studies in science education suggest that these models should be part of the curriculum aligned with new science standards, i.e. Next Generation Science Standards, which stress the importance of engaging students in scientific practices. By collecting data from 400 secondary school students in Maryland, we aim to respond to the question: How can we use secondary school students' explanatory models to provide students with constructive feedback for more comprehensive learning of ocean acidification (the related evidence, causes and impact)? The data were analyzed through discourse analysis method. We highlighted and coded students' inscriptions (e.g., drawings, writings, and representations) that are signs of students' understanding (or lack thereof) of ocean acidification. These signs included explanations of pH levels, drawings of oyster growth, and inclusions of relevant data. The findings showed that the explanatory models can be critical forms of feedback as they reveal a) students' alternative conceptions on how ocean acidification impacts oysters or how acidification works in general; b) students' interpretations of oceans' (non)connectedness to Earth system; c) the choice of scientific representations and their sources; and d) the way students' integrate evidence or data from the investigations. Our work tackles an understanding of one of the most vital signs of modern climatic changes. Recent scientific evidence shows that if the change in ocean pH becomes too extreme, many organisms may not be able to adjust to this change. Based on our findings, we suggest that teachers can use explanatory models as sources of feedback to recognize how well their students conceptualize ocean acidification, integrate scientific practices, and use cultural artifacts of doing science.

Modeling Above-Ground Biomass Across Multiple Circum-Arctic Tundra Sites Using High Spatial Resolution Remote Sensing

NASA Astrophysics Data System (ADS)

Räsänen, Aleksi; Juutinen, Sari; Aurela, Mika; Virtanen, Tarmo

2017-04-01

Biomass is one of the central bio-geophysical variables in Earth observation for tracking plant productivity, and flow of carbon, nutrients, and water. Most of the satellite based biomass mapping exercises in Arctic environments have been performed by using rather coarse spatial resolution data, e.g. Landsat and AVHRR which have spatial resolutions of 30 m and >1 km, respectively. While the coarse resolution images have high temporal resolution, they are incapable of capturing the fragmented nature of tundra environment and fine-scale changes in vegetation and carbon exchange patterns. Very high spatial resolution (VHSR, spatial resolution 0.5-2 m) satellite images have the potential to detect environmental variables with an ecologically sound spatial resolution. The usage of VHSR images has, nevertheless, been modest so far in biomass modeling in the Arctic. Our objectives were to use VHSR for predicting above ground biomass in tundra landscapes, evaluate whether a common predictive model can be applied across circum-Arctic tundra and peatland sites having different types of vegetation, and produce knowledge on distribution of plant functional types (PFT) in these sites. Such model development is dependent on ground-based surveys of vegetation with the same spatial resolution and extent with the VHSR images. In this study, we conducted ground-based surveys of vegetation composition and biomass in four different arctic tundra or peatland areas located in Russia, Canada, and Finland. First, we sorted species into PFTs and developed PFT-specific models to predict biomass on the basis of non-destructive measurements (cover, height). Second, we predicted overall biomass on landscape scale by combinations of single bands and vegetation indices of very high resolution satellite images (QuickBird or WorldView-2 images of the eight sites). We compared area-specific empirical regression models and common models that were applied across all sites. We found that NDVI was usually the highest scoring spectral indices in explaining biomass distribution with good explanatory power. Furthermore, models which had more than one explanatory variable had higher explanatory power than models with a single index. The dissimilarity between common and site-specific model estimates was, however, high and data indicates that variation in vegetation properties and its impact on spectral reflectance needs to be acknowledged. Our work produced knowledge on above-ground biomass distribution and contribution of PFTs across circum-Arctic low-growth landscapes and will contribute to developing space-borne vegetation monitoring schemes utilizing VHSR satellite images.

The use of generalised additive models (GAM) in dentistry.

PubMed

Helfenstein, U; Steiner, M; Menghini, G

1997-12-01

Ordinary multiple regression and logistic multiple regression are widely applied statistical methods which allow a researcher to 'explain' or 'predict' a response variable from a set of explanatory variables or predictors. In these models it is usually assumed that quantitative predictors such as age enter linearly into the model. During recent years these methods have been further developed to allow more flexibility in the way explanatory variables 'act' on a response variable. The methods are called 'generalised additive models' (GAM). The rigid linear terms characterising the association between response and predictors are replaced in an optimal way by flexible curved functions of the predictors (the 'profiles'). Plotting the 'profiles' allows the researcher to visualise easily the shape by which predictors 'act' over the whole range of values. The method facilitates detection of particular shapes such as 'bumps', 'U-shapes', 'J-shapes, 'threshold values' etc. Information about the shape of the association is not revealed by traditional methods. The shapes of the profiles may be checked by performing a Monte Carlo simulation ('bootstrapping'). After the presentation of the GAM a relevant case study is presented in order to demonstrate application and use of the method. The dependence of caries in primary teeth on a set of explanatory variables is investigated. Since GAMs may not be easily accessible to dentists, this article presents them in an introductory condensed form. It was thought that a nonmathematical summary and a worked example might encourage readers to consider the methods described. GAMs may be of great value to dentists in allowing visualisation of the shape by which predictors 'act' and obtaining a better understanding of the complex relationships between predictors and response.

Management practices associated with conception rate and service rate of lactating Holstein cows in large, commercial dairy herds.

PubMed

Schefers, J M; Weigel, K A; Rawson, C L; Zwald, N R; Cook, N B

2010-04-01

Data from lactating Holstein cows in herds that participate in a commercial progeny testing program were analyzed to explain management factors associated with herd-average conception and service rates on large commercial dairies. On-farm herd management software was used as the source of data related to production, reproduction, culling, and milk quality for 108 herds. Also, a survey regarding management, facilities, nutrition, and labor was completed on 86 farms. A total of 41 explanatory variables related to management factors and conditions that could affect conception and service rate were considered in this study. Models explaining conception and service rates were developed using a machine learning algorithm for constructing model trees. The most important explanatory variables associated with conception rate were the percentage of repeated inseminations between 4 and 17 d post-artificial insemination, stocking density in the breeding pen, length of the voluntary waiting period, days at pregnancy examination, and somatic cell score. The most important explanatory variables associated with service rate were the number of lactating cows per breeding technician, use of a resynchronization program, utilization of soakers in the holding area during the summer, and bunk space per cow in the breeding pen. The aforementioned models explained 35% and 40% of the observed variation in conception rate and service rate, respectively, and underline the association of herd-level management factors not strictly related to reproduction with herd reproductive performance. Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Modeling and mapping abundance of American Woodcock across the Midwestern and Northeastern United States

USGS Publications Warehouse

Thogmartin, W.E.; Sauer, J.R.; Knutson, M.G.

2007-01-01

We used an over-dispersed Poisson regression with fixed and random effects, fitted by Markov chain Monte Carlo methods, to model population spatial patterns of relative abundance of American woodcock (Scolopax minor) across its breeding range in the United States. We predicted North American woodcock Singing Ground Survey counts with a log-linear function of explanatory variables describing habitat, year effects, and observer effects. The model also included a conditional autoregressive term representing potential correlation between adjacent route counts. Categories of explanatory habitat variables in the model included land-cover composition, climate, terrain heterogeneity, and human influence. Woodcock counts were higher in landscapes with more forest, especially aspen (Populus tremuloides) and birch (Betula spp.) forest, and in locations with a high degree of interspersion among forest, shrubs, and grasslands. Woodcock counts were lower in landscapes with a high degree of human development. The most noteworthy practical application of this spatial modeling approach was the ability to map predicted relative abundance. Based on a map of predicted relative abundance derived from the posterior parameter estimates, we identified major concentrations of woodcock abundance in east-central Minnesota, USA, the intersection of Vermont, USA, New York, USA, and Ontario, Canada, the upper peninsula of Michigan, USA, and St. Lawrence County, New York. The functional relations we elucidated for the American woodcock provide a basis for the development of management programs and the model and map may serve to focus management and monitoring on areas and habitat features important to American woodcock.

Predictive Models of the Hydrological Regime of Unregulated Streams in Arizona

USGS Publications Warehouse

Anning, David W.; Parker, John T.C.

2009-01-01

Three statistical models were developed by the U.S. Geological Survey in cooperation with the Arizona Department of Environmental Quality to improve the predictability of flow occurrence in unregulated streams throughout Arizona. The models can be used to predict the probabilities of the hydrological regime being one of four categories developed by this investigation: perennial, which has streamflow year-round; nearly perennial, which has streamflow 90 to 99.9 percent of the year; weakly perennial, which has streamflow 80 to 90 percent of the year; or nonperennial, which has streamflow less than 80 percent of the year. The models were developed to assist the Arizona Department of Environmental Quality in selecting sites for participation in the U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program. One model was developed for each of the three hydrologic provinces in Arizona - the Plateau Uplands, the Central Highlands, and the Basin and Range Lowlands. The models for predicting the hydrological regime were calibrated using statistical methods and explanatory variables of discharge, drainage-area, altitude, and location data for selected U.S. Geological Survey streamflow-gaging stations and a climate index derived from annual precipitation data. Models were calibrated on the basis of streamflow data from 46 stations for the Plateau Uplands province, 82 stations for the Central Highlands province, and 90 stations for the Basin and Range Lowlands province. The models were developed using classification trees that facilitated the analysis of mixed numeric and factor variables. In all three models, a threshold stream discharge was the initial variable to be considered within the classification tree and was the single most important explanatory variable. If a stream discharge value at a station was below the threshold, then the station record was determined as being nonperennial. If, however, the stream discharge was above the threshold, subsequent decisions were made according to the classification tree and explanatory variables to determine the hydrological regime of the reach as being perennial, nearly perennial, weakly perennial, or nonperennial. Using model calibration data, misclassification rates for each model were 17 percent for the Plateau Uplands, 15 percent for the Central Highlands, and 14 percent for the Basin and Range Lowlands models. The actual misclassification rate may be higher; however, the model has not been field verified for a full error assessment. The calibrated models were used to classify stream reaches for which the Arizona Department of Environmental Quality had collected miscellaneous discharge measurements. A total of 5,080 measurements at 696 sites were routed through the appropriate classification tree to predict the hydrological regime of the reaches in which the measurements were made. The predictions resulted in classification of all stream reaches as perennial or nonperennial; no reaches were predicted as nearly perennial or weakly perennial. The percentages of sites predicted as being perennial and nonperennial, respectively, were 77 and 23 for the Plateau Uplands, 87 and 13 for the Central Highlands, and 76 and 24 for the Basin and Range Lowlands.

Does One Size Fit All? University Differences in the Influence of Wages, Financial Aid, and Integration on Student Retention

ERIC Educational Resources Information Center

Kerkvliet, J.; Nowell, C.

2005-01-01

We develop and empirically implement a model of university student retention using opportunity cost, financial aid, academic and social integration, and students' background explanatory variables. For one year, we tracked students from Weber State University (WSU) and Oregon State University (OSU) to learn whether they remained enrolled for 0, 1,…

Self-Regulatory Climate: A Social Resource for Student Regulation and Achievement

ERIC Educational Resources Information Center

Adams, Curt M.; Forsyth, Patrick B.; Dollarhide, Ellen; Miskell, Ryan; Ware, Jordan

2015-01-01

Background/Context: Schools have differential effects on student learning and development, but research has not generated much explanatory evidence of the social-psychological pathway to better achievement outcomes. Explanatory evidence of how normative conditions enable students to thrive is particularly relevant in the urban context where…

Validation of an explanatory tool for data-fused displays for high-technology future aircraft

NASA Astrophysics Data System (ADS)

Fletcher, Georgina C. L.; Shanks, Craig R.; Selcon, Stephen J.

1996-05-01

As the number of sensor and data sources in the military cockpit increases, pilots will suffer high levels of workload which could result in reduced performance and the loss of situational awareness. A DRA research program has been investigating the use of data-fused displays in decision support and has developed and laboratory-tested an explanatory tool for displaying information in air combat scenarios. The tool has been designed to provide pictorial explanations of data that maintain situational awareness by involving the pilot in the hostile aircraft threat assessment task. This paper reports a study carried out to validate the success of the explanatory tool in a realistic flight simulation facility. Aircrew were asked to perform a threat assessment task, either with or without the explanatory tool providing information in the form of missile launch success zone envelopes, while concurrently flying a waypoint course within set flight parameters. The results showed that there was a significant improvement (p less than 0.01) in threat assessment accuracy of 30% when using the explanatory tool. This threat assessment performance advantage was achieved without a trade-off with flying task performance. Situational awareness measures showed no general differences between the explanatory and control conditions, but significant learning effects suggested that the explanatory tool makes the task initially more intuitive and hence less demanding on the pilots' attentional resources. The paper concludes that DRA's data-fused explanatory tool is successful at improving threat assessment accuracy in a realistic simulated flying environment, and briefly discusses the requirements for further research in the area.

Logistic regression modeling to assess groundwater vulnerability to contamination in Hawaii, USA

NASA Astrophysics Data System (ADS)

Mair, Alan; El-Kadi, Aly I.

2013-10-01

Capture zone analysis combined with a subjective susceptibility index is currently used in Hawaii to assess vulnerability to contamination of drinking water sources derived from groundwater. In this study, we developed an alternative objective approach that combines well capture zones with multiple-variable logistic regression (LR) modeling and applied it to the highly-utilized Pearl Harbor and Honolulu aquifers on the island of Oahu, Hawaii. Input for the LR models utilized explanatory variables based on hydrogeology, land use, and well geometry/location. A suite of 11 target contaminants detected in the region, including elevated nitrate (> 1 mg/L), four chlorinated solvents, four agricultural fumigants, and two pesticides, was used to develop the models. We then tested the ability of the new approach to accurately separate groups of wells with low and high vulnerability, and the suitability of nitrate as an indicator of other types of contamination. Our results produced contaminant-specific LR models that accurately identified groups of wells with the lowest/highest reported detections and the lowest/highest nitrate concentrations. Current and former agricultural land uses were identified as significant explanatory variables for eight of the 11 target contaminants, while elevated nitrate was a significant variable for five contaminants. The utility of the combined approach is contingent on the availability of hydrologic and chemical monitoring data for calibrating groundwater and LR models. Application of the approach using a reference site with sufficient data could help identify key variables in areas with similar hydrogeology and land use but limited data. In addition, elevated nitrate may also be a suitable indicator of groundwater contamination in areas with limited data. The objective LR modeling approach developed in this study is flexible enough to address a wide range of contaminants and represents a suitable addition to the current subjective approach.

Using Indirect Turbulence Measurements for Real-Time Parameter Estimation in Turbulent Air

NASA Technical Reports Server (NTRS)

Martos, Borja; Morelli, Eugene A.

2012-01-01

The use of indirect turbulence measurements for real-time estimation of parameters in a linear longitudinal dynamics model in atmospheric turbulence was studied. It is shown that measuring the atmospheric turbulence makes it possible to treat the turbulence as a measured explanatory variable in the parameter estimation problem. Commercial off-the-shelf sensors were researched and evaluated, then compared to air data booms. Sources of colored noise in the explanatory variables resulting from typical turbulence measurement techniques were identified and studied. A major source of colored noise in the explanatory variables was identified as frequency dependent upwash and time delay. The resulting upwash and time delay corrections were analyzed and compared to previous time shift dynamic modeling research. Simulation data as well as flight test data in atmospheric turbulence were used to verify the time delay behavior. Recommendations are given for follow on flight research and instrumentation.

Direction dependence analysis: A framework to test the direction of effects in linear models with an implementation in SPSS.

PubMed

Wiedermann, Wolfgang; Li, Xintong

2018-04-16

In nonexperimental data, at least three possible explanations exist for the association of two variables x and y: (1) x is the cause of y, (2) y is the cause of x, or (3) an unmeasured confounder is present. Statistical tests that identify which of the three explanatory models fits best would be a useful adjunct to the use of theory alone. The present article introduces one such statistical method, direction dependence analysis (DDA), which assesses the relative plausibility of the three explanatory models on the basis of higher-moment information about the variables (i.e., skewness and kurtosis). DDA involves the evaluation of three properties of the data: (1) the observed distributions of the variables, (2) the residual distributions of the competing models, and (3) the independence properties of the predictors and residuals of the competing models. When the observed variables are nonnormally distributed, we show that DDA components can be used to uniquely identify each explanatory model. Statistical inference methods for model selection are presented, and macros to implement DDA in SPSS are provided. An empirical example is given to illustrate the approach. Conceptual and empirical considerations are discussed for best-practice applications in psychological data, and sample size recommendations based on previous simulation studies are provided.

Towards a General Theory of Immunity?

PubMed

Eberl, Gérard; Pradeu, Thomas

2018-04-01

Theories are indispensable to organize immunological data into coherent, explanatory, and predictive frameworks. We propose to combine different models to develop a unifying theory of immunity which situates immunology in the wider context of physiology. We believe that the immune system will be increasingly understood as a central component of a network of partner physiological systems that interconnect to maintain homeostasis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Child Effortful Control as a Mediator of Parenting Practices on Externalizing Behavior: Evidence for a Sex-Differentiated Pathway across the Transition from Preschool to School

ERIC Educational Resources Information Center

Chang, Hyein; Olson, Sheryl L.; Sameroff, Arnold J.; Sexton, Holly R.

2011-01-01

An explanatory model for children's development of disruptive behavior across the transition from preschool to school was tested. It was hypothesized that child effortful control would mediate the effects of parenting on children's externalizing behavior and that child sex would moderate these relations. Participants were 241 children (123 boys)…

Modeling Effects of Temperature, Soil, Moisture, Nutrition and Variety As Determinants of Severity of Pythium Damping-Off and Root Disease in Subterranean Clover

PubMed Central

You, Ming P.; Rensing, Kelly; Renton, Michael; Barbetti, Martin J.

2017-01-01

Subterranean clover (Trifolium subterraneum) is a critical pasture legume in Mediterranean regions of southern Australia and elsewhere, including Mediterranean-type climatic regions in Africa, Asia, Australia, Europe, North America, and South America. Pythium damping-off and root disease caused by Pythium irregulare is a significant threat to subterranean clover in Australia and a study was conducted to define how environmental factors (viz. temperature, soil type, moisture and nutrition) as well as variety, influence the extent of damping-off and root disease as well as subterranean clover productivity under challenge by this pathogen. Relationships were statistically modeled using linear and generalized linear models and boosted regression trees. Modeling found complex relationships between explanatory variables and the extent of Pythium damping-off and root rot. Linear modeling identified high-level (4 or 5-way) significant interactions for each dependent variable (dry shoot and root weight, emergence, tap and lateral root disease index). Furthermore, all explanatory variables (temperature, soil, moisture, nutrition, variety) were found significant as part of some interaction within these models. A significant five-way interaction between all explanatory variables was found for both dry shoot and root dry weights, and a four way interaction between temperature, soil, moisture, and nutrition was found for both tap and lateral root disease index. A second approach to modeling using boosted regression trees provided support for and helped clarify the complex nature of the relationships found in linear models. All explanatory variables showed at least 5% relative influence on each of the five dependent variables. All models indicated differences due to soil type, with the sand-based soil having either higher weights, greater emergence, or lower disease indices; while lowest weights and less emergence, as well as higher disease indices, were found for loam soil and low temperature. There was more severe tap and lateral root rot disease in higher moisture situations. PMID:29184544

[Functional somatic syndromes from the view of cultural anthropology].

PubMed

Nakagami, Ayako; Tsujiuchi, Takuya

2009-09-01

The functional somatic syndromes have acquired major socio-cultural and political dimensions. Socio-cultural factors clearly affect symptoms, suffering, and disability perception and reporting. And knowledge of explanatory models of bodily distress for patients from different cultural backgrounds is useful in the establishment of a stable doctor -patient relationship. FSS may be an operational category to bridge between medical explanatory model and patient's model. According to medical anthropology, sickness has two faces; illness and disease. "Disease" is the problem from the practitioner's perspective, and "illness" is the human experience of symptoms and suffering. In this paper, the anthropological research on chronic fatigue syndrome as "not real" illness experience was described.

Surrogate analysis and index developer (SAID) tool and real-time data dissemination utilities

USGS Publications Warehouse

Domanski, Marian M.; Straub, Timothy D.; Wood, Molly S.; Landers, Mark N.; Wall, Gary R.; Brady, Steven J.

2015-01-01

The use of acoustic and other parameters as surrogates for suspended-sediment concentrations (SSC) in rivers has been successful in multiple applications across the Nation. Critical to advancing the operational use of surrogates are tools to process and evaluate the data along with the subsequent development of regression models from which real-time sediment concentrations can be made available to the public. Recent developments in both areas are having an immediate impact on surrogate research, and on surrogate monitoring sites currently in operation. The Surrogate Analysis and Index Developer (SAID) standalone tool, under development by the U.S. Geological Survey (USGS), assists in the creation of regression models that relate response and explanatory variables by providing visual and quantitative diagnostics to the user. SAID also processes acoustic parameters to be used as explanatory variables for suspended-sediment concentrations. The sediment acoustic method utilizes acoustic parameters from fixed-mount stationary equipment. The background theory and method used by the tool have been described in recent publications, and the tool also serves to support sediment-acoustic-index methods being drafted by the multi-agency Sediment Acoustic Leadership Team (SALT), and other surrogate guidelines like USGS Techniques and Methods 3-C4 for turbidity and SSC. The regression models in SAID can be used in utilities that have been developed to work with the USGS National Water Information System (NWIS) and for the USGS National Real-Time Water Quality (NRTWQ) Web site. The real-time dissemination of predicted SSC and prediction intervals for each time step has substantial potential to improve understanding of sediment-related water-quality and associated engineering and ecological management decisions.

Revising and assessing explanatory models in a high school genetics class: A comparison of unsuccessful and successful performance

NASA Astrophysics Data System (ADS)

Johnson, Susan K.; Stewart, Jim

2002-07-01

In this paper we describe the model-revising problem-solving strategies of two groups of students (one successful, one unsuccessful) as they worked (in a genetics course we developed) to revise Mendel's simple dominance model to explain the inheritance of a trait expressed in any of four variations. The two groups described in this paper were chosen with the intent that the strategies that they employed be used to inform the design of model-based instruction. Differences were found in the groups' abilities to recognize anomalous data, use existing models as templates for revisions, and assess revised models.

A Pessimistic Explanatory Style is Prognostic for Poor Lung Cancer Survival

PubMed Central

Novotny, Paul; Colligan, Robert C.; Szydlo, Daniel W.; Clark, Matthew M.; Rausch, Sarah; Wampfler, Jason; Sloan, Jeff A.; Yang, Ping

2010-01-01

Background Several studies have demonstrated the importance of personality constructs on health behaviors and health status. Having a pessimistic outlook has been related to negative health behaviors and higher mortality. However, the construct has not been well explored in cancer populations. Methods Survival time of 534 adults, who were diagnosed with lung cancer and had a pessimistic explanatory style, was examined. The patients had completed the Minnesota Multiphasic Personality Inventory (MMPI) approximately 18.2 years prior to receiving their lung cancer diagnosis. MMPI Optimism-Pessimism (PSM) scores were divided into high (60 or more) and low scores (less than 60), and log-rank tests and Kaplan-Meier curves were used to determine survival differences. Multivariate Cox models were used for assessing prognostic values of pessimism along with other known predictors for lung cancer survival outcome. Booting strapping of the survival models was used as a sensitivity analysis. Results At the time of lung cancer diagnosis, patients were on average 67 years old; 48% were female; 85% had non-small cell lung cancer (NSCLC); 15% had small cell lung cancer (SCLC); 30% were stage I; 4% were stage II; 31% were stage III/limited; and 35% were stage IV/extensive. Patients who exhibited a non-pessimistic explanatory style survived approximately six months longer than patients classified as having a pessimistic explanatory style. Conclusion Among lung cancer patients, those having a pessimistic explanatory style experienced less favorable survival outcome, which may be related to cancer treatment decisions. Further research in this area is warranted. PMID:20139778

A Predictive Model of Domestic Violence in Multicultural Families Focusing on Perpetrator.

PubMed

Choi, Eun Young; Hyun, Hye Jin

2016-09-01

This study was conducted to assess predictor variables of husbands in multicultural families and examine the relationship among variables after setting up a hypothetical model including influencing factors, so as to provide a framework necessary for developing nursing interventions of domestic violence. The participants were 260 husbands in multicultural families in four cities in Korea. Data were analyzed using SPSS 22.0 and AMOS 20.0. Self-control, social support, family of origin violence experience and stress on cultural adaptation directly affected to dysfunctional communication, and the explanatory power of the variables was 64.7%. Family of origin violence experience in domestic stress on cultural adaptation, and dysfunctional communication were directly related to domestic violence in multicultural families, and the explanatory power of the variables was 64.6%. We found out that all variables in the model had mediation effects to domestic violence through dysfunctional communication. In other words, self-control and social support had complete mediation effects, and family of origin violence experience in domestic violence and stress on cultural adaptation had partial mediation effects. The variables explained in this study should be considered as predictive factors of domestic violence in multicultural families, and used to provide preventive nursing intervention. Our resutls can be taken into account for developing and implementing programs on alleviating dysfunctional communication in multicultural families in Korea. Copyright © 2016. Published by Elsevier B.V.

Establishing an Explanatory Model for Mathematics Identity.

PubMed

Cribbs, Jennifer D; Hazari, Zahra; Sonnert, Gerhard; Sadler, Philip M

2015-04-01

This article empirically tests a previously developed theoretical framework for mathematics identity based on students' beliefs. The study employs data from more than 9,000 college calculus students across the United States to build a robust structural equation model. While it is generally thought that students' beliefs about their own competence in mathematics directly impact their identity as a "math person," findings indicate that students' self-perceptions related to competence and performance have an indirect effect on their mathematics identity, primarily by association with students' interest and external recognition in mathematics. Thus, the model indicates that students' competence and performance beliefs are not sufficient for their mathematics identity development, and it highlights the roles of interest and recognition. © 2015 The Authors. Child Development © 2015 Society for Research in Child Development, Inc.

Impact of managed care on physicians' decisions to manipulate reimbursement rules: an explanatory model.

PubMed

VanGeest, Jonathan; Weiner, Saul; Johnson, Timothy; Cummins, Deborah

2007-07-01

To develop and test an explanatory model of the impact of managed care on physicians' decisions to manipulate reimbursement rules for patients. A self-administered mailed questionnaire of a national random sample of 1124 practicing physicians in the USA. Structural equation modelling was used. The main outcome measure assessed whether or not physicians had manipulated reimbursement rules (such as exaggerated the severity of patients conditions, changed billing diagnoses, or reported signs or symptoms that the patients did not have) to help patients secure coverage for needed treatment or services. The response rate was 64% (n = 720). Physicians' decisions to manipulate reimbursement rules for patients are directly driven not only by ethical beliefs about gaming the system but also by requests from patients, the perception of insufficient time to deliver care, and the proportion of Medicaid patients. Covert advocacy is also the indirect result of utilization review hassles, primary care specialty, and practice environment. Managed care is not just a set of rules that physicians choose to follow or disobey, but an environment of competing pressures from patients, purchasers, and high workload. Reimbursement manipulation is a response to that environment, rather than simply a reflection of individual physicians' values.

How relevant is environmental quality to per capita health expenditures? Empirical evidence from panel of developing countries.

PubMed

Yahaya, Adamu; Nor, Norashidah Mohamed; Habibullah, Muzafar Shah; Ghani, Judhiana Abd; Noor, Zaleha Mohd

2016-01-01

Developing countries have witnessed economic growth as their GDP keeps increasing steadily over the years. The growth led to higher energy consumption which eventually leads to increase in air pollutions that pose a danger to human health. People's healthcare demand, in turn, increase due to the changes in the socioeconomic life and improvement in the health technology. This study is an attempt to investigate the impact of environmental quality on per capital health expenditure in 125 developing countries within a panel cointegration framework from 1995 to 2012. We found out that a long-run relationship exists between per capita health expenditure and all explanatory variables as they were panel cointegrated. The explanatory variables were found to be statistically significant in explaining the per capita health expenditure. The result further revealed that CO2 has the highest explanatory power on the per capita health expenditure. The impact of the explanatory power of the variables is greater in the long-run compared to the short-run. Based on this result, we conclude that environmental quality is a powerful determinant of health expenditure in developing countries. Therefore, developing countries should as a matter of health care policy give provision of healthy air a priority via effective policy implementation on environmental management and control measures to lessen the pressure on health care expenditure. Moreover more environmental proxies with alternative methods should be considered in the future research.

Hill's Heuristics and Explanatory Coherentism in Epidemiology.

PubMed

Dammann, Olaf

2018-01-01

In this essay, I argue that Ted Poston's theory of explanatory coherentism is well-suited as a tool for causal explanation in the health sciences, particularly in epidemiology. Coherence has not only played a role in epidemiology for more than half a century as one of Hill's viewpoints, it can also provide background theory for the development of explanatory systems by integrating epidemiologic evidence with a diversity of other error-independent data. I propose that computational formalization of Hill's viewpoints in an explanatory coherentist framework would provide an excellent starting point for a formal epistemological (knowledge-theoretical) project designed to improve causal explanation in the health sciences. As an example, I briefly introduce Paul Thagard's ECHO system and offer my responses to possible objections to my proposal. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A case study of alternative site response explanatory variables in Parkfield, California

USGS Publications Warehouse

Thompson, E.M.; Baise, L.G.; Kayen, R.E.; Morgan, E.C.; Kaklamanos, J.

2011-01-01

The combination of densely-spaced strong-motion stations in Parkfield, California, and spectral analysis of surface waves (SASW) profiles provides an ideal dataset for assessing the accuracy of different site response explanatory variables. We judge accuracy in terms of spatial coverage and correlation with observations. The performance of the alternative models is period-dependent, but generally we observe that: (1) where a profile is available, the square-root-of-impedance method outperforms VS30 (average S-wave velocity to 30 m depth), and (2) where a profile is unavailable, the topographic-slope method outperforms surficial geology. The fundamental site frequency is a valuable site response explanatory variable, though less valuable than VS30. However, given the expense and difficulty of obtaining reliable estimates of VS30 and the relative ease with which the fundamental site frequency can be computed, the fundamental site frequency may prove to be a valuable site response explanatory variable for many applications. ?? 2011 ASCE.

Regression models for estimating concentrations of atrazine plus deethylatrazine in shallow groundwater in agricultural areas of the United States

USGS Publications Warehouse

Stackelberg, Paul E.; Barbash, Jack E.; Gilliom, Robert J.; Stone, Wesley W.; Wolock, David M.

2012-01-01

Tobit regression models were developed to predict the summed concentration of atrazine [6-chloro-N-ethyl-N'-(1-methylethyl)-1,3,5-triazine-2,4-diamine] and its degradate deethylatrazine [6-chloro-N-(1-methylethyl)-1,3,5,-triazine-2,4-diamine] (DEA) in shallow groundwater underlying agricultural settings across the conterminous United States. The models were developed from atrazine and DEA concentrations in samples from 1298 wells and explanatory variables that represent the source of atrazine and various aspects of the transport and fate of atrazine and DEA in the subsurface. One advantage of these newly developed models over previous national regression models is that they predict concentrations (rather than detection frequency), which can be compared with water quality benchmarks. Model results indicate that variability in the concentration of atrazine residues (atrazine plus DEA) in groundwater underlying agricultural areas is more strongly controlled by the history of atrazine use in relation to the timing of recharge (groundwater age) than by processes that control the dispersion, adsorption, or degradation of these compounds in the saturated zone. Current (1990s) atrazine use was found to be a weak explanatory variable, perhaps because it does not represent the use of atrazine at the time of recharge of the sampled groundwater and because the likelihood that these compounds will reach the water table is affected by other factors operating within the unsaturated zone, such as soil characteristics, artificial drainage, and water movement. Results show that only about 5% of agricultural areas have greater than a 10% probability of exceeding the USEPA maximum contaminant level of 3.0 μg L-1. These models are not developed for regulatory purposes but rather can be used to (i) identify areas of potential concern, (ii) provide conservative estimates of the concentrations of atrazine residues in deeper potential drinking water supplies, and (iii) set priorities among areas for future groundwater monitoring.

Developing Passenger Demand Models for International Aviation from/to Egypt: A Case Study of Cairo Airport and Egyptair

NASA Technical Reports Server (NTRS)

Abbas, Khaled A.; Fattah, Nabil Abdel; Reda, Hala R.

2003-01-01

This research is concerned with developing passenger demand models for international aviation from/to Egypt. In this context, aviation sector in Egypt is represented by the biggest and main airport namely Cairo airport as well as by the main Egyptian international air carrier namely Egyptair. The developed models utilize two variables to represent aviation demand, namely total number of international flights originating from and attracted to Cairo airport as well as total number of passengers using Egyptair international flights originating from and attracted to Cairo airport. Such demand variables were related, using different functional forms, to several explanatory variables including population, GDP and number of foreign tourists. Finally, two models were selected based on their logical acceptability, best fit and statistical significance. To demonstrate usefulness of developed models, these were used to forecast future demand patterns.

Mendelian genetics: Paradigm, conjecture, or research program

NASA Astrophysics Data System (ADS)

Oldham, V.; Brouwer, W.

Kuhn's model of the structure of scientific revolutions, Popper's hypothetic-deductive model of science, and Lakatos's methodology of competing research programs are applied to a historical episode in biology. Each of these three models offers a different explanatory system for the development, neglect, and eventual acceptance of Mendel's paradigm of inheritance. The authors conclude that both rational and nonrational criteria play an important role during times of crisis in science, when different research programs compete for acceptance. It is suggested that Kuhn's model, emphasizing the nonrational basis of science, and Popper's model, emphasizing the rational basis of science, can be used fruitfully in high school science courses.

Response, Resistance, or Restraint: A Triadic Model of Pre-Service Teachers' Perceptions on the (F)utility of Educational Therapy and Life Skills Education in ELT

ERIC Educational Resources Information Center

Tavakoli, Mansoor; Zabihi, Reza; Ghadiri, Momene

2017-01-01

The improvement of mental health has been given considerable attention in educational settings since more than four decades. Accordingly, many programs were developed with the purpose of enabling children not only to deal with educational issues, but also to resolve their psychosocial problems. This study used an Explanatory Mixed-Methods Research…

The French School System and the Universalist Metanarrative (1880-2000s): Some Reflections about So-Called Explanatory Historical Notions Such as "La Forme Scolaire"

ERIC Educational Resources Information Center

Robert, Andre D.

2013-01-01

This article aims to question the relevance of notions such as "laforme scolair"' in the account of the French state action in keeping up with the development of mass schooling, over a long historical process (from the late nineteenth century to the present day). Through its origin, this model is linked to a Universalist philosophical…

Global Aerospace Industries: Rapid Changes Ahead? (Abridged)

DTIC Science & Technology

2012-04-30

Understanding the Situation: Contestable Markets • Central idea: the extent to which markets are “contestable” causes monopolists and oligopolists to behave...find useful explanatory models for Boeing?s success, discussed in Chapter II. In Chapter III, we consider the narrow-body airliner market , currently...families have provided resources for a number of wide-body developments some of which have become part of the defense marketplace. The narrow-body market

Explanatory model of emotional-cognitive variables in school mathematics performance: a longitudinal study in primary school.

PubMed

Cerda, Gamal; Pérez, Carlos; Navarro, José I; Aguilar, Manuel; Casas, José A; Aragón, Estíbaliz

2015-01-01

This study tested a structural model of cognitive-emotional explanatory variables to explain performance in mathematics. The predictor variables assessed were related to students' level of development of early mathematical competencies (EMCs), specifically, relational and numerical competencies, predisposition toward mathematics, and the level of logical intelligence in a population of primary school Chilean students (n = 634). This longitudinal study also included the academic performance of the students during a period of 4 years as a variable. The sampled students were initially assessed by means of an Early Numeracy Test, and, subsequently, they were administered a Likert-type scale to measure their predisposition toward mathematics (EPMAT) and a basic test of logical intelligence. The results of these tests were used to analyse the interaction of all the aforementioned variables by means of a structural equations model. This combined interaction model was able to predict 64.3% of the variability of observed performance. Preschool students' performance in EMCs was a strong predictor for achievement in mathematics for students between 8 and 11 years of age. Therefore, this paper highlights the importance of EMCs and the modulating role of predisposition toward mathematics. Also, this paper discusses the educational role of these findings, as well as possible ways to improve negative predispositions toward mathematical tasks in the school domain.

Afference copy as a quantitative neurophysiological model for consciousness.

PubMed

Cornelis, Hugo; Coop, Allan D

2014-06-01

Consciousness is a topic of considerable human curiosity with a long history of philosophical analysis and debate. We consider there is nothing particularly complicated about consciousness when viewed as a necessary process of the vertebrate nervous system. Here, we propose a physiological "explanatory gap" is created during each present moment by the temporal requirements of neuronal activity. The gap extends from the time exteroceptive and proprioceptive stimuli activate the nervous system until they emerge into consciousness. During this "moment", it is impossible for an organism to have any conscious knowledge of the ongoing evolution of its environment. In our schematic model, a mechanism of "afference copy" is employed to bridge the explanatory gap with consciously experienced percepts. These percepts are fabricated from the conjunction of the cumulative memory of previous relevant experience and the given stimuli. They are structured to provide the best possible prediction of the expected content of subjective conscious experience likely to occur during the period of the gap. The model is based on the proposition that the neural circuitry necessary to support consciousness is a product of sub/preconscious reflexive learning and recall processes. Based on a review of various psychological and neurophysiological findings, we develop a framework which contextualizes the model and briefly discuss further implications.

Multiple causes of nonstationarity in the Weihe annual low-flow series

NASA Astrophysics Data System (ADS)

Xiong, Bin; Xiong, Lihua; Chen, Jie; Xu, Chong-Yu; Li, Lingqi

2018-02-01

Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, through a nonstationary frequency analysis framework with the generalized linear model (GLM) to consider time-varying distribution parameters, the multiple explanatory variables were incorporated to explain the variation in low-flow distribution parameters. These variables are comprised of the three indices of human activities (HAs; i.e., population, POP; irrigation area, IAR; and gross domestic product, GDP) and the eight measuring indices of the climate and catchment conditions (i.e., total precipitation P, mean frequency of precipitation events λ, temperature T, potential evapotranspiration (EP), climate aridity index AIEP, base-flow index (BFI), recession constant K and the recession-related aridity index AIK). This framework was applied to model the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise regression for the optimal explanatory variables show that the variables related to irrigation, recession, temperature and precipitation play an important role in modeling. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that the nonstationary distribution model with any one of all explanatory variables is better than the one without explanatory variables, the nonstationary gamma distribution model with four optimal variables is the best model and AIK is of the highest relative importance among these four variables, followed by IAR, BFI and AIEP. We conclude that the incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to analyze future occurrences of low-flow extremes in similar areas.

The coexistence of natural and supernatural explanations within and across domains and development.

PubMed

Busch, Justin T A; Watson-Jones, Rachel E; Legare, Cristine H

2017-03-01

People across highly diverse cultural contexts use both natural and supernatural explanations to explain questions of fundamental concern such as death, illness, and human origins. The present study examines the development of explanatory coexistence within and across domains of existential concern in individuals in Tanna, Vanuatu. We examined three age groups: 7- to 12-year-old children, 13- to 18-year-old adolescents, and 19- to 70-year-old adults (N = 72). Within the domain of death, biological and spontaneous explanations were most common across all ages. For illness, children showed the highest rates of explanatory coexistence, while adolescents and adults favoured biological explanations. Within the human origins domain, theistic explanations were most common across the age groups. Overall, these data show that coexistence reasoning in these domains is pervasive across cultures, yet at the same time it is deeply contextually specific, reflecting the nuanced differences in local ecologies and cultural beliefs. Statement of contribution What is already known on this subject? Individuals across highly diverse cultural contexts use both natural and supernatural explanations to understand the events that occur in their lives. Context and cultural input play a large role in determining when and how individuals incorporate natural and supernatural explanations. The development of explanatory coexistence has primarily studied explanations for isolated domains. What does this study add? We examined explanatory coexistence in a culture with recent conversion to Christianity and formal education. The current research examines how individuals reason within and across the domains of human origins, illness, and death. Developmental differences associated with explanatory coexistence are examined. © 2016 The British Psychological Society.

Learned Social Hopelessness: The Role of Explanatory Style in Predicting Social Support during Adolescence

ERIC Educational Resources Information Center

Ciarrochi, Joseph; Heaven, Patrick C. L.

2008-01-01

Background: Almost no research has examined the impact of explanatory style on social adjustment. We hypothesised that adolescents with a pessimistic style would be less likely to develop and maintain social support networks. Methods: Seven hundred and nineteen students (351 males and 366 females; 2 unknown; M[subscript AGE] = 12.28, SD = 0.49)…

Causes of Job Turnover in the Public School Superintendency: An Explanatory Analysis in the Western United States

ERIC Educational Resources Information Center

Melver, Toby A.

2011-01-01

The purpose of this mixed-methods study was to determine the factors that affect public school superintendent turnover in five western states. An explanatory theory was developed to cover all of the possible variables and show the relationship between those variables. The questions that guided this research study were: (1) What environmental…

The extension of total gain (TG) statistic in survival models: properties and applications.

PubMed

Choodari-Oskooei, Babak; Royston, Patrick; Parmar, Mahesh K B

2015-07-01

The results of multivariable regression models are usually summarized in the form of parameter estimates for the covariates, goodness-of-fit statistics, and the relevant p-values. These statistics do not inform us about whether covariate information will lead to any substantial improvement in prediction. Predictive ability measures can be used for this purpose since they provide important information about the practical significance of prognostic factors. R (2)-type indices are the most familiar forms of such measures in survival models, but they all have limitations and none is widely used. In this paper, we extend the total gain (TG) measure, proposed for a logistic regression model, to survival models and explore its properties using simulations and real data. TG is based on the binary regression quantile plot, otherwise known as the predictiveness curve. Standardised TG ranges from 0 (no explanatory power) to 1 ('perfect' explanatory power). The results of our simulations show that unlike many of the other R (2)-type predictive ability measures, TG is independent of random censoring. It increases as the effect of a covariate increases and can be applied to different types of survival models, including models with time-dependent covariate effects. We also apply TG to quantify the predictive ability of multivariable prognostic models developed in several disease areas. Overall, TG performs well in our simulation studies and can be recommended as a measure to quantify the predictive ability in survival models.

Using Explanatory Item Response Models to Evaluate Complex Scientific Tasks Designed for the Next Generation Science Standards

NASA Astrophysics Data System (ADS)

Chiu, Tina

This dissertation includes three studies that analyze a new set of assessment tasks developed by the Learning Progressions in Middle School Science (LPS) Project. These assessment tasks were designed to measure science content knowledge on the structure of matter domain and scientific argumentation, while following the goals from the Next Generation Science Standards (NGSS). The three studies focus on the evidence available for the success of this design and its implementation, generally labelled as "validity" evidence. I use explanatory item response models (EIRMs) as the overarching framework to investigate these assessment tasks. These models can be useful when gathering validity evidence for assessments as they can help explain student learning and group differences. In the first study, I explore the dimensionality of the LPS assessment by comparing the fit of unidimensional, between-item multidimensional, and Rasch testlet models to see which is most appropriate for this data. By applying multidimensional item response models, multiple relationships can be investigated, and in turn, allow for a more substantive look into the assessment tasks. The second study focuses on person predictors through latent regression and differential item functioning (DIF) models. Latent regression models show the influence of certain person characteristics on item responses, while DIF models test whether one group is differentially affected by specific assessment items, after conditioning on latent ability. Finally, the last study applies the linear logistic test model (LLTM) to investigate whether item features can help explain differences in item difficulties.

Examining the Value of a Scaffolded Critique Framework to Promote Argumentative and Explanatory Writings Within an Argument-Based Inquiry Approach

NASA Astrophysics Data System (ADS)

Jang, Jeong-yoon; Hand, Brian

2017-12-01

This study investigated the value of using a scaffolded critique framework to promote two different types of writing—argumentative writing and explanatory writing—with different purposes within an argument-based inquiry approach known as the Science Writing Heuristic (SWH) approach. A quasi-experimental design with sixth and seventh grade students taught by two teachers was used. A total of 170 students participated in the study, with 87 in the control group (four classes) and 83 in the treatment group (four classes). All students used the SWH templates as an argumentative writing to guide their written work and completed these templates during the SWH investigations of each unit. After completing the SWH investigations, both groups of students were asked to complete the summary writing task as an explanatory writing at the end of each unit. All students' writing samples were scored using analytical frameworks developed for the study. The results indicated that the treatment group performed significantly better on the explanatory writing task than the control group. In addition, the results of the partial correlation suggested that there is a very strong significantly positive relationship between the argumentative writing and the explanatory writing.

Toward a model-based cognitive neuroscience of mind wandering.

PubMed

Hawkins, G E; Mittner, M; Boekel, W; Heathcote, A; Forstmann, B U

2015-12-03

People often "mind wander" during everyday tasks, temporarily losing track of time, place, or current task goals. In laboratory-based tasks, mind wandering is often associated with performance decrements in behavioral variables and changes in neural recordings. Such empirical associations provide descriptive accounts of mind wandering - how it affects ongoing task performance - but fail to provide true explanatory accounts - why it affects task performance. In this perspectives paper, we consider mind wandering as a neural state or process that affects the parameters of quantitative cognitive process models, which in turn affect observed behavioral performance. Our approach thus uses cognitive process models to bridge the explanatory divide between neural and behavioral data. We provide an overview of two general frameworks for developing a model-based cognitive neuroscience of mind wandering. The first approach uses neural data to segment observed performance into a discrete mixture of latent task-related and task-unrelated states, and the second regresses single-trial measures of neural activity onto structured trial-by-trial variation in the parameters of cognitive process models. We discuss the relative merits of the two approaches, and the research questions they can answer, and highlight that both approaches allow neural data to provide additional constraint on the parameters of cognitive models, which will lead to a more precise account of the effect of mind wandering on brain and behavior. We conclude by summarizing prospects for mind wandering as conceived within a model-based cognitive neuroscience framework, highlighting the opportunities for its continued study and the benefits that arise from using well-developed quantitative techniques to study abstract theoretical constructs. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Explanatory models of adult patients with type 2 diabetes mellitus from urban centers of central Ethiopia.

PubMed

Habte, Bruck M; Kebede, Tedla; Fenta, Teferi G; Boon, Heather

2016-09-13

Type 2 diabetes, which is increasing as a public health problem in the low resource settings of Africa has been associated with the high prevalence of micro-vascular complications and increasing levels of macro-vascular complications. There is evidence from the developed world that understanding patient perceptions of chronic illness is important to design effective strategies for helping patients manage these conditions. This study utilized Kleinman's model to explore the illness perceptions of type 2 diabetes patients attending treatment in Addis Ababa and Butajira (Ethiopia) and better understand how they manage their illness. Qualitative interviews were conducted to elicit the explanatory models of purposively sampled type 2 diabetes patients attending treatment in three hospitals in central Ethiopia until saturation of key emerging themes was achieved. Analysis of interview transcripts was guided by Kleinman's model. A total of 39 participants, 24 from Addis Ababa and the rest from Butajira took part in the study. This study revealed that patients' explanatory models were informed by both the traditional and biomedical models with emotional distress evident in some of the participants. The traditional model seemed to reflect the strong religious and cultural influences for the majority of study participants. The findings also revealed that symptoms played significant roles in how patients viewed their illness including assessment of its severity. Most were uncertain about the cause of their illness, with those expressing certainty citing factors over which they believed they had little or no control. This may have contributed to the perceptions about the use of religious healing and traditional medicines in a complementary or alternative manner to the biomedical regimen which could affect their adherence to recommended regimens and their health outcomes. This study suggests the need for a strong diabetes care program that is sensitive to patients' experiences of their illness including emotional distress. Individuals providing the diabetes care should consider local and individual contexts and strive to make their approach patient-centered and engage active participation of patients. There appears to be a need for better training of health providers in different areas including health communications and the fundamentals of mental healthcare.

Family Interaction Patterns Associated with Self-Esteem in Preadolescent Girls and Boys.

ERIC Educational Resources Information Center

Loeb, Roger C.; And Others

1980-01-01

Examines four traditional explanatory models for the influence of parents on children's self-esteem. These models are directiveness, modeling, reward and punishment, and positive family interaction. (Author/DB)

Somatization revisited: diagnosis and perceived causes of common mental disorders.

PubMed

Henningsen, Peter; Jakobsen, Thorsten; Schiltenwolf, Marcus; Weiss, Mitchell G

2005-02-01

The assessment of somatoform disorders is complicated by persistent theoretical and practical questions of classification and assessment. Critical rethinking of professional concepts of somatization suggests the value of complementary assessment of patients' illness explanatory models of somatoform and other common mental disorders. We undertook this prospective study to assess medically unexplained somatic symptoms and their patient-perceived causes of illness and to show how patients' explanatory models relate to professional diagnoses of common mental disorders and how they may predict the short-term course of illness. Tertiary care patients (N=186) with prominent somatoform symptoms were evaluated with the Structured Clinical Interview for DSM-IV, a locally adapted Explanatory Model Interview to elicit patients' illness experience (priority symptoms) and perceived causes, and clinical self-report questionnaires. The self-report questionnaires were administered at baseline and after 6 months. Diagnostic overlap between somatoform, depressive, and anxiety disorders occurred frequently (79.6%). Patients explained pure somatoform disorders mainly with organic causal attributions; they explained pure depressive and/or anxiety disorders mainly with psychosocial perceived causes, and patients in the diagnostic overlap group typically reported mixed causal attributions. In this last group, among patients with similar levels of symptom severity, organic perceived causes were related to a lower physical health sum score on the MOS Short Form, and psychosocial perceived causes were related to less severe depressive symptoms, assessed with the Hospital Anxiety and Depression Scale at 6 months. Among patients meeting criteria for comorbid somatoform with anxiety and/or depressive disorders, complementary assessment of patient-perceived causes, a key element of illness explanatory models, was related to levels of functional impairment and short-term prognosis. For such patients, causal attributions may be particularly useful to clarify clinically significant features of common mental disorders and thereby contribute to clinical assessment.

Seasonally adjusted birth frequencies follow the Poisson distribution.

PubMed

Barra, Mathias; Lindstrøm, Jonas C; Adams, Samantha S; Augestad, Liv A

2015-12-15

Variations in birth frequencies have an impact on activity planning in maternity wards. Previous studies of this phenomenon have commonly included elective births. A Danish study of spontaneous births found that birth frequencies were well modelled by a Poisson process. Somewhat unexpectedly, there were also weekly variations in the frequency of spontaneous births. Another study claimed that birth frequencies follow the Benford distribution. Our objective was to test these results. We analysed 50,017 spontaneous births at Akershus University Hospital in the period 1999-2014. To investigate the Poisson distribution of these births, we plotted their variance over a sliding average. We specified various Poisson regression models, with the number of births on a given day as the outcome variable. The explanatory variables included various combinations of years, months, days of the week and the digit sum of the date. The relationship between the variance and the average fits well with an underlying Poisson process. A Benford distribution was disproved by a goodness-of-fit test (p < 0.01). The fundamental model with year and month as explanatory variables is significantly improved (p < 0.001) by adding day of the week as an explanatory variable. Altogether 7.5% more children are born on Tuesdays than on Sundays. The digit sum of the date is non-significant as an explanatory variable (p = 0.23), nor does it increase the explained variance. INERPRETATION: Spontaneous births are well modelled by a time-dependent Poisson process when monthly and day-of-the-week variation is included. The frequency is highest in summer towards June and July, Friday and Tuesday stand out as particularly busy days, and the activity level is at its lowest during weekends.

Comparison between splines and fractional polynomials for multivariable model building with continuous covariates: a simulation study with continuous response.

PubMed

Binder, Harald; Sauerbrei, Willi; Royston, Patrick

2013-06-15

In observational studies, many continuous or categorical covariates may be related to an outcome. Various spline-based procedures or the multivariable fractional polynomial (MFP) procedure can be used to identify important variables and functional forms for continuous covariates. This is the main aim of an explanatory model, as opposed to a model only for prediction. The type of analysis often guides the complexity of the final model. Spline-based procedures and MFP have tuning parameters for choosing the required complexity. To compare model selection approaches, we perform a simulation study in the linear regression context based on a data structure intended to reflect realistic biomedical data. We vary the sample size, variance explained and complexity parameters for model selection. We consider 15 variables. A sample size of 200 (1000) and R(2)  = 0.2 (0.8) is the scenario with the smallest (largest) amount of information. For assessing performance, we consider prediction error, correct and incorrect inclusion of covariates, qualitative measures for judging selected functional forms and further novel criteria. From limited information, a suitable explanatory model cannot be obtained. Prediction performance from all types of models is similar. With a medium amount of information, MFP performs better than splines on several criteria. MFP better recovers simpler functions, whereas splines better recover more complex functions. For a large amount of information and no local structure, MFP and the spline procedures often select similar explanatory models. Copyright © 2012 John Wiley & Sons, Ltd.

The Social Explanatory Styles Questionnaire: Assessing Moderators of Basic Social-Cognitive Phenomena Including Spontaneous Trait Inference, the Fundamental Attribution Error, and Moral Blame

PubMed Central

Gill, Michael J.; Andreychik, Michael R.

2014-01-01

Why is he poor? Why is she failing academically? Why is he so generous? Why is she so conscientious? Answers to such everyday questions—social explanations—have powerful effects on relationships at the interpersonal and societal levels. How do people select an explanation in particular cases? We suggest that, often, explanations are selected based on the individual's pre-existing general theories of social causality. More specifically, we suggest that over time individuals develop general beliefs regarding the causes of social events. We refer to these beliefs as social explanatory styles. Our goal in the present article is to offer and validate a measure of individual differences in social explanatory styles. Accordingly, we offer the Social Explanatory Styles Questionnaire (SESQ), which measures three independent dimensions of social explanatory style: Dispositionism, historicism, and controllability. Studies 1–3 examine basic psychometric properties of the SESQ and provide positive evidence regarding internal consistency, factor structure, and both convergent and divergent validity. Studies 4–6 examine predictive validity for each subscale: Does each explanatory dimension moderate an important phenomenon of social cognition? Results suggest that they do. In Study 4, we show that SESQ dispositionism moderates the tendency to make spontaneous trait inferences. In Study 5, we show that SESQ historicism moderates the tendency to commit the Fundamental Attribution Error. Finally, in Study 6 we show that SESQ controllability predicts polarization of moral blame judgments: Heightened blaming toward controllable stigmas (assimilation), and attenuated blaming toward uncontrollable stigmas (contrast). Decades of research suggest that explanatory style regarding the self is a powerful predictor of self-functioning. We think it is likely that social explanatory styles—perhaps comprising interactive combinations of the basic dimensions tapped by the SESQ—will be similarly potent predictors of social functioning. We hope the SESQ will be a useful tool for exploring that possibility. PMID:25007152

Estimating and Modelling Bias of the Hierarchical Partitioning Public-Domain Software: Implications in Environmental Management and Conservation

PubMed Central

Olea, Pedro P.; Mateo-Tomás, Patricia; de Frutos, Ángel

2010-01-01

Background Hierarchical partitioning (HP) is an analytical method of multiple regression that identifies the most likely causal factors while alleviating multicollinearity problems. Its use is increasing in ecology and conservation by its usefulness for complementing multiple regression analysis. A public-domain software “hier.part package” has been developed for running HP in R software. Its authors highlight a “minor rounding error” for hierarchies constructed from >9 variables, however potential bias by using this module has not yet been examined. Knowing this bias is pivotal because, for example, the ranking obtained in HP is being used as a criterion for establishing priorities of conservation. Methodology/Principal Findings Using numerical simulations and two real examples, we assessed the robustness of this HP module in relation to the order the variables have in the analysis. Results indicated a considerable effect of the variable order on the amount of independent variance explained by predictors for models with >9 explanatory variables. For these models the nominal ranking of importance of the predictors changed with variable order, i.e. predictors declared important by its contribution in explaining the response variable frequently changed to be either most or less important with other variable orders. The probability of changing position of a variable was best explained by the difference in independent explanatory power between that variable and the previous one in the nominal ranking of importance. The lesser is this difference, the more likely is the change of position. Conclusions/Significance HP should be applied with caution when more than 9 explanatory variables are used to know ranking of covariate importance. The explained variance is not a useful parameter to use in models with more than 9 independent variables. The inconsistency in the results obtained by HP should be considered in future studies as well as in those already published. Some recommendations to improve the analysis with this HP module are given. PMID:20657734

Models, theory structure and mechanisms in biochemistry: The case of allosterism.

PubMed

Alleva, Karina; Díez, José; Federico, Lucia

2017-06-01

From the perspective of the new mechanistic philosophy, it has been argued that explanatory causal mechanisms in some special sciences such as biochemistry and neurobiology cannot be captured by any useful notion of theory, or at least by any standard notion. The goal of this paper is to show that a model-theoretic notion of theory, and in particular the structuralist notion of a theory-net already applied to other unified explanatory theories, adequately suits the MWC allosteric mechanism explanatory set-up. We also argue, contra some mechanistic claims questioning the use of laws in biological explanations, that the theory reconstructed in this way essentially contains non-accidental regularities that qualify as laws, and that taking into account these lawful components, it is possible to explicate the unified character of the theory. Finally, we argue that, contrary to what some mechanists also claim, functional explanations that do not fully specify the mechanistic structure are not defective or incomplete in any relevant sense, and that functional components are perfectly explanatory. The conclusion is that, as some authors have emphasized in other fields (Walmsley 2008), particular elements of traditional approaches do not contradict but rather complement the new mechanist philosophy, and taken together they may offer a more complete understanding of special sciences and the variety of explanations they provide. Copyright © 2017 Elsevier Ltd. All rights reserved.

Themes on circulation in the third world.

PubMed

Chapman, M; Prothero, R M

1983-01-01

"This article focuses upon circulation, or reciprocal flows of people, with specific reference to Third World societies." Aspects considered include attempts to standardize terminology and to formulate typologies of population movement; the development of explanatory models of circulation and modernization, social networks, family welfare, and capitalism; and "the transfer of methods and concepts to societies and populations different from those from which they initially evolved and in which they were first tested." excerpt

Random parameter models for accident prediction on two-lane undivided highways in India.

PubMed

Dinu, R R; Veeraragavan, A

2011-02-01

Generalized linear modeling (GLM), with the assumption of Poisson or negative binomial error structure, has been widely employed in road accident modeling. A number of explanatory variables related to traffic, road geometry, and environment that contribute to accident occurrence have been identified and accident prediction models have been proposed. The accident prediction models reported in literature largely employ the fixed parameter modeling approach, where the magnitude of influence of an explanatory variable is considered to be fixed for any observation in the population. Similar models have been proposed for Indian highways too, which include additional variables representing traffic composition. The mixed traffic on Indian highways comes with a lot of variability within, ranging from difference in vehicle types to variability in driver behavior. This could result in variability in the effect of explanatory variables on accidents across locations. Random parameter models, which can capture some of such variability, are expected to be more appropriate for the Indian situation. The present study is an attempt to employ random parameter modeling for accident prediction on two-lane undivided rural highways in India. Three years of accident history, from nearly 200 km of highway segments, is used to calibrate and validate the models. The results of the analysis suggest that the model coefficients for traffic volume, proportion of cars, motorized two-wheelers and trucks in traffic, and driveway density and horizontal and vertical curvatures are randomly distributed across locations. The paper is concluded with a discussion on modeling results and the limitations of the present study. Copyright © 2010 Elsevier Ltd. All rights reserved.

Climate Change Impacts on Migration in the Vulnerable Countries

NASA Astrophysics Data System (ADS)

An, Nazan; Incealtin, Gamze; Kurnaz, M. Levent; Şengün Ucal, Meltem

2014-05-01

This work focuses on the economic, demographic and environmental drivers of migration related with the sustainable development in underdeveloped and developed countries, which are the most vulnerable to the climate change impacts through the Climate-Development Modeling including climate modeling and panel logit data analysis. We have studied some countries namely Bangladesh, Netherlands, Morocco, Malaysia, Ethiopia and Bolivia. We have analyzed these countries according to their economic, demographic and environmental indicators related with the determinants of migration, and we tried to indicate that their conditions differ according to all these factors concerning with the climate change impacts. This modeling covers some explanatory variables, which have the relationship with the migration, including GDP per capita, population, temperature and precipitation, which indicate the seasonal differences according to the years, the occurrence of natural hazards over the years, coastal location of countries, permanent cropland areas and fish capture which represents the amount of capturing over the years. We analyzed that whether there is a relationship between the migration and these explanatory variables. In order to achieve sustainable development by preventing or decreasing environmental migration due to climate change impacts or related other factors, these countries need to maintain economic, social, political, demographic, and in particular environmental performance. There are some significant risks stemming from climate change, which is not under control. When the economic and environmental conditions are considered, we have to regard climate change to be the more destructive force for those who are less defensible against all of these risks and impacts of uncontrolled climate change. This work was supported by the BU Research Fund under the project number 6990. One of the authors (MLK) was partially supported by Mercator-IPC Fellowship Program.

Detection of outliers in the response and explanatory variables of the simple circular regression model

NASA Astrophysics Data System (ADS)

Mahmood, Ehab A.; Rana, Sohel; Hussin, Abdul Ghapor; Midi, Habshah

2016-06-01

The circular regression model may contain one or more data points which appear to be peculiar or inconsistent with the main part of the model. This may be occur due to recording errors, sudden short events, sampling under abnormal conditions etc. The existence of these data points "outliers" in the data set cause lot of problems in the research results and the conclusions. Therefore, we should identify them before applying statistical analysis. In this article, we aim to propose a statistic to identify outliers in the both of the response and explanatory variables of the simple circular regression model. Our proposed statistic is robust circular distance RCDxy and it is justified by the three robust measurements such as proportion of detection outliers, masking and swamping rates.

Transportation economics and energy

NASA Astrophysics Data System (ADS)

Soltani Sobh, Ali

The overall objective of this research is to study the impacts of technology improvement including fuel efficiency increment, extending the use of natural gas vehicle and electric vehicles on key parameters of transportation. In the first chapter, a simple economic analysis is used in order to demonstrate the adoption rate of natural gas vehicles as an alternative fuel vehicle. The effect of different factors on adoption rate of commuters is calculated in sensitivity analysis. In second chapter the VMT is modeled and forecasted under influence of CNG vehicles in different scenarios. The VMT modeling is based on the time series data for Washington State. In order to investigate the effect of population growth on VMT, the per capita model is also developed. In third chapter the effect of fuel efficiency improvement on fuel tax revenue and greenhouse emission is examined. The model is developed based on time series data of Washington State. The rebound effect resulted from fuel efficiency improvement is estimated and is considered in fuel consumption forecasting. The reduction in fuel tax revenue and greenhouse gas (GHG) emissions as two outcomes of lower fuel consumption are computed. In addition, the proper fuel tax rate to restitute the revenue is suggested. In the fourth chapter effective factors on electric vehicles (EV) adoption is discussed. The constructed model is aggregated binomial logit share model that estimates the modal split between EV and conventional vehicles for different states over time. Various factors are incorporated in the utility function as explanatory variables in order to quantify their effect on EV adoption choices. The explanatory variables include income, VMT, electricity price, gasoline price, urban area and number of EV stations.

Logistic regression modeling to assess groundwater vulnerability to contamination in Hawaii, USA.

PubMed

Mair, Alan; El-Kadi, Aly I

2013-10-01

Capture zone analysis combined with a subjective susceptibility index is currently used in Hawaii to assess vulnerability to contamination of drinking water sources derived from groundwater. In this study, we developed an alternative objective approach that combines well capture zones with multiple-variable logistic regression (LR) modeling and applied it to the highly-utilized Pearl Harbor and Honolulu aquifers on the island of Oahu, Hawaii. Input for the LR models utilized explanatory variables based on hydrogeology, land use, and well geometry/location. A suite of 11 target contaminants detected in the region, including elevated nitrate (>1 mg/L), four chlorinated solvents, four agricultural fumigants, and two pesticides, was used to develop the models. We then tested the ability of the new approach to accurately separate groups of wells with low and high vulnerability, and the suitability of nitrate as an indicator of other types of contamination. Our results produced contaminant-specific LR models that accurately identified groups of wells with the lowest/highest reported detections and the lowest/highest nitrate concentrations. Current and former agricultural land uses were identified as significant explanatory variables for eight of the 11 target contaminants, while elevated nitrate was a significant variable for five contaminants. The utility of the combined approach is contingent on the availability of hydrologic and chemical monitoring data for calibrating groundwater and LR models. Application of the approach using a reference site with sufficient data could help identify key variables in areas with similar hydrogeology and land use but limited data. In addition, elevated nitrate may also be a suitable indicator of groundwater contamination in areas with limited data. The objective LR modeling approach developed in this study is flexible enough to address a wide range of contaminants and represents a suitable addition to the current subjective approach. © 2013 Elsevier B.V. All rights reserved.

The self, attributional processes and abnormal beliefs: towards a model of persecutory delusions.

PubMed

Bentall, R P; Kinderman, P; Kaney, S

1994-03-01

In this paper we review a series of recent investigations into cognitive abnormalities associated with persecutory delusions. Studies indicate that persecutory delusions are associated with abnormal attention to threat-related stimuli, an explanatory bias towards attributing negative outcomes to external causes and biases in information processing relating to the self-concept. We propose an integrative model to account for these findings in which it is hypothesized that, in deluded patients, activation of self/ideal discrepancies by threat-related information triggers defensive explanatory biases, which have the function of reducing the self/ideal discrepancies but result in persecutory ideation. We conclude by discussing the implications of this model for the cognitive-behavioural treatment of paranoid delusions.

Cognitive ability influences on written expression: Evidence for developmental and sex-based differences in school-age children.

PubMed

Hajovsky, Daniel B; Villeneuve, Ethan F; Reynolds, Matthew R; Niileksela, Christopher R; Mason, Benjamin A; Shudak, Nicholas J

2018-04-01

Some studies have demonstrated that the Cattell-Horn-Carroll (CHC) cognitive abilities influence writing; however, little research has investigated whether CHC cognitive abilities influence writing the same way for males and females across grades. We used multiple group structural equation models to investigate whether CHC cognitive ability influences on written expression differed between grades or sex using the Kaufman Assessment Battery for Children, Second Edition and the Kaufman Tests of Educational Achievement, Second Edition co-normed standardization sample data (N=2117). After testing for consistent measurement of cognitive abilities across grades and sex, we tested whether the cognitive ability influences on written expression were moderated by grade level or sex. An important developmental shift was observed equally across sex groups: Learning Efficiency (Gl) influences decreased whereas Crystallized Ability (Gc) influences increased after fourth grade. Further, Short-Term Memory (Gsm) and Retrieval Fluency (Gr) influences on written expression depended on sex at grades 1-4, with larger Gr influences for females and larger Gsm influences for males. We internally replicated our main findings using two different cognitive explanatory models, adding further support for the developmental and sex-based differential cognitive ability influences on writing. Explanatory cognitive models of writing need to incorporate development, and possibly, sex to provide an expanded understanding of writing development and guard against potential generalizability issues characteristic of special population (i.e., male-female) studies. Copyright © 2017 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Thinking about online sources: Exploring students' epistemic cognition in internet-based chemistry learning

NASA Astrophysics Data System (ADS)

Dai, Ting

This dissertation investigated the relation between epistemic cognition---epistemic aims and source beliefs---and learning outcome in an Internet--based research context. Based on a framework of epistemic cognition (Chinn, Buckland, & Samarapungavan, 2011), a context--specific epistemic aims and source beliefs questionnaire (CEASBQ) was developed and administered to 354 students from college--level introductory chemistry courses. A series of multitrait--multimethod model comparisons provided evidence for construct convergent and discriminant validity for three epistemic aims--- true beliefs, justified beliefs, explanatory connection, which were all distinguished from, yet correlated with, mastery goals. Students' epistemic aims were specific to the chemistry topics in research. Multidimensional scaling results indicated that students' source evaluation was based on two dimensions--- professional expertise and first--hand knowledge, suggesting a multidimensional structure of source beliefs. Most importantly, online learning outcome was found to be significantly associated with two epistemic aims---justified beliefs and explanatory connection: The more students sought justifications in the online research, the lower they tended to score on the learning outcome measure, whereas the more students sought explanatory connections between information, the higher they scored on the outcome measure. There was a significant but small positive association between source beliefs and learning outcome. The influences of epistemic aims and source beliefs on learning outcome were found to be above and beyond the effects of a number of covariates, including prior knowledge and perceived ability with online sources.

Testing an explanatory model of nurses' intention to report adverse drug reactions in hospital settings.

PubMed

Angelis, Alessia De; Pancani, Luca; Steca, Patrizia; Colaceci, Sofia; Giusti, Angela; Tibaldi, Laura; Alvaro, Rosaria; Ausili, Davide; Vellone, Ercole

2017-05-01

To test an explanatory model of nurses' intention to report adverse drug reactions in hospital settings, based on the theory of planned behaviour. Under-reporting of adverse drug reactions is an important problem among nurses. A cross-sectional design was used. Data were collected with the adverse drug reporting nurses' questionnaire. Confirmatory factor analysis was performed to test the factor validity of the adverse drug reporting nurses' questionnaire, and structural equation modelling was used to test the explanatory model. The convenience sample comprised 500 Italian hospital nurses (mean age = 43.52). Confirmatory factor analysis supported the factor validity of the adverse drug reporting nurses' questionnaire. The structural equation modelling showed a good fit with the data. Nurses' intention to report adverse drug reactions was significantly predicted by attitudes, subjective norms and perceived behavioural control (R² = 0.16). The theory of planned behaviour effectively explained the mechanisms behind nurses' intention to report adverse drug reactions, showing how several factors come into play. In a scenario of organisational empowerment towards adverse drug reaction reporting, the major predictors of the intention to report are support for the decision to report adverse drug reactions from other health care practitioners, perceptions about the value of adverse drug reaction reporting and nurses' favourable self-assessment of their adverse drug reaction reporting skills. © 2017 John Wiley & Sons Ltd.

Human age estimation combining third molar and skeletal development.

PubMed

Thevissen, P W; Kaur, J; Willems, G

2012-03-01

The wide prediction intervals obtained with age estimation methods based on third molar development could be reduced by combining these dental observations with age-related skeletal information. Therefore, on cephalometric radiographs, the most accurate age-estimating skeletal variable and related registration method were searched and added to a regression model, with age as response and third molar stages as explanatory variable. In a pilot set up on a dataset of 496 (283 M; 213 F) cephalometric radiographs, the techniques of Baccetti et al. (2005) (BA), Seedat et al. (2005) (SE), Caldas et al. (2007) and Rai et al. (2008) (RA) were verified. In the main study, data from 460 (208 F, 224 M) individuals in an age range between 3 and 26 years, for which at the same day an orthopantogram and a cephalogram were taken, were collected. On the orthopantomograms, the left third molar development was registered using the scoring system described by Gleiser and Hunt (1955) and modified by Köhler (1994) (GH). On the cephalograms, cervical vertebrae development was registered according to the BA and SE techniques. A regression model, with age as response and the GH scores as explanatory variable, was fitted to the data. Next, information of BA, SE and BA + SE was, respectively, added to this model. From all obtained models, the determination coefficients and the root mean squared errors were calculated. Inclusion of information from cephalograms based on the BA, as well as the SE, technique improved the amount of explained variance in age acquired from panoramic radiographs using the GH technique with 48%. Inclusion of cephalometric BA + SE information marginally improved the previous result (+1%). The RMSE decreased with 1.93, 1.85 and 2.03 years by adding, respectively, BA, SE and BA + SE information to the GH model. The SE technique allows clinically the fastest and easiest registration of the degree of development of the cervical vertebrae. Therefore, the choice of technique to classify cervical vertebrae development in addition to third molar development is preferably the SE technique.

Empirical spatial econometric modelling of small scale neighbourhood

NASA Astrophysics Data System (ADS)

Gerkman, Linda

2012-07-01

The aim of the paper is to model small scale neighbourhood in a house price model by implementing the newest methodology in spatial econometrics. A common problem when modelling house prices is that in practice it is seldom possible to obtain all the desired variables. Especially variables capturing the small scale neighbourhood conditions are hard to find. If there are important explanatory variables missing from the model, the omitted variables are spatially autocorrelated and they are correlated with the explanatory variables included in the model, it can be shown that a spatial Durbin model is motivated. In the empirical application on new house price data from Helsinki in Finland, we find the motivation for a spatial Durbin model, we estimate the model and interpret the estimates for the summary measures of impacts. By the analysis we show that the model structure makes it possible to model and find small scale neighbourhood effects, when we know that they exist, but we are lacking proper variables to measure them.

A meta-analysis and statistical modelling of nitrates in groundwater at the African scale

NASA Astrophysics Data System (ADS)

Ouedraogo, Issoufou; Vanclooster, Marnik

2016-06-01

Contamination of groundwater with nitrate poses a major health risk to millions of people around Africa. Assessing the space-time distribution of this contamination, as well as understanding the factors that explain this contamination, is important for managing sustainable drinking water at the regional scale. This study aims to assess the variables that contribute to nitrate pollution in groundwater at the African scale by statistical modelling. We compiled a literature database of nitrate concentration in groundwater (around 250 studies) and combined it with digital maps of physical attributes such as soil, geology, climate, hydrogeology, and anthropogenic data for statistical model development. The maximum, medium, and minimum observed nitrate concentrations were analysed. In total, 13 explanatory variables were screened to explain observed nitrate pollution in groundwater. For the mean nitrate concentration, four variables are retained in the statistical explanatory model: (1) depth to groundwater (shallow groundwater, typically < 50 m); (2) recharge rate; (3) aquifer type; and (4) population density. The first three variables represent intrinsic vulnerability of groundwater systems to pollution, while the latter variable is a proxy for anthropogenic pollution pressure. The model explains 65 % of the variation of mean nitrate contamination in groundwater at the African scale. Using the same proxy information, we could develop a statistical model for the maximum nitrate concentrations that explains 42 % of the nitrate variation. For the maximum concentrations, other environmental attributes such as soil type, slope, rainfall, climate class, and region type improve the prediction of maximum nitrate concentrations at the African scale. As to minimal nitrate concentrations, in the absence of normal distribution assumptions of the data set, we do not develop a statistical model for these data. The data-based statistical model presented here represents an important step towards developing tools that will allow us to accurately predict nitrate distribution at the African scale and thus may support groundwater monitoring and water management that aims to protect groundwater systems. Yet they should be further refined and validated when more detailed and harmonized data become available and/or combined with more conceptual descriptions of the fate of nutrients in the hydrosystem.

Explanatory model of emotional-cognitive variables in school mathematics performance: a longitudinal study in primary school

PubMed Central

Cerda, Gamal; Pérez, Carlos; Navarro, José I.; Aguilar, Manuel; Casas, José A.; Aragón, Estíbaliz

2015-01-01

This study tested a structural model of cognitive-emotional explanatory variables to explain performance in mathematics. The predictor variables assessed were related to students’ level of development of early mathematical competencies (EMCs), specifically, relational and numerical competencies, predisposition toward mathematics, and the level of logical intelligence in a population of primary school Chilean students (n = 634). This longitudinal study also included the academic performance of the students during a period of 4 years as a variable. The sampled students were initially assessed by means of an Early Numeracy Test, and, subsequently, they were administered a Likert-type scale to measure their predisposition toward mathematics (EPMAT) and a basic test of logical intelligence. The results of these tests were used to analyse the interaction of all the aforementioned variables by means of a structural equations model. This combined interaction model was able to predict 64.3% of the variability of observed performance. Preschool students’ performance in EMCs was a strong predictor for achievement in mathematics for students between 8 and 11 years of age. Therefore, this paper highlights the importance of EMCs and the modulating role of predisposition toward mathematics. Also, this paper discusses the educational role of these findings, as well as possible ways to improve negative predispositions toward mathematical tasks in the school domain. PMID:26441739

Correction of the significance level when attempting multiple transformations of an explanatory variable in generalized linear models

PubMed Central

2013-01-01

Background In statistical modeling, finding the most favorable coding for an exploratory quantitative variable involves many tests. This process involves multiple testing problems and requires the correction of the significance level. Methods For each coding, a test on the nullity of the coefficient associated with the new coded variable is computed. The selected coding corresponds to that associated with the largest statistical test (or equivalently the smallest pvalue). In the context of the Generalized Linear Model, Liquet and Commenges (Stat Probability Lett,71:33–38,2005) proposed an asymptotic correction of the significance level. This procedure, based on the score test, has been developed for dichotomous and Box-Cox transformations. In this paper, we suggest the use of resampling methods to estimate the significance level for categorical transformations with more than two levels and, by definition those that involve more than one parameter in the model. The categorical transformation is a more flexible way to explore the unknown shape of the effect between an explanatory and a dependent variable. Results The simulations we ran in this study showed good performances of the proposed methods. These methods were illustrated using the data from a study of the relationship between cholesterol and dementia. Conclusion The algorithms were implemented using R, and the associated CPMCGLM R package is available on the CRAN. PMID:23758852

Narrative insight in psychosis: The relationship with spiritual and religious explanatory frameworks.

PubMed

Marriott, Michael R; Thompson, Andrew R; Cockshutt, Graham; Rowse, Georgina

2018-03-25

When considering psychosis, the concept of narrative insight has been offered as an alternative to clinical insight in determining individuals' responses to their difficulties, as it allows for a more holistic and person-centred framework to be embraced within professional practice. This study aims to explore the validity of the narrative insight construct within a group of people who have experienced psychosis. Inductive qualitative methods were used to explore how eight participants utilized spiritual or religious explanatory frameworks for their experiences of psychosis and to consider these in relation to the construct of narrative insight. Semi-structured interviews were undertaken with individuals who identified themselves as interested in spiritual or religious ideas and whose self-reported experiences which were identified as akin to psychosis by experienced academic clinicians. Transcriptions from these interviews were subject to interpretative phenomenological analysis within a broader research question; a selection of themes and data from the resultant phenomenological structure are explored here for their relevance to narrative insight. Participants discussed spiritual and biological explanations for their experiences and were able to hold alternative potential explanations alongside each other. They were reflective regarding the origins of their explanations and would describe a process of testing and proof in relation to them. These findings suggest that the narrative insight construct has the potential to be a valid approach to understanding experiences of psychosis, and challenge the dominance of the clinical insight construct within clinical practice. Clinicians should value the explanatory framework for experiences which are provided by individuals experiencing psychosis, and encourage them to develop a framework which is coherent to their own world view rather than predominantly pursuing a biomedical explanation. Assessments of psychosis should be adapted to include an understanding of the cohesiveness of the individual's explanatory framework and personal value to them, with a reduced focus on their acceptance of biomedical models of 'illness'. Care and care research for individuals experiencing psychosis should consider the value of narrative insight within future developments. © 2018 The Authors. Psychology and Psychotherapy: Theory, Research and Practice published by John Wiley & Sons Ltd on behalf of British Psychological Society.

An Explanatory Model of Dating Violence Risk Factors in Spanish Adolescents.

PubMed

Aizpitarte, Alazne; Alonso-Arbiol, Itziar; Van de Vijver, Fons J R

2017-12-01

Dating violence is a serious public health issue that needs further understanding in terms of risk factors that may be involved in it. The main goal of this study was to test a mediational model of dating violence risk factors. The sample was composed of 477 secondary and college students from Spain (59% females). A dynamic developmental explanatory model considering aggressiveness, insecure attachment, interparental conflict, and peer dating violence was tested using a multigroup structural equation model. Aggressiveness partially mediated the relation between anxious attachment and dating violence and fully mediated the association between interparental conflict resolution and dating violence. Furthermore, perceived peer dating violence was a direct predictor of dating violence. Implications for prevention and intervention plans are discussed. © 2017 The Authors. Journal of Research on Adolescence © 2017 Society for Research on Adolescence.

A comparison of data-driven groundwater vulnerability assessment methods

USGS Publications Warehouse

Sorichetta, Alessandro; Ballabio, Cristiano; Masetti, Marco; Robinson, Gilpin R.; Sterlacchini, Simone

2013-01-01

Increasing availability of geo-environmental data has promoted the use of statistical methods to assess groundwater vulnerability. Nitrate is a widespread anthropogenic contaminant in groundwater and its occurrence can be used to identify aquifer settings vulnerable to contamination. In this study, multivariate Weights of Evidence (WofE) and Logistic Regression (LR) methods, where the response variable is binary, were used to evaluate the role and importance of a number of explanatory variables associated with nitrate sources and occurrence in groundwater in the Milan District (central part of the Po Plain, Italy). The results of these models have been used to map the spatial variation of groundwater vulnerability to nitrate in the region, and we compare the similarities and differences of their spatial patterns and associated explanatory variables. We modify the standard WofE method used in previous groundwater vulnerability studies to a form analogous to that used in LR; this provides a framework to compare the results of both models and reduces the effect of sampling bias on the results of the standard WofE model. In addition, a nonlinear Generalized Additive Model has been used to extend the LR analysis. Both approaches improved discrimination of the standard WofE and LR models, as measured by the c-statistic. Groundwater vulnerability probability outputs, based on rank-order classification of the respective model results, were similar in spatial patterns and identified similar strong explanatory variables associated with nitrate source (population density as a proxy for sewage systems and septic sources) and nitrate occurrence (groundwater depth).

High school students' understanding and problem solving in population genetics

NASA Astrophysics Data System (ADS)

Soderberg, Patti D.

This study is an investigation of student understanding of population genetics and how students developed, used and revised conceptual models to solve problems. The students in this study participated in three rounds of problem solving. The first round involved the use of a population genetics model to predict the number of carriers in a population. The second round required them to revise their model of simple dominance population genetics to make inferences about populations containing three phenotype variations. The third round of problem solving required the students to revise their model of population genetics to explain anomalous data where the proportions of males and females with a trait varied significantly. As the students solved problems, they were involved in basic scientific processes as they observed population phenomena, constructed explanatory models to explain the data they observed, and attempted to persuade their peers as to the adequacy of their models. In this study, the students produced new knowledge about the genetics of a trait in a population through the revision and use of explanatory population genetics models using reasoning that was similar to what scientists do. The students learned, used and revised a model of Hardy-Weinberg equilibrium to generate and test hypotheses about the genetics of phenotypes given only population data. Students were also interviewed prior to and following instruction. This study suggests that a commonly held intuitive belief about the predominance of a dominant variation in populations is resistant to change, despite instruction and interferes with a student's ability to understand Hardy-Weinberg equilibrium and microevolution.

Coping with Stress and Types of Burnout: Explanatory Power of Different Coping Strategies

PubMed Central

Montero-Marin, Jesus; Prado-Abril, Javier; Piva Demarzo, Marcelo Marcos; Gascon, Santiago; García-Campayo, Javier

2014-01-01

Background Burnout occurs when professionals use ineffective coping strategies to try to protect themselves from work-related stress. The dimensions of ‘overload’, ‘lack of development’ and ‘neglect’, belonging to the ‘frenetic’, ‘under-challenged’ and ‘worn-out’ subtypes, respectively, comprise a brief typological definition of burnout. The aim of the present study was to estimate the explanatory power of the different coping strategies on the development of burnout subtypes. Methods This was a cross-sectional survey with a random sample of university employees, stratified by occupation (n = 429). Multivariate linear regression models were constructed between the ‘Burnout Clinical Subtypes Questionnaire’, with its three dimensions –overload, lack of development and neglect– as dependent variables, and the ‘Coping Orientation for Problem Experiences’, with its fifteen dimensions, as independent variables. Adjusted multiple determination coefficients and beta coefficients were calculated to evaluate and compare the explanatory capacity of the different coping strategies. Results The ‘Coping Orientation for Problem Experiences’ subscales together explained 15% of the ‘overload’ (p<0.001), 9% of the ‘lack of development’ (p<0.001), and 21% of the ‘neglect’ (p<0.001). ‘Overload’ was mainly explained by ‘venting of emotions’ (Beta = 0.34; p<0.001); ‘lack of development’ by ‘cognitive avoidance’ (Beta = 0.21; p<0.001); and ‘neglect’ by ‘behavioural disengagement’ (Beta = 0.40; p<0.001). Other interesting associations were observed. Conclusions These findings further our understanding of the way in which the effectiveness of interventions for burnout may be improved, by influencing new treatments and preventive programmes using features of the strategies for handling stress in the workplace. PMID:24551223

Ordinal regression models to describe tourist satisfaction with Sintra's world heritage

NASA Astrophysics Data System (ADS)

Mouriño, Helena

2013-10-01

In Tourism Research, ordinal regression models are becoming a very powerful tool in modelling the relationship between an ordinal response variable and a set of explanatory variables. In August and September 2010, we conducted a pioneering Tourist Survey in Sintra, Portugal. The data were obtained by face-to-face interviews at the entrances of the Palaces and Parks of Sintra. The work developed in this paper focus on two main points: tourists' perception of the entrance fees; overall level of satisfaction with this heritage site. For attaining these goals, ordinal regression models were developed. We concluded that tourist's nationality was the only significant variable to describe the perception of the admission fees. Also, Sintra's image among tourists depends not only on their nationality, but also on previous knowledge about Sintra's World Heritage status.

Watershed regressions for pesticides (warp) models for predicting atrazine concentrations in Corn Belt streams

USGS Publications Warehouse

Stone, Wesley W.; Gilliom, Robert J.

2012-01-01

Watershed Regressions for Pesticides (WARP) models, previously developed for atrazine at the national scale, are improved for application to the United States (U.S.) Corn Belt region by developing region-specific models that include watershed characteristics that are influential in predicting atrazine concentration statistics within the Corn Belt. WARP models for the Corn Belt (WARP-CB) were developed for annual maximum moving-average (14-, 21-, 30-, 60-, and 90-day durations) and annual 95th-percentile atrazine concentrations in streams of the Corn Belt region. The WARP-CB models accounted for 53 to 62% of the variability in the various concentration statistics among the model-development sites. Model predictions were within a factor of 5 of the observed concentration statistic for over 90% of the model-development sites. The WARP-CB residuals and uncertainty are lower than those of the National WARP model for the same sites. Although atrazine-use intensity is the most important explanatory variable in the National WARP models, it is not a significant variable in the WARP-CB models. The WARP-CB models provide improved predictions for Corn Belt streams draining watersheds with atrazine-use intensities of 17 kg/km2 of watershed area or greater.

Estimation of Nitrogen Vertical Distribution by Bi-Directional Canopy Reflectance in Winter Wheat

PubMed Central

Huang, Wenjiang; Yang, Qinying; Pu, Ruiliang; Yang, Shaoyuan

2014-01-01

Timely measurement of vertical foliage nitrogen distribution is critical for increasing crop yield and reducing environmental impact. In this study, a novel method with partial least square regression (PLSR) and vegetation indices was developed to determine optimal models for extracting vertical foliage nitrogen distribution of winter wheat by using bi-directional reflectance distribution function (BRDF) data. The BRDF data were collected from ground-based hyperspectral reflectance measurements recorded at the Xiaotangshan Precision Agriculture Experimental Base in 2003, 2004 and 2007. The view zenith angles (1) at nadir, 40° and 50°; (2) at nadir, 30° and 40°; and (3) at nadir, 20° and 30° were selected as optical view angles to estimate foliage nitrogen density (FND) at an upper, middle and bottom layer, respectively. For each layer, three optimal PLSR analysis models with FND as a dependent variable and two vegetation indices (nitrogen reflectance index (NRI), normalized pigment chlorophyll index (NPCI) or a combination of NRI and NPCI) at corresponding angles as explanatory variables were established. The experimental results from an independent model verification demonstrated that the PLSR analysis models with the combination of NRI and NPCI as the explanatory variables were the most accurate in estimating FND for each layer. The coefficients of determination (R2) of this model between upper layer-, middle layer- and bottom layer-derived and laboratory-measured foliage nitrogen density were 0.7335, 0.7336, 0.6746, respectively. PMID:25353983

Estimation of nitrogen vertical distribution by bi-directional canopy reflectance in winter wheat.

PubMed

Huang, Wenjiang; Yang, Qinying; Pu, Ruiliang; Yang, Shaoyuan

2014-10-28

Timely measurement of vertical foliage nitrogen distribution is critical for increasing crop yield and reducing environmental impact. In this study, a novel method with partial least square regression (PLSR) and vegetation indices was developed to determine optimal models for extracting vertical foliage nitrogen distribution of winter wheat by using bi-directional reflectance distribution function (BRDF) data. The BRDF data were collected from ground-based hyperspectral reflectance measurements recorded at the Xiaotangshan Precision Agriculture Experimental Base in 2003, 2004 and 2007. The view zenith angles (1) at nadir, 40° and 50°; (2) at nadir, 30° and 40°; and (3) at nadir, 20° and 30° were selected as optical view angles to estimate foliage nitrogen density (FND) at an upper, middle and bottom layer, respectively. For each layer, three optimal PLSR analysis models with FND as a dependent variable and two vegetation indices (nitrogen reflectance index (NRI), normalized pigment chlorophyll index (NPCI) or a combination of NRI and NPCI) at corresponding angles as explanatory variables were established. The experimental results from an independent model verification demonstrated that the PLSR analysis models with the combination of NRI and NPCI as the explanatory variables were the most accurate in estimating FND for each layer. The coefficients of determination (R2) of this model between upper layer-, middle layer- and bottom layer-derived and laboratory-measured foliage nitrogen density were 0.7335, 0.7336, 0.6746, respectively.

On Spatially Explicit Models of Epidemic and Endemic Cholera: The Haiti and Lake Kivu Case Studies.

NASA Astrophysics Data System (ADS)

Rinaldo, A.; Bertuzzo, E.; Mari, L.; Finger, F.; Casagrandi, R.; Gatto, M.; Rodriguez-Iturbe, I.

2014-12-01

The first part of the Lecture deals with the predictive ability of mechanistic models for the Haitian cholera epidemic. Predictive models of epidemic cholera need to resolve at suitable aggregation levels spatial data pertaining to local communities, epidemiological records, hydrologic drivers, waterways, patterns of human mobility and proxies of exposure rates. A formal model comparison framework provides a quantitative assessment of the explanatory and predictive abilities of various model settings with different spatial aggregation levels. Intensive computations and objective model comparisons show that parsimonious spatially explicit models accounting for spatial connections have superior explanatory power than spatially disconnected ones for short-to intermediate calibration windows. In general, spatially connected models show better predictive ability than disconnected ones. We suggest limits and validity of the various approaches and discuss the pathway towards the development of case-specific predictive tools in the context of emergency management. The second part deals with approaches suitable to describe patterns of endemic cholera. Cholera outbreaks have been reported in the Democratic Republic of the Congo since the 1970s. Here we employ a spatially explicit, inhomogeneous Markov chain model to describe cholera incidence in eight health zones on the shore of lake Kivu. Remotely sensed datasets of chlorophyll a concentration in the lake, precipitation and indices of global climate anomalies are used as environmental drivers in addition to baseline seasonality. The effect of human mobility is also modelled mechanistically. We test several models on a multi-year dataset of reported cholera cases. Fourteen models, accounting for different environmental drivers, are selected in calibration. Among these, the one accounting for seasonality, El Nino Southern Oscillation, precipitation and human mobility outperforms the others in cross-validation.

Multiple Use One-Sided Hypotheses Testing in Univariate Linear Calibration

NASA Technical Reports Server (NTRS)

Krishnamoorthy, K.; Kulkarni, Pandurang M.; Mathew, Thomas

1996-01-01

Consider a normally distributed response variable, related to an explanatory variable through the simple linear regression model. Data obtained on the response variable, corresponding to known values of the explanatory variable (i.e., calibration data), are to be used for testing hypotheses concerning unknown values of the explanatory variable. We consider the problem of testing an unlimited sequence of one sided hypotheses concerning the explanatory variable, using the corresponding sequence of values of the response variable and the same set of calibration data. This is the situation of multiple use of the calibration data. The tests derived in this context are characterized by two types of uncertainties: one uncertainty associated with the sequence of values of the response variable, and a second uncertainty associated with the calibration data. We derive tests based on a condition that incorporates both of these uncertainties. The solution has practical applications in the decision limit problem. We illustrate our results using an example dealing with the estimation of blood alcohol concentration based on breath estimates of the alcohol concentration. In the example, the problem is to test if the unknown blood alcohol concentration of an individual exceeds a threshold that is safe for driving.

Independent contrasts and PGLS regression estimators are equivalent.

PubMed

Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary

2012-05-01

We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.

Using Qualitative Methods to Explore Lay Explanatory Models, Health-Seeking Behaviours and Self-Care Practices of Podoconiosis Patients in North-West Ethiopia.

PubMed

Banks, Harrison S; Tsegay, Girmay; Wubie, Moges; Tamiru, Abreham; Davey, Gail; Cooper, Max

2016-08-01

Podoconiosis (endemic non-filarial elephantiasis) is a chronic, non-infectious disease resulting from exposure of bare feet to red-clay soil in tropical highlands. This study examined lay beliefs about three under-researched aspects of podoconiosis patients' care: explanatory models, health-seeking behaviours and self-care. In-depth interviews and focus group discussions were undertaken with 34 participants (19 male, 15 female) between April-May 2015 at podoconiosis treatment centres across East and West Gojjam regions in north-west Ethiopia. Explanatory models for podoconiosis included contamination from blood, magic, soil or affected individuals. Belief in heredity or divine punishment often delayed clinic attendance. All participants had tried holy water treatment and some, holy soil. Herbal treatments were considered ineffectual, costly and appeared to promote fluid escape. Motivators for clinic attendance were failure of traditional treatments and severe or disabling symptoms. Patients did not report self-treatment with antibiotics. Self-care was hindered by water being unavailable or expensive and patient fatigue. A pluralistic approach to podoconiosis self-treatment was discovered. Holy water is widely valued, though some patients prefer holy soil. Priests and traditional healers could help promote self-care and "signpost" patients to clinics. Change in behaviour and improving water access is key to self-care.

The psychological factor 'self-blame' predicts overuse injury among top-level Swedish track and field athletes: a 12-month cohort study.

PubMed

Timpka, Toomas; Jacobsson, Jenny; Dahlström, Örjan; Kowalski, Jan; Bargoria, Victor; Ekberg, Joakim; Nilsson, Sverker; Renström, Per

2015-11-01

Athletes' psychological characteristics are important for understanding sports injury mechanisms. We examined the relevance of psychological factors in an integrated model of overuse injury risk in athletics/track and field. Swedish track and field athletes (n=278) entering a 12-month injury surveillance in March 2009 were also invited to complete a psychological survey. Simple Cox proportional hazards models were compiled for single explanatory variables. We also tested multiple models for 3 explanatory variable groupings: an epidemiological model without psychological variables, a psychological model excluding epidemiological variables and an integrated (combined) model. The integrated multiple model included the maladaptive coping behaviour self-blame (p=0.007; HR 1.32; 95% CI 1.08 to 1.61), and an interaction between athlete category and injury history (p<0.001). Youth female (p=0.034; HR 0.51; 95% CI 0.27 to 0.95) and youth male (p=0.047; HR 0.49; 95% CI 0.24 to 0.99) athletes with no severe injury the previous year were at half the risk of sustaining a new injury compared with the reference group. A training load index entered the epidemiological multiple model, but not the integrated model. The coping behaviour self-blame replaced training load in an integrated explanatory model of overuse injury risk in athletes. What seemed to be more strongly related to the likelihood of overuse injury was not the athletics load per se, but, rather, the load applied in situations when the athlete's body was in need of rest. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Black-white preterm birth disparity: a marker of inequality

EPA Science Inventory

Purpose. The racial disparity in preterrn birth (PTB) is a persistent feature of perinatal epidemiology, inconsistently modeled in the literature. Rather than include race as an explanatory variable, or employ race-stratified models, we sought to directly model the PTB disparity ...

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…

Dietary restrictions in healing among speakers of Iquito, an endangered language of the Peruvian Amazon

PubMed Central

2011-01-01

Background Ethnobotanical research was carried out with speakers of Iquito, a critically endangered Amazonian language of the Zaparoan family. The study focused on the concept of "dieting" (siyan++ni in Iquito), a practice involving prohibitions considered necessary to the healing process. These restrictions include: 1) foods and activities that can exacerbate illness, 2) environmental influences that conflict with some methods of healing (e.g. steam baths or enemas) and 3) foods and activities forbidden by the spirits of certain powerful medicinal plants. The study tested the following hypotheses: H1 - Each restriction will correlate with specific elements in illness explanatory models and H2 - Illnesses whose explanatory models have personalistic elements will show a greater number and variety of restrictions than those based on naturalistic reasoning. Methods The work was carried out in 2009 and 2010 in the Alto Nanay region of Peru. In structured interviews, informants gave explanatory models for illness categories, including etiologies, pathophysiologies, treatments and dietary restrictions necessary for 49 illnesses. Seventeen botanical vouchers for species said to have powerful spirits that require diets were also collected. Results All restrictions found correspond to some aspect of illness explanatory models. Thirty-five percent match up with specific illness etiologies, 53% correspond to particular pathophysiologies, 18% correspond with overall seriousness of the illness and 18% are only found with particular forms of treatment. Diets based on personalistic reasoning have a significantly higher average number of restrictions than those based on naturalistic reasoning. Conclusions Dieting plays a central role in healing among Iquito speakers. Specific prohibitions can be explained in terms of specific aspects of illness etiologies, pathophysiologies and treatments. Although the Amazonian literature contains few studies focusing on dietary proscriptions over a wide range of illnesses, some specific restrictions reported here do correspond with trends seen in other Amazonian societies, particularly those related to sympathetic reasoning and for magical and spiritual uses of plants. PMID:21745400

Dietary restrictions in healing among speakers of Iquito, an endangered language of the Peruvian Amazon.

PubMed

Jernigan, Kevin A

2011-07-11

Ethno botanical research was carried out with speakers of Iquitos, a critically endangered Amazonian language of the Zaparoan family. The study focused on the concept of "dieting" (siyan++ni in Iquitos), a practice involving prohibitions considered necessary to the healing process. These restrictions include: 1) foods and activities that can exacerbate illness, 2) environmental influences that conflict with some methods of healing (e.g. steam baths or enemas) and 3) foods and activities forbidden by the spirits of certain powerful medicinal plants. The study tested the following hypotheses: H1--Each restriction will correlate with specific elements in illness explanatory models and H2--Illnesses whose explanatory models have personality elements will show a greater number and variety of restrictions than those based on naturalistic reasoning. The work was carried out in 2009 and 2010 in the Alto Nanay region of Peru. In structured interviews, informants gave explanatory models for illness categories, including etiologies, pathophysiologies, treatments and dietary restrictions necessary for 49 illnesses. Seventeen botanical vouchers for species said to have powerful spirits that require diets were also collected. All restrictions found correspond to some aspect of illness explanatory models. Thirty-five percent match up with specific illness etiologies, 53% correspond to particular pathophysiologies, 18% correspond with overall seriousness of the illness and 18% are only found with particular forms of treatment. Diets based on personalistic reasoning have a significantly higher average number of restrictions than those based on naturalistic reasoning. Dieting plays a central role in healing among Iquitos speakers. Specific prohibitions can be explained in terms of specific aspects of illness etiologies, pathophysiologies and treatments. Although the Amazonian literature contains few studies focusing on dietary proscriptions over a wide range of illnesses, some specific restrictions reported here do correspond with trends seen in other Amazonian societies, particularly those related to sympathetic reasoning and for magical and spiritual uses of plants.

Explanatory models of addictive behaviour among native German, Russian-German, and Turkish youth.

PubMed

Penka, S; Heimann, H; Heinz, A; Schouler-Ocak, M

2008-01-01

In Germany, the public system of addiction treatment is used less by migrants with addictive disorders than by their non-migrant counterparts. To date, the literature has focused primarily on language, sociocultural factors, and residence status when discussing access barriers to this part of the health care system. However, little attention has been paid to cultural differences in explanatory models of addictive behaviour. This is surprising when we consider the important role played by popular knowledge in a population's perceptions of and responses to illnesses, including their causes, symptoms, and treatment. In the present study, we examined explanatory models of addictive behaviour and of mental disorders in 124 native German und Russian-German youth and compared these models to those observed in an earlier study of 144 German and Turkish youth. We employed the free listing technique German and to compile the terms that participating subjects used to describe addictive behaviour. Subsequently, we examined how a subset of our study population assigned these terms to the respective disorders by means of the pile sort method. Although the explanatory models used by the German and Russian-German youth in our study were surprisingly similar, those employed by Turkish youth did not make any fundamental distinction between illegal and legal drugs (e.g. alcohol and nicotine). German and Russian-German youth regarded eating disorders as "embarrassing" or "disgraceful", but Turkish youth did not. Unlike our German and Russian-German subjects, the Turkish youth did not classify eating disorders as being addictive in nature. Moreover, medical concepts crucial to a proper understanding of dependence disorders (e.g. the term "physical dependence") were characterised by almost half of our Turkish subjects as useless in describing addictions. These findings show that it is impossible to translate medical or everyday concepts of disease and treatment properly into a different language without considering the connotations and implications of each term as it relates to the respective culture. Terms that are central to Western medical models of disease may otherwise be misunderstood, misinterpreted, or simply rejected.

Darwinism and cultural change.

PubMed

Godfrey-Smith, Peter

2012-08-05

Evolutionary models of cultural change have acquired an important role in attempts to explain the course of human evolution, especially our specialization in knowledge-gathering and intelligent control of environments. In both biological and cultural change, different patterns of explanation become relevant at different 'grains' of analysis and in contexts associated with different explanatory targets. Existing treatments of the evolutionary approach to culture, both positive and negative, underestimate the importance of these distinctions. Close attention to grain of analysis motivates distinctions between three possible modes of cultural evolution, each associated with different empirical assumptions and explanatory roles.

How do Small Groups Promote Behaviour Change? An Integrative Conceptual Review of Explanatory Mechanisms.

PubMed

Borek, Aleksandra J; Abraham, Charles

2018-03-01

Small groups are used to promote health, well-being, and personal change by altering members' perceptions, beliefs, expectations, and behaviour patterns. An extensive cross-disciplinary literature has articulated and tested theories explaining how such groups develop, function, and facilitate change. Yet these theoretical understandings are rarely applied in the development, description, and evaluation of health-promotion, group-based, behaviour-change interventions. Medline database, library catalogues, search engines, specific journals and reference lists were searched for relevant texts. Texts were reviewed for explanatory concepts or theories describing change processes in groups, which were integrated into the developing conceptual structure. This was designed to be a parsimonious conceptual framework that could be applied to design and delivery. Five categories of interacting processes and concepts were identified and defined: (1) group development processes, (2) dynamic group processes, (3) social change processes, (4) personal change processes, and (5) group design and operating parameters. Each of these categories encompasses a variety of theorised mechanisms explaining individual change in small groups. The final conceptual model, together with the design issues and practical recommendations derived from it, provides a practical basis for linking research and theory explaining group functioning to optimal design of group-based, behaviour-change interventions. © 2018 The Authors. Applied Psychology: Health and Well-Being published by John Wiley & Sons Ltd on behalf of International Association of Applied Psychology.

Spatial generalised linear mixed models based on distances.

PubMed

Melo, Oscar O; Mateu, Jorge; Melo, Carlos E

2016-10-01

Risk models derived from environmental data have been widely shown to be effective in delineating geographical areas of risk because they are intuitively easy to understand. We present a new method based on distances, which allows the modelling of continuous and non-continuous random variables through distance-based spatial generalised linear mixed models. The parameters are estimated using Markov chain Monte Carlo maximum likelihood, which is a feasible and a useful technique. The proposed method depends on a detrending step built from continuous or categorical explanatory variables, or a mixture among them, by using an appropriate Euclidean distance. The method is illustrated through the analysis of the variation in the prevalence of Loa loa among a sample of village residents in Cameroon, where the explanatory variables included elevation, together with maximum normalised-difference vegetation index and the standard deviation of normalised-difference vegetation index calculated from repeated satellite scans over time. © The Author(s) 2013.

Logit Models for the Analysis of Two-Way Categorical Data

ERIC Educational Resources Information Center

Draxler, Clemens

2011-01-01

This article discusses the application of logit models for the analyses of 2-way categorical observations. The models described are generalized linear models using the logit link function. One of the models is the Rasch model (Rasch, 1960). The objective is to test hypotheses of marginal and conditional independence between explanatory quantities…

Understanding and Forecasting Ethnolinguistic Vitality

ERIC Educational Resources Information Center

Karan, Mark E.

2011-01-01

Forecasting of ethnolinguistic vitality can only be done within a well-functioning descriptive and explanatory model of the dynamics of language stability and shift. It is proposed that the Perceived Benefit Model of Language Shift, used with a taxonomy of language shift motivations, provides that model. The model, based on individual language…

A Note on the Heterogeneous Choice Model

ERIC Educational Resources Information Center

Rohwer, Goetz

2015-01-01

The heterogeneous choice model (HCM) has been proposed as an extension of the standard logit and probit models, which allows taking into account different error variances of explanatory variables. In this note, I show that in an important special case, this model is just another way to specify an interaction effect.

The Relationship between Students' Epistemologies and Model-Based Reasoning.

ERIC Educational Resources Information Center

Gobert, Janice; Discenna, Jennifer

Models and modeling are frequently used as instructional tools in science education to convey important information concerning both the explanatory and structural features of topic areas in science. The efficacy of models as such rests almost entirely upon students' ability to conceptualize them as abstracted "representations" of…

Targets, Effects, and Perpetrators of Sexual Harassment in Newsrooms.

ERIC Educational Resources Information Center

Brown, Cindy M.; Flatow, Gail M.

1997-01-01

Surveys Indiana journalists, finding that two models tested (the sociocultural model and the organizational model, both grounded in conception of power differences between harassed and harasser) have explanatory power, but that they explain the same results in different ways and sometimes combinations of the models provide better explanations of…

Meteorological influences on the interannual variability of meningitis incidence in northwest Nigeria.

NASA Astrophysics Data System (ADS)

Abdussalam, Auwal; Monaghan, Andrew; Dukic, Vanja; Hayden, Mary; Hopson, Thomas; Leckebusch, Gregor

2013-04-01

Northwest Nigeria is a region with high risk of bacterial meningitis. Since the first documented epidemic of meningitis in Nigeria in 1905, the disease has been endemic in the northern part of the country, with epidemics occurring regularly. In this study we examine the influence of climate on the interannual variability of meningitis incidence and epidemics. Monthly aggregate counts of clinically confirmed hospital-reported cases of meningitis were collected in northwest Nigeria for the 22-year period spanning 1990-2011. Several generalized linear statistical models were fit to the monthly meningitis counts, including generalized additive models. Explanatory variables included monthly records of temperatures, humidity, rainfall, wind speed, sunshine and dustiness from weather stations nearest to the hospitals, and a time series of polysaccharide vaccination efficacy. The effects of other confounding factors -- i.e., mainly non-climatic factors for which records were not available -- were estimated as a smooth, monthly-varying function of time in the generalized additive models. Results reveal that the most important explanatory climatic variables are mean maximum monthly temperature, relative humidity and dustiness. Accounting for confounding factors (e.g., social processes) in the generalized additive models explains more of the year-to-year variation of meningococcal disease compared to those generalized linear models that do not account for such factors. Promising results from several models that included only explanatory variables that preceded the meningitis case data by 1-month suggest there may be potential for prediction of meningitis in northwest Nigeria to aid decision makers on this time scale.

Spatial modelling of landscape aesthetic potential in urban-rural fringes.

PubMed

Sahraoui, Yohan; Clauzel, Céline; Foltête, Jean-Christophe

2016-10-01

The aesthetic potential of landscape has to be modelled to provide tools for land-use planning. This involves identifying landscape attributes and revealing individuals' landscape preferences. Landscape aesthetic judgments of individuals (n = 1420) were studied by means of a photo-based survey. A set of landscape visibility metrics was created to measure landscape composition and configuration in each photograph using spatial data. These metrics were used as explanatory variables in multiple linear regressions to explain aesthetic judgments. We demonstrate that landscape aesthetic judgments may be synthesized in three consensus groups. The statistical results obtained show that landscape visibility metrics have good explanatory power. Ultimately, we propose a spatial modelling of landscape aesthetic potential based on these results combined with systematic computation of visibility metrics. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicting factors for malaria re-introduction: an applied model in an elimination setting to prevent malaria outbreaks.

PubMed

Ranjbar, Mansour; Shoghli, Alireza; Kolifarhood, Goodarz; Tabatabaei, Seyed Mehdi; Amlashi, Morteza; Mohammadi, Mahdi

2016-03-02

Malaria re-introduction is a challenge in elimination settings. To prevent re-introduction, receptivity, vulnerability, and health system capacity of foci should be monitored using appropriate tools. This study aimed to design an applicable model to monitor predicting factors of re-introduction of malaria in highly prone areas. This exploratory, descriptive study was conducted in a pre-elimination setting with a high-risk of malaria transmission re-introduction. By using nominal group technique and literature review, a list of predicting indicators for malaria re-introduction and outbreak was defined. Accordingly, a checklist was developed and completed in the field for foci affected by re-introduction and for cleared-up foci as a control group, for a period of 12 weeks before re-introduction and for the same period in the previous year. Using field data and analytic hierarchical process (AHP), each variable and its sub-categories were weighted, and by calculating geometric means for each sub-category, score of corresponding cells of interaction matrices, lower and upper threshold of different risks strata, including low and mild risk of re-introduction and moderate and high risk of malaria outbreaks, were determined. The developed predictive model was calibrated through resampling with different sets of explanatory variables using R software. Sensitivity and specificity of the model were calculated based on new samples. Twenty explanatory predictive variables of malaria re-introduction were identified and a predictive model was developed. Unpermitted immigrants from endemic neighbouring countries were determined as a pivotal factor (AHP score: 0.181). Moreover, quality of population movement (0.114), following malaria transmission season (0.088), average daily minimum temperature in the previous 8 weeks (0.062), an outdoor resting shelter for vectors (0.045), and rainfall (0.042) were determined. Positive and negative predictive values of the model were 81.8 and 100 %, respectively. This study introduced a new, simple, yet reliable model to forecast malaria re-introduction and outbreaks eight weeks in advance in pre-elimination and elimination settings. The model incorporates comprehensive deterministic factors that can easily be measured in the field, thereby facilitating preventive measures.

Spatial analysis of agri-environmental policy uptake and expenditure in Scotland.

PubMed

Yang, Anastasia L; Rounsevell, Mark D A; Wilson, Ronald M; Haggett, Claire

2014-01-15

Agri-environment is one of the most widely supported rural development policy measures in Scotland in terms of number of participants and expenditure. It comprises 69 management options and sub-options that are delivered primarily through the competitive 'Rural Priorities scheme'. Understanding the spatial determinants of uptake and expenditure would assist policy-makers in guiding future policy targeting efforts for the rural environment. This study is unique in examining the spatial dependency and determinants of Scotland's agri-environmental measures and categorised options uptake and payments at the parish level. Spatial econometrics is applied to test the influence of 40 explanatory variables on farming characteristics, land capability, designated sites, accessibility and population. Results identified spatial dependency for each of the dependent variables, which supported the use of spatially-explicit models. The goodness of fit of the spatial models was better than for the aspatial regression models. There was also notable improvement in the models for participation compared with the models for expenditure. Furthermore a range of expected explanatory variables were found to be significant and varied according to the dependent variable used. The majority of models for both payment and uptake showed a significant positive relationship with SSSI (Sites of Special Scientific Interest), which are designated sites prioritised in Scottish policy. These results indicate that environmental targeting efforts by the government for AEP uptake in designated sites can be effective. However habitats outside of SSSI, termed here the 'wider countryside' may not be sufficiently competitive to receive funding in the current policy system. Copyright © 2013 Elsevier Ltd. All rights reserved.

An Institutional Model of Organizational Practice: Financial Reporting at the Fortune 200.

ERIC Educational Resources Information Center

Mezias, Stephen J.

1990-01-01

Compares applied economic models and an institutional model in an empirical study of financial reporting practice at the Fortune 200 between 1962 and 1984. Findings indicate that the institutional model adds significant explanatory power over and above the models currently dominating the applied economics literature. Includes 47 references. (MLH)

Extended Mixed-Efects Item Response Models with the MH-RM Algorithm

ERIC Educational Resources Information Center

Chalmers, R. Philip

2015-01-01

A mixed-effects item response theory (IRT) model is presented as a logical extension of the generalized linear mixed-effects modeling approach to formulating explanatory IRT models. Fixed and random coefficients in the extended model are estimated using a Metropolis-Hastings Robbins-Monro (MH-RM) stochastic imputation algorithm to accommodate for…

Landscape risk factors for Lyme disease in the eastern broadleaf forest province of the Hudson River valley and the effect of explanatory data classification resolution.

PubMed

Messier, Kyle P; Jackson, Laura E; White, Jennifer L; Hilborn, Elizabeth D

2015-01-01

This study assessed how landcover classification affects associations between landscape characteristics and Lyme disease rate. Landscape variables were derived from the National Land Cover Database (NLCD), including native classes (e.g., deciduous forest, developed low intensity) and aggregate classes (e.g., forest, developed). Percent of each landcover type, median income, and centroid coordinates were calculated by census tract. Regression results from individual and aggregate variable models were compared with the dispersion parameter-based R(2) (Rα(2)) and AIC. The maximum Rα(2) was 0.82 and 0.83 for the best aggregate and individual model, respectively. The AICs for the best models differed by less than 0.5%. The aggregate model variables included forest, developed, agriculture, agriculture-squared, y-coordinate, y-coordinate-squared, income and income-squared. The individual model variables included deciduous forest, deciduous forest-squared, developed low intensity, pasture, y-coordinate, y-coordinate-squared, income, and income-squared. Results indicate that regional landscape models for Lyme disease rate are robust to NLCD landcover classification resolution. Published by Elsevier Ltd.

Parent Predictors of Adolescents' Explanatory Style

ERIC Educational Resources Information Center

Vélez, Clorinda E.; Krause, Elizabeth D.; Brunwasser, Steven M.; Freres, Derek R.; Abenavoli, Rachel M.; Gillham, Jane E.

2015-01-01

The current study tested the prospective relations (6-month lag) between three aspects of the parent-child relationship at Time 1 (T1) and adolescents' explanatory styles at Time 2 (T2): caregiving behaviors, parents' explanatory style for their own negative events, and parents' explanatory style for their children's negative events. The sample…

Infection Elicited Autoimmunity and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: An Explanatory Model

PubMed Central

Blomberg, Jonas; Gottfries, Carl-Gerhard; Elfaitouri, Amal; Rizwan, Muhammad; Rosén, Anders

2018-01-01

Myalgic encephalomyelitis (ME) often also called chronic fatigue syndrome (ME/CFS) is a common, debilitating, disease of unknown origin. Although a subject of controversy and a considerable scientific literature, we think that a solid understanding of ME/CFS pathogenesis is emerging. In this study, we compiled recent findings and placed them in the context of the clinical picture and natural history of the disease. A pattern emerged, giving rise to an explanatory model. ME/CFS often starts after or during an infection. A logical explanation is that the infection initiates an autoreactive process, which affects several functions, including brain and energy metabolism. According to our model for ME/CFS pathogenesis, patients with a genetic predisposition and dysbiosis experience a gradual development of B cell clones prone to autoreactivity. Under normal circumstances these B cell offsprings would have led to tolerance. Subsequent exogenous microbial exposition (triggering) can lead to comorbidities such as fibromyalgia, thyroid disorder, and orthostatic hypotension. A decisive infectious trigger may then lead to immunization against autoantigens involved in aerobic energy production and/or hormone receptors and ion channel proteins, producing postexertional malaise and ME/CFS, affecting both muscle and brain. In principle, cloning and sequencing of immunoglobulin variable domains could reveal the evolution of pathogenic clones. Although evidence consistent with the model accumulated in recent years, there are several missing links in it. Hopefully, the hypothesis generates testable propositions that can augment the understanding of the pathogenesis of ME/CFS. PMID:29497420

The International Classification of Functioning as an explanatory model of health after distal radius fracture: A cohort study

PubMed Central

Harris, Jocelyn E; MacDermid, Joy C; Roth, James

2005-01-01

Background Distal radius fractures are common injuries that have an increasing impact on health across the lifespan. The purpose of this study was to identify health impacts in body structure/function, activity, and participation at baseline and follow-up, to determine whether they support the ICF model of health. Methods This is a prospective cohort study of 790 individuals who were assessed at 1 week, 3 months, and 1 year post injury. The Patient Rated Wrist Evaluation (PRWE), The Wrist Outcome Measure (WOM), and the Medical Outcome Survey Short-Form (SF-36) were used to measure impairment, activity, participation, and health. Multiple regression was used to develop explanatory models of health outcome. Results Regression analysis showed that the PRWE explained between 13% (one week) and 33% (three months) of the SF-36 Physical Component Summary Scores with pain, activities and participation subscales showing dominant effects at different stages of recovery. PRWE scores were less related to Mental Component Summary Scores, 10% (three months) and 8% (one year). Wrist impairment scores were less powerful predictors of health status than the PRWE. Conclusion The ICF is an informative model for examining distal radius fracture. Difficulty in the domains of activity and participation were able to explain a significant portion of physical health. Post-fracture rehabilitation and outcome assessments should extend beyond physical impairment to insure comprehensive treatment to individuals with distal radius fracture. PMID:16288664

Explanatory style across the life span: evidence for stability over 52 years.

PubMed

Burns, M O; Seligman, M E

1989-03-01

Analyzed explanatory style across the life span. 30 Ss whose average age was 72 responded to questions about their current life and provided diaries or letters written in their youth, an average of 52 years earlier. A blind content analysis of explanatory style derived from these 2 sources revealed that explanatory style for negative events was stable throughout adult life (r = .54, p less than .002). In contrast, there appeared to be no stability of explanatory style for positive events between the same 2 time periods. These results suggest that explanatory style for negative events may persist across the life span and may constitute an enduring risk factor for depression, low achievement, and physical illness.

Improving the physiological realism of experimental models.

PubMed

Vinnakota, Kalyan C; Cha, Chae Y; Rorsman, Patrik; Balaban, Robert S; La Gerche, Andre; Wade-Martins, Richard; Beard, Daniel A; Jeneson, Jeroen A L

2016-04-06

The Virtual Physiological Human (VPH) project aims to develop integrative, explanatory and predictive computational models (C-Models) as numerical investigational tools to study disease, identify and design effective therapies and provide an in silico platform for drug screening. Ultimately, these models rely on the analysis and integration of experimental data. As such, the success of VPH depends on the availability of physiologically realistic experimental models (E-Models) of human organ function that can be parametrized to test the numerical models. Here, the current state of suitable E-models, ranging from in vitro non-human cell organelles to in vivo human organ systems, is discussed. Specifically, challenges and recent progress in improving the physiological realism of E-models that may benefit the VPH project are highlighted and discussed using examples from the field of research on cardiovascular disease, musculoskeletal disorders, diabetes and Parkinson's disease.

Artificialized land characteristics and sediment connectivity explain muddy flood hazard in Wallonia

NASA Astrophysics Data System (ADS)

de Walque, Baptiste; Bielders, Charles; Degré, Aurore; Maugnard, Alexandre

2017-04-01

Muddy flood occurrence is an off-site erosion problem of growing interest in Europe and in particular in the loess belt and Condroz regions of Wallonia (Belgium). In order to assess the probability of occurrence of muddy floods in specific places, a muddy flood hazard prediction model has been built. It was used to test 11 different explanatory variables in simple and multiple logistic regressions approaches. A database of 442 muddy flood-affected sites and an equal number of homologous non flooded sites was used. For each site, relief, land use, sediment production and sediment connectivity of the contributing area were extracted. To assess the prediction quality of the model, we proceeded to a validation using 48 new pairs of homologous sites. Based on Akaïke Information Criterion (AIC), we determined that the best muddy flood hazard assessment model requires a total of 6 explanatory variable as inputs: the spatial aggregation of the artificialized land, the sediment connectivity, the artificialized land proximity to the outlet, the proportion of artificialized land, the mean slope and the Gravelius index of compactness of the contributive area. The artificialized land properties listed above showed to improve substantially the model quality (p-values from 10e-10 to 10e-4). All of the 3 properties showed negative correlation with the muddy flood hazard. These results highlight the importance of considering the artificialized land characteristics in the sediment transport assessment models. Indeed, artificialized land such as roads may dramatically deviate flows and influence the connectivity in the landscape. Besides the artificialized land properties, the sediment connectivity showed significant explanatory power (p-value of 10e-11). A positive correlation between the sediment connectivity and the muddy flood hazard was found, ranging from 0.3 to 0.45 depending on the sediment connectivity index. Several studies already have highlighted the importance of this parameter in the sediment transport characterization in the landscape. Using the best muddy flood probability of occurrence threshold value of 0.49, the validation of the best multiple logistic regression resulted in a prediction quality of 75.6% (original dataset) and 81.2% (secondary dataset). The developed statistical model could be used as a reliable tool to target muddy floods mitigation measures in sites resulting with the highest muddy floods hazard.

Natural language from artificial life.

PubMed

Kirby, Simon

2002-01-01

This article aims to show that linguistics, in particular the study of the lexico-syntactic aspects of language, provides fertile ground for artificial life modeling. A survey of the models that have been developed over the last decade and a half is presented to demonstrate that ALife techniques have a lot to offer an explanatory theory of language. It is argued that this is because much of the structure of language is determined by the interaction of three complex adaptive systems: learning, culture, and biological evolution. Computational simulation, informed by theoretical linguistics, is an appropriate response to the challenge of explaining real linguistic data in terms of the processes that underpin human language.

Sexual function in women in rural Tamil Nadu: disease, dysfunction, distress and norms.

PubMed

Viswanathan, Shonima; Prasad, Jasmine; Jacob, K S; Kuruvilla, Anju

2014-01-01

We examined the nature, prevalence and explanatory models of sexual concerns and dysfunction among women in rural Tamil Nadu. Married women between 18 and 65 years of age, from randomly selected villages in Kaniyambadi block, Vellore district, Tamil Nadu, were chosen by stratified sampling technique. Sexual functioning was assessed using the Female Sexual Function Index (FSFI). The modified Short Explanatory Model Interview (SEMI) was used to assess beliefs about sexual concerns and the General Health Questionnaire-12 (GHQ-12) was used to screen for common mental disorders. Sociodemographic variables and other risk factors were also assessed. Most of the women (277; 98.2%) contacted agreed to participate in the study. The prevalence of sexual dysfunction, based on the cut-off score on the FSFI, was 64.3%. However, only a minority of women considered it a problem (4.7%), expressed dissatisfaction (5.8%) or sought medical help (2.5%). The most common explanatory models offered for sexual problems included an unhappy marriage,stress and physical problems. Factors associated with lower FSFI included older age, illiteracy, as well as medical illness and sexual and marital factors such as menopause, poor quality of marital relationship, history of physical abuse and lack of privacy. The diagnosis of female sexual dysfunction needs to be nuanced and based on the broader personal and social context. Our findings argue that there is a need to use models that employ personal, local and contextual standards in assessing complex behaviours such as sexual function. Copyright 2014, NMJI.

Land Use, Land Conservation, and Wind Energy Development Outcomes in New England

NASA Astrophysics Data System (ADS)

Weimar, William Cameron

This dissertation provides three independent research inquiries. The first examines how inter-governmental policy, site-specific, and social factors lead to the success, prolonged delay, or failure of inland wind power projects in New England. The three case studies examined include the 48 megawatt Glebe Mountain Wind Farm proposal in southern Vermont, the 30 megawatt Hoosac Wind Farm in western Massachusetts, and the 24 megawatt Lempster Wind Farm in southern New Hampshire. To ascertain why the project outcomes varied, 45 semi-structured interviews were conducted with a range of stakeholders, including wind development firms, utility companies, state regulatory agencies, regional planning commissions, town officials, land conservation organizations, and opposition groups. The second study establishes a comprehensive set of thirty-seven explanatory variables to determine the amount of suitable land and the corresponding electricity generation potential within the prime wind resource areas of Western Massachusetts. The explanatory variables are incorporated into Boolean GIS suitability models which represent the two divergent positions towards wind power development in Massachusetts, and a third, balanced model. The third study determines that exurban residential development is not the only land use factor that reduces wind power development potential in Western Massachusetts. A set of Boolean GIS models for 1985 and 2009 find the onset of conservation easements on private lands having the largest impact. During this 25 year period a combination of land use conversion and land conservation has reduced the access to prime wind resource areas by 18% (11,601 hectares), an equivalent loss of 5,800--8,700 GWh/year of zero carbon electricity generation. The six main findings from this research are: (1) Visual aesthetics remain the main factor of opposition to specific projects; (2) The Not-in-my Backyard debate for wind power remains unsettled; (3) Widespread support exists for regional land use energy plans; (4) The wind resources of Western Massachusetts can significantly contribute to the state's current renewable portfolio standard while balancing conservation and renewable energy development objectives; However, (5) a combination of exurban residential development and conservation easements significantly reduces wind power development potential over time; and (6) a need exists to legally define wind as a publicly beneficial resource.

Empathy and Child Neglect: A Theoretical Model

ERIC Educational Resources Information Center

De Paul, Joaquin; Guibert, Maria

2008-01-01

Objective: To present an explanatory theory-based model of child neglect. This model does not address neglectful behaviors of parents with mental retardation, alcohol or drug abuse, or severe mental health problems. In this model parental behavior aimed to satisfy a child's need is considered a helping behavior and, as a consequence, child neglect…

Empathy and child neglect: a theoretical model.

PubMed

De Paul, Joaquín; Guibert, María

2008-11-01

To present an explanatory theory-based model of child neglect. This model does not address neglectful behaviors of parents with mental retardation, alcohol or drug abuse, or severe mental health problems. In this model parental behavior aimed to satisfy a child's need is considered a helping behavior and, as a consequence, child neglect is considered as a specific type of non-helping behavior. The central hypothesis of the theoretical model presented here suggests that neglectful parents cannot develop the helping response set to care for their children because the observation of a child's signal of need does not lead to the experience of emotions that motivate helping or because the parents experience these emotions, but specific cognitions modify the motivation to help. The present theoretical model suggests that different typologies of neglectful parents could be developed based on different reasons that parents might not to experience emotions that motivate helping behaviors. The model can be helpful to promote new empirical studies about the etiology of different groups of neglectful families.

Using Qualitative Methods to Explore Lay Explanatory Models, Health-Seeking Behaviours and Self-Care Practices of Podoconiosis Patients in North-West Ethiopia

PubMed Central

Banks, Harrison S.; Tsegay, Girmay; Wubie, Moges; Tamiru, Abreham; Davey, Gail; Cooper, Max

2016-01-01

Background Podoconiosis (endemic non-filarial elephantiasis) is a chronic, non-infectious disease resulting from exposure of bare feet to red-clay soil in tropical highlands. This study examined lay beliefs about three under-researched aspects of podoconiosis patients’ care: explanatory models, health-seeking behaviours and self-care. Methods In-depth interviews and focus group discussions were undertaken with 34 participants (19 male, 15 female) between April-May 2015 at podoconiosis treatment centres across East and West Gojjam regions in north-west Ethiopia. Results Explanatory models for podoconiosis included contamination from blood, magic, soil or affected individuals. Belief in heredity or divine punishment often delayed clinic attendance. All participants had tried holy water treatment and some, holy soil. Herbal treatments were considered ineffectual, costly and appeared to promote fluid escape. Motivators for clinic attendance were failure of traditional treatments and severe or disabling symptoms. Patients did not report self-treatment with antibiotics. Self-care was hindered by water being unavailable or expensive and patient fatigue. Conclusion A pluralistic approach to podoconiosis self-treatment was discovered. Holy water is widely valued, though some patients prefer holy soil. Priests and traditional healers could help promote self-care and “signpost” patients to clinics. Change in behaviour and improving water access is key to self-care. PMID:27536772

[Multilevel analysis of the technical efficiency of hospitals in the Spanish National Health System by property and type of management].

PubMed

Pérez-Romero, Carmen; Ortega-Díaz, M Isabel; Ocaña-Riola, Ricardo; Martín-Martín, José Jesús

2018-05-11

To analyze technical efficiency by type of property and management of general hospitals in the Spanish National Health System (2010-2012) and identify hospital and regional explanatory variables. 230 hospitals were analyzed combining data envelopment analysis and fixed effects multilevel linear models. Data envelopment analysis measured overall, technical and scale efficiency, and the analysis of explanatory factors was performed using multilevel models. The average rate of overall technical efficiency of hospitals without legal personality is lower than hospitals with legal personality (0.691 and 0.876 in 2012). There is a significant variability in efficiency under variable returns (TE) by direct, indirect and mixed forms of management. The 29% of the variability in TE es attributable to the Region. Legal personality increased the TE of the hospitals by 11.14 points. On the other hand, most of the forms of management (different to those of the traditional hospitals) increased TE in varying percentages. At regional level, according to the model considered, insularity and average annual income per household are explanatory variables of TE. Having legal personality favours technical efficiency. The regulatory and management framework of hospitals, more than public or private ownership, seem to explain technical efficiency. Regional characteristics explain the variability in TE. Copyright © 2018 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Oral health of 65-year olds in Sweden and Norway: a global question and ICF, the latest conceptual model from WHO.

PubMed

Ekbäck, Gunnar; Åstrøm, Anne Nordrehaug; Klock, Kristin; Ordell, Sven; Unell, Lennart

2012-07-01

The aims of this study were to identify explanatory factors of satisfaction with oral health among Norwegian and Swedish 65 year olds in terms of items from four different domains of ICF and to compare the strengths of the various ICF domains in explaining satisfaction with oral health. Further it was to assess whether the explanatory factors of ICF domains vary between Norway and Sweden. In 2007, standardized questionnaires were mailed to all the residents in certain counties of Sweden and Norway who were born in 1942. Response rates were 73.1% (n = 6078) in Sweden and 56.0% (n = 4062) in Norway. In total, 33 questions based on four different ICF domains were chosen to explain satisfaction with oral health. Logistic regression showed that four different ICF domains in terms of body function, body structure, activity/participation and environmental factors explained, respectively, 53%, 31%, 12% and 34% of the explanatory variance in the satisfaction with oral health. In the final analysis, only nine items were statistically significant (p < 0.05). This study indicates that ICF as a conceptual model could cover a broad spectrum of factors embedded in OHRQoL measured by a global question in Sweden and Norway. Nine items, representing four ICF domains, were important in the final model for explaining satisfaction with oral health.

Azúcar y nervios: explanatory models and treatment experiences of Hispanics with diabetes and depression.

PubMed

Cabassa, Leopoldo J; Hansen, Marissa C; Palinkas, Lawrence A; Ell, Kathleen

2008-06-01

This study examined the explanatory models of depression, perceived relationships between diabetes and depression, and depression treatment experiences of low-income, Spanish-speaking, Hispanics with diabetes and depression. A purposive sample (n=19) was selected from participants enrolled in a randomized controlled trial conducted in Los Angeles, California (United States) testing the effectiveness of a health services quality improvement intervention. Four focus groups followed by 10 in-depth semi-structured qualitative interviews were conducted. Data were analyzed using the methodology of coding, consensus, co-occurrence, and comparison, an analytical strategy rooted in grounded theory. Depression was perceived as a serious condition linked to the accumulation of social stressors. Somatic and anxiety-like symptoms and the cultural idiom of nervios were central themes in low-income Hispanics' explanatory models of depression. The perceived reciprocal relationships between diabetes and depression highlighted the multiple pathways by which these two illnesses impact each other and support the integration of diabetes and depression treatments. Concerns about depression treatments included fears about the addictive and harmful properties of antidepressants, worries about taking too many pills, and the stigma attached to taking psychotropic medications. This study provides important insights about the cultural and social dynamics that shape low-income Hispanics' illness and treatment experiences and support the use of patient-centered approaches to reduce the morbidity and mortality associated with diabetes and depression.

“Azúcar y Nervios: Explanatory Models and Treatment Experiences of Hispanics with Diabetes and Depression”

PubMed Central

Hansen, Marissa C; Palinkas, Lawrence A; Ell, Kathleen

2008-01-01

This study examined the explanatory models of depression, perceived relationships between diabetes and depression, and depression treatment experiences of low-income, Spanish-speaking, Hispanics with diabetes and depression. A purposive sample (n =19) was selected from participants enrolled in a randomized controlled trial conducted in Los Angeles, California (US) testing the effectiveness of a health services quality improvement intervention. Four focus groups followed by 10 in-depth semi-structured qualitative interviews were conducted. Data were analyzed using the methodology of coding, consensus, co-occurrence, and comparison, an analytical strategy rooted in grounded theory. Depression was perceived as a serious condition linked to the accumulation of social stressors. Somatic and anxiety-like symptoms and the cultural idiom of nervios were central themes in low-income Hispanics’ explanatory models of depression. The perceived reciprocal relationships between diabetes and depression highlighted the multiple pathways by which these two illnesses impact each other and support the integration of diabetes and depression treatments. Concerns about depression treatments included fears about the addictive and harmful properties of antidepressants, worries about taking too many pills, and the stigma attached to taking psychotropic medications. This study provides important insights about the cultural and social dynamics that shape low-income Hispanics’ illness and treatment experiences and support the use of patient-centered approaches to reduce the morbidity and mortality associated with diabetes and depression. PMID:18339466

The effect of topography on arctic-alpine aboveground biomass and NDVI patterns

NASA Astrophysics Data System (ADS)

Riihimäki, Henri; Heiskanen, Janne; Luoto, Miska

2017-04-01

Topography is a key factor affecting numerous environmental phenomena, including Arctic and alpine aboveground biomass (AGB) distribution. Digital Elevation Model (DEM) is a source of topographic information which can be linked to local growing conditions. Here, we investigated the effect of DEM derived variables, namely elevation, topographic position, radiation and wetness on AGB and Normalized Difference Vegetation Index (NDVI) in a Fennoscandian forest-alpine tundra ecotone. Boosted regression trees were used to derive non-parametric response curves and relative influences of the explanatory variables. Elevation and potential incoming solar radiation were the most important explanatory variables for both AGB and NDVI. In the NDVI models, the response curves were smooth compared with AGB models. This might be caused by large contribution of field and shrub layer to NDVI, especially at the treeline. Furthermore, radiation and elevation had a significant interaction, showing that the highest NDVI and biomass values are found from low-elevation, high-radiation sites, typically on the south-southwest facing valley slopes. Topographic wetness had minor influence on AGB and NDVI. Topographic position had generally weak effects on AGB and NDVI, although protected topographic position seemed to be more favorable below the treeline. The explanatory power of the topographic variables, particularly elevation and radiation demonstrates that DEM-derived land surface parameters can be used for exploring biomass distribution resulting from landform control on local growing conditions.

The use of cognitive ability measures as explanatory variables in regression analysis.

PubMed

Junker, Brian; Schofield, Lynne Steuerle; Taylor, Lowell J

2012-12-01

Cognitive ability measures are often taken as explanatory variables in regression analysis, e.g., as a factor affecting a market outcome such as an individual's wage, or a decision such as an individual's education acquisition. Cognitive ability is a latent construct; its true value is unobserved. Nonetheless, researchers often assume that a test score , constructed via standard psychometric practice from individuals' responses to test items, can be safely used in regression analysis. We examine problems that can arise, and suggest that an alternative approach, a "mixed effects structural equations" (MESE) model, may be more appropriate in many circumstances.

Emerging paradigms in medicine: implications for the future of psychiatry.

PubMed

Lake, James

2007-01-01

The causes of mental illness remain obscure in spite of rapid progress in the neurosciences. This is due in part to the fact that contemporary biomedical psychiatry rests on philosophically and scientifically ambiguous ground. In Western medicine paradigms, theories from physics, chemistry, and biology form the basis of an explanatory model of illness, including mental illness. Symptoms are conceptualized as subjective descriptions of effects caused by factors characterized in empirical terms. Conventional biomedicine asserts that all causes of illness, and by extension, mechanisms of action underlying legitimate treatment approaches, rest on biological processes that can be described in the reductionist language of Western science. However, in contemporary Western psychiatry, there is no single adequate explanatory model of the causes of mental illness. What remains are competing psychodynamic, genetic, endocrinologic, and neurobiological models of symptom formation reflecting disparate ideological positions and diverse clinical training backgrounds of mental health professionals. There is no unifying theory in psychiatry because no single explanatory model has been confirmed as more valid than any other. I hypothesize in this article that the synthesis of ideas and clinical approaches from Western biomedicine and non-Western systems of medicine based on understandings of human consciousness, the neurosciences, complexity theory, and quantum field theory, will lead to rapid evolution of conventional Western biomedical psychiatry toward truly integrative mental healthcare. The result will be the emergence of an integrative mental healthcare model that will more adequately address the disparate causes, conditions, and meanings of symptoms combining multimodal approaches from Western biomedicine and non-Western systems of medicine.

Developing a Learning Progression of Buoyancy to Model Conceptual Change: A Latent Class and Rule Space Model Analysis

NASA Astrophysics Data System (ADS)

Gao, Yizhu; Zhai, Xiaoming; Andersson, Björn; Zeng, Pingfei; Xin, Tao

2018-06-01

We applied latent class analysis and the rule space model to verify the cumulative characteristic of conceptual change by developing a learning progression for buoyancy. For this study, we first abstracted seven attributes of buoyancy and then developed a hypothesized learning progression for buoyancy. A 14-item buoyancy instrument was administered to 1089 8th grade students to verify and refine the learning progression. The results suggest four levels of progression during conceptual change when 8th grade students understand buoyancy. Students at level 0 can only master Density. When students progress to level 1, they can grasp Direction, Identification, Submerged volume, and Relative density on the basis of the prior level. Then, students gradually master Archimedes' theory as they reach level 2. The most advanced students can further grasp Relation with motion and arrive at level 3. In addition, this four-level learning progression can be accounted for by the Qualitative-Quantitative-Integrative explanatory model.

Does IQ explain socio-economic differentials in total and cardiovascular disease mortality? Comparison with the explanatory power of traditional cardiovascular disease risk factors in the Vietnam Experience Study.

PubMed

Batty, G David; Shipley, Martin J; Dundas, Ruth; Macintyre, Sally; Der, Geoff; Mortensen, Laust H; Deary, Ian J

2009-08-01

The aim of this study was to examine the explanatory power of intelligence (IQ) compared with traditional cardiovascular disease (CVD) risk factors in the relationship of socio-economic disadvantage with total and CVD mortality, that is the extent to which IQ may account for the variance in this well-documented association. Cohort study of 4289 US male former military personnel with data on four widely used markers of socio-economic position (early adulthood and current income, occupational prestige, and education), IQ test scores (early adulthood and middle-age), a range of nine established CVD risk factors (systolic and diastolic blood pressure, total blood cholesterol, HDL cholesterol, body mass index, smoking, blood glucose, resting heart rate, and forced expiratory volume in 1 s), and later mortality. We used the relative index of inequality (RII) to quantify the relation between each index of socio-economic position and mortality. Fifteen years of mortality surveillance gave rise to 237 deaths (62 from CVD and 175 from 'other' causes). In age-adjusted analyses, as expected, each of the four indices of socio-economic position was inversely associated with total, CVD, and 'other' causes of mortality, such that elevated rates were evident in the most socio-economically disadvantaged men. When IQ in middle-age was introduced to the age-adjusted model, there was marked attenuation in the RII across the socio-economic predictors for total mortality (average 50% attenuation in RII), CVD (55%), and 'other' causes of death (49%). When the nine traditional risk factors were added to the age-adjusted model, the comparable reduction in RII was less marked than that seen after IQ adjustment: all-causes (40%), CVD (40%), and 'other' mortality (43%). Adding IQ to the latter model resulted in marked, additional explanatory power for all outcomes in comparison to the age-adjusted analyses: all-causes (63%), CVD (63%), and 'other' mortality (65%). When we utilized IQ in early adulthood rather than middle-age as an explanatory variable, the attenuating effect on the socio-economic gradient was less pronounced although the same pattern was still present. In the present analyses of socio-economic gradients in total and CVD mortality, IQ appeared to offer greater explanatory power than that apparent for traditional CVD risk factors.

A journey of a thousand miles begins with one small step - human agency, hydrological processes and time in socio-hydrology

NASA Astrophysics Data System (ADS)

Ertsen, M. W.; Murphy, J. T.; Purdue, L. E.; Zhu, T.

2014-04-01

When simulating social action in modeling efforts, as in socio-hydrology, an issue of obvious importance is how to ensure that social action by human agents is well-represented in the analysis and the model. Generally, human decision-making is either modeled on a yearly basis or lumped together as collective social structures. Both responses are problematic, as human decision-making is more complex and organizations are the result of human agency and cannot be used as explanatory forces. A way out of the dilemma of how to include human agency is to go to the largest societal and environmental clustering possible: society itself and climate, with time steps of years or decades. In the paper, another way out is developed: to face human agency squarely, and direct the modeling approach to the agency of individuals and couple this with the lowest appropriate hydrological level and time step. This approach is supported theoretically by the work of Bruno Latour, the French sociologist and philosopher. We discuss irrigation archaeology, as it is in this discipline that the issues of scale and explanatory force are well discussed. The issue is not just what scale to use: it is what scale matters. We argue that understanding the arrangements that permitted the management of irrigation over centuries requires modeling and understanding the small-scale, day-to-day operations and personal interactions upon which they were built. This effort, however, must be informed by the longer-term dynamics, as these provide the context within which human agency is acted out.

A journey of a thousand miles begins with one small step - human agency, hydrological processes and time in socio-hydrology

NASA Astrophysics Data System (ADS)

Ertsen, M. W.; Murphy, J. T.; Purdue, L. E.; Zhu, T.

2013-11-01

When simulating social action in modeling efforts, as in socio-hydrology, an issue of obvious importance is how to ensure that social action by human agents is well-represented in the analysis and the model. Generally, human decision-making is either modeled on a yearly basis or lumped together as collective social structures. Both responses are problematic, as human decision making is more complex and organizations are the result of human agency and cannot be used as explanatory forces. A way out of the dilemma how to include human agency is to go to the largest societal and environmental clustering possible: society itself and climate, with time steps of years or decades. In the paper, the other way out is developed: to face human agency squarely, and direct the modeling approach to the human agency of individuals and couple this with the lowest appropriate hydrological level and time step. This approach is supported theoretically by the work of Bruno Latour, the French sociologist and philosopher. We discuss irrigation archaeology, as it is in this discipline that the issues of scale and explanatory force are well discussed. The issue is not just what scale to use: it is what scale matters. We argue that understanding the arrangements that permitted the management of irrigation over centuries, requires modeling and understanding the small-scale, day-to-day operations and personal interactions upon which they were built. This effort, however, must be informed by the longer-term dynamics as these provide the context within which human agency, is acted out.

An explanatory model for state Medicaid per capita prescription drug expenditures.

PubMed

Roy, Sanjoy; Madhavan, S Suresh

2012-01-01

Rising prescription drug expenditure is a growing concern for publicly funded drug benefit programs like Medicaid. To be able to contain drug expenditures in Medicaid, it is important that cause(s) for such increases are identified. This study attempts to establish an explanatory model for Medicaid prescription drugs expenditure based on the impacts of key influencers/predictors identified using a comprehensive framework of drug utilization. A modified Andersen's behavior model of health services utilization is employed to identify potential determinants of pharmaceutical expenditures in state Medicaid programs. Level of federal matching funds, access to primary care, severity of diseases, unemployment, and education levels were found to be key influencers of Medicaid prescription drug expenditure. Increases in all, except education levels, were found to result in increases in drug expenditures. Findings from this study could better inform intervention policies and cost-containment strategies for state Medicaid drug benefit programs.

Modeling the frequency of opposing left-turn conflicts at signalized intersections using generalized linear regression models.

PubMed

Zhang, Xin; Liu, Pan; Chen, Yuguang; Bai, Lu; Wang, Wei

2014-01-01

The primary objective of this study was to identify whether the frequency of traffic conflicts at signalized intersections can be modeled. The opposing left-turn conflicts were selected for the development of conflict predictive models. Using data collected at 30 approaches at 20 signalized intersections, the underlying distributions of the conflicts under different traffic conditions were examined. Different conflict-predictive models were developed to relate the frequency of opposing left-turn conflicts to various explanatory variables. The models considered include a linear regression model, a negative binomial model, and separate models developed for four traffic scenarios. The prediction performance of different models was compared. The frequency of traffic conflicts follows a negative binominal distribution. The linear regression model is not appropriate for the conflict frequency data. In addition, drivers behaved differently under different traffic conditions. Accordingly, the effects of conflicting traffic volumes on conflict frequency vary across different traffic conditions. The occurrences of traffic conflicts at signalized intersections can be modeled using generalized linear regression models. The use of conflict predictive models has potential to expand the uses of surrogate safety measures in safety estimation and evaluation.

How is the Ideal Gas Law Explanatory?

NASA Astrophysics Data System (ADS)

Woody, Andrea I.

2013-07-01

Using the ideal gas law as a comparative example, this essay reviews contemporary research in philosophy of science concerning scientific explanation. It outlines the inferential, causal, unification, and erotetic conceptions of explanation and discusses an alternative project, the functional perspective. In each case, the aim is to highlight insights from these investigations that are salient for pedagogical concerns. Perhaps most importantly, this essay argues that science teachers should be mindful of the normative and prescriptive components of explanatory discourse both in the classroom and in science more generally. Giving attention to this dimension of explanation not only will do justice to the nature of explanatory activity in science but also will support the development of robust reasoning skills in science students while helping them understand an important respect in which science is more than a straightforward collection of empirical facts, and consequently, science education involves more than simply learning them.

A FORTRAN program for multivariate survival analysis on the personal computer.

PubMed

Mulder, P G

1988-01-01

In this paper a FORTRAN program is presented for multivariate survival or life table regression analysis in a competing risks' situation. The relevant failure rate (for example, a particular disease or mortality rate) is modelled as a log-linear function of a vector of (possibly time-dependent) explanatory variables. The explanatory variables may also include the variable time itself, which is useful for parameterizing piecewise exponential time-to-failure distributions in a Gompertz-like or Weibull-like way as a more efficient alternative to Cox's proportional hazards model. Maximum likelihood estimates of the coefficients of the log-linear relationship are obtained from the iterative Newton-Raphson method. The program runs on a personal computer under DOS; running time is quite acceptable, even for large samples.

Prevalence and predictors for musculoskeletal discomfort in Malaysian office workers: Investigating explanatory factors for a developing country.

PubMed

Maakip, Ismail; Keegel, Tessa; Oakman, Jodi

2016-03-01

Musculoskeletal disorders (MSDs) are a major occupational health issue for workers in developed and developing countries, including Malaysia. Most research related to MSDs has been undertaken in developed countries; given the different regulatory and cultural practices it is plausible that contributions of hazard and risk factors may be different. A population of Malaysian public service office workers were surveyed (N = 417, 65.5% response rate) to determine prevalence and associated predictors of MSD discomfort. The 6-month period prevalence of MSD discomfort was 92.8% (95%CI = 90.2-95.2%). Akaike's Information Criterion (AIC) analyses was used to compare a range of models and determine a model of best fit. Contributions associated with MSD discomfort in the final model consisted of physical demands (61%), workload (14%), gender (13%), work-home balance (9%) and psychosocial factors (3%). Factors associated with MSD discomfort were similar in developed and developing countries but the relative contribution of factors was different, providing insight into future development of risk management strategies. Copyright © 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Linguistics, human communication and psychiatry.

PubMed

Thomas, P; Fraser, W

1994-11-01

Psycholinguistics and sociolinguistics have extended our understanding of the abnormal communication seen in psychosis, as well as that of people with autism and Asperger's syndrome. Psycholinguistics has the potential to increase the explanatory power of cognitive and neuropsychological approaches to psychosis and new methods of assessment and therapy are now being developed, based on linguistic theory. A MEDLINE literature search was used. Of 205 relevant articles identified, 65 were selected for review. Greater familiarity with linguistic theory could improve psychiatrists' assessment skills and their understanding of the relevance of human communication to the new cognitive models of psychosis.

unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance

USGS Publications Warehouse

Fiske, Ian J.; Chandler, Richard B.

2011-01-01

Ecological research uses data collection techniques that are prone to substantial and unique types of measurement error to address scientific questions about species abundance and distribution. These data collection schemes include a number of survey methods in which unmarked individuals are counted, or determined to be present, at spatially- referenced sites. Examples include site occupancy sampling, repeated counts, distance sampling, removal sampling, and double observer sampling. To appropriately analyze these data, hierarchical models have been developed to separately model explanatory variables of both a latent abundance or occurrence process and a conditional detection process. Because these models have a straightforward interpretation paralleling mechanisms under which the data arose, they have recently gained immense popularity. The common hierarchical structure of these models is well-suited for a unified modeling interface. The R package unmarked provides such a unified modeling framework, including tools for data exploration, model fitting, model criticism, post-hoc analysis, and model comparison.

Freshwater Vulnerability to Nitrate Contamination as an Indicator of Sustainability and Resilience within the Water-Energy-Food Nexus of the California Coastal Basins

NASA Astrophysics Data System (ADS)

Nanus, L.; Geyer, G.; Gurdak, J. J.; Orencio, P. M.; Endo, A.; Taniguchi, M.

2014-12-01

The California Coastal Basin (CCB) aquifers are representative of many coastal aquifers that are vulnerable to nonpoint-source (NPS) contamination from intense agriculture and increased urbanization combined with historical groundwater use and overdraft conditions. Overdraft has led to seawater intrusion along parts of the central California coast, which negatively affects food production because of high salinity concentrations in groundwater used for irrigation. Recent drought conditions in California have led to an increased need to further understand freshwater sustainability and resilience within the water-energy-food (WEF) nexus. Assessing the vulnerability of NPS contamination in groundwater provides valuable information for optimal resource management and policy. Vulnerability models of nitrate contamination in the CCB were developed as one of many indicators to evaluate risk in terms of susceptibility of the physical environment at local and regional scales. Multivariate logistic regression models were developed to predict the probability of NPS nitrate contamination in recently recharged groundwater and to identify significant explanatory variables as controlling factors in the CCB. Different factors were found to be significant in the sub-regions of the CCB and issues of scale are important. For example, land use is scale dependent because of the difference in land management practices between the CCB sub-regions. However, dissolved oxygen concentrations in groundwater, farm fertilizer, and soil thickness are scale invariant because they are significant both regionally and sub-regionally. Thus, the vulnerability models for the CCB show that different explanatory variables are scale invariant. This finding has important implications for accurately quantifying linkages between vulnerability and consequences within the WEF nexus, including inherent tradeoffs in water and food production in California and associated impacts on the local and regional economy, governance, environment, and society at multiple scales.

Optical Properties of Three Beach Waters: Implications for Predictive Modeling of Enterococci

EPA Science Inventory

Sunlight plays an important role in the inactivation of fecal indicator bacteria in recreational waters. Solar radiation can explain temporal trends in bacterial counts and is commonly used as an explanatory variable in predictive models. Broadband surface radiation provides a ba...

Determinants of urban sprawl in European cities

PubMed Central

Alvanides, Seraphim; Garrod, Guy

2015-01-01

This paper provides empirical evidence that helps to answer several key questions relating to the extent of urban sprawl in Europe. Building on the monocentric city model, this study uses existing data sources to derive a set of panel data for 282 European cities at three time points (1990, 2000 and 2006). Two indices of urban sprawl are calculated that, respectively, reflect changes in artificial area and the levels of urban fragmentation for each city. These are supplemented by a set of data on various economic and geographical variables that might explain the variation of the two indices. Using a Hausman-Taylor estimator and random regressors to control for the possible correlation between explanatory variables and unobservable city-level effects, we find that the fundamental conclusions of the standard monocentric model are valid in the European context for both indices. Although the variables generated by the monocentric model explain a large part of the variation of artificial area, their explanatory power for modelling the fragmentation index is relatively low. PMID:26321770

Determinants of urban sprawl in European cities.

PubMed

Oueslati, Walid; Alvanides, Seraphim; Garrod, Guy

2015-07-01

This paper provides empirical evidence that helps to answer several key questions relating to the extent of urban sprawl in Europe. Building on the monocentric city model, this study uses existing data sources to derive a set of panel data for 282 European cities at three time points (1990, 2000 and 2006). Two indices of urban sprawl are calculated that, respectively, reflect changes in artificial area and the levels of urban fragmentation for each city. These are supplemented by a set of data on various economic and geographical variables that might explain the variation of the two indices. Using a Hausman-Taylor estimator and random regressors to control for the possible correlation between explanatory variables and unobservable city-level effects, we find that the fundamental conclusions of the standard monocentric model are valid in the European context for both indices. Although the variables generated by the monocentric model explain a large part of the variation of artificial area, their explanatory power for modelling the fragmentation index is relatively low.

Improving the physiological realism of experimental models

PubMed Central

Vinnakota, Kalyan C.; Cha, Chae Y.; Rorsman, Patrik; Balaban, Robert S.; La Gerche, Andre; Wade-Martins, Richard; Beard, Daniel A.

2016-01-01

The Virtual Physiological Human (VPH) project aims to develop integrative, explanatory and predictive computational models (C-Models) as numerical investigational tools to study disease, identify and design effective therapies and provide an in silico platform for drug screening. Ultimately, these models rely on the analysis and integration of experimental data. As such, the success of VPH depends on the availability of physiologically realistic experimental models (E-Models) of human organ function that can be parametrized to test the numerical models. Here, the current state of suitable E-models, ranging from in vitro non-human cell organelles to in vivo human organ systems, is discussed. Specifically, challenges and recent progress in improving the physiological realism of E-models that may benefit the VPH project are highlighted and discussed using examples from the field of research on cardiovascular disease, musculoskeletal disorders, diabetes and Parkinson's disease. PMID:27051507

Cost Efficiency in the University: A Departmental Evaluation Model

ERIC Educational Resources Information Center

Gimenez, Victor M.; Martinez, Jose Luis

2006-01-01

This article presents a model for the analysis of cost efficiency within the framework of data envelopment analysis models. It calculates the cost excess, separating a unit of production from its optimal or frontier levels, and, at the same time, breaks these excesses down into three explanatory factors: (a) technical inefficiency, which depends…

Identifying Multiple Levels of Discussion-Based Teaching Strategies for Constructing Scientific Models

ERIC Educational Resources Information Center

Williams, Grant; Clement, John

2015-01-01

This study sought to identify specific types of discussion-based strategies that two successful high school physics teachers using a model-based approach utilized in attempting to foster students' construction of explanatory models for scientific concepts. We found evidence that, in addition to previously documented dialogical strategies that…

Family Interaction Patterns Associated with Self-Esteem in Preadolescent Girls and Boys.

ERIC Educational Resources Information Center

Loeb, Roger C.; And Others

This study used behavioral measures of family interaction to examine four traditional explanatory models for the influence of parents on their children's self-esteem. The four models examined were: (1) identification/modeling, (2) directiveness, (3) reinforcement, and (4) warmth/involvement. A total of 98 fourth- and fifth-grade girls and boys…

Techniques for estimating flood-peak discharges of rural, unregulated streams in Ohio

USGS Publications Warehouse

Koltun, G.F.

2003-01-01

Regional equations for estimating 2-, 5-, 10-, 25-, 50-, 100-, and 500-year flood-peak discharges at ungaged sites on rural, unregulated streams in Ohio were developed by means of ordinary and generalized least-squares (GLS) regression techniques. One-variable, simple equations and three-variable, full-model equations were developed on the basis of selected basin characteristics and flood-frequency estimates determined for 305 streamflow-gaging stations in Ohio and adjacent states. The average standard errors of prediction ranged from about 39 to 49 percent for the simple equations, and from about 34 to 41 percent for the full-model equations. Flood-frequency estimates determined by means of log-Pearson Type III analyses are reported along with weighted flood-frequency estimates, computed as a function of the log-Pearson Type III estimates and the regression estimates. Values of explanatory variables used in the regression models were determined from digital spatial data sets by means of a geographic information system (GIS), with the exception of drainage area, which was determined by digitizing the area within basin boundaries manually delineated on topographic maps. Use of GIS-based explanatory variables represents a major departure in methodology from that described in previous reports on estimating flood-frequency characteristics of Ohio streams. Examples are presented illustrating application of the regression equations to ungaged sites on ungaged and gaged streams. A method is provided to adjust regression estimates for ungaged sites by use of weighted and regression estimates for a gaged site on the same stream. A region-of-influence method, which employs a computer program to estimate flood-frequency characteristics for ungaged sites based on data from gaged sites with similar characteristics, was also tested and compared to the GLS full-model equations. For all recurrence intervals, the GLS full-model equations had superior prediction accuracy relative to the simple equations and therefore are recommended for use.

An explanatory model of community pharmacists' support in the secondary prevention of cardiovascular disease.

PubMed

Puspitasari, Hanni P; Costa, Daniel S J; Aslani, Parisa; Krass, Ines

2016-01-01

Community pharmacists have faced ongoing challenges in the delivery of clinical pharmacy services. Various attitudinal and environmental factors have been found to be associated with the provision of general clinical pharmacy services or services which focus on a specific condition, including cardiovascular disease (CVD). However, the interrelationship and relative influence of explanatory factors has not been investigated. To develop a model illustrating influences on CVD support provision by community pharmacists. Mail surveys were sent to a random sample of 1350 Australian community pharmacies to investigate determinants of CVD support provision. A theoretical model modified from the Theory of Planned Behavior (TPB) was used as a framework for the survey instrument. Structural equation modeling was used to determine how pharmacists' attitudes and environmental factors influence CVD support. A response rate of 15.8% (209/1320) was obtained. The model for CVD support provision by community pharmacists demonstrated good fit: χ(2)/df = 1.403, RMSEA = 0.047 (90% CI = 0.031-0.062), CFI = 0.962, TLI = 0.955 and WRMR = 0.838. Factors found to predict CVD support included: two attitudinal latent factors ("subjective norms of pharmacists' role in CVD support" and "pharmacists' perceived responsibilities in CVD support") and environmental factors i.e. pharmacy infrastructure (documentation and a private area), workload, location; government funded pharmacy practice programs; and pharmacists' involvement with Continuing Professional Development and attendance at CVD courses. Pharmacists' attitudes appeared to be the strongest predictor of CVD support provision. The TPB framework was useful in identifying "subjective norms" and "pharmacists' beliefs" as key constructs of community pharmacists' attitudes. Community pharmacies would be able to provide such an advanced clinical service if they strongly believed that this was an acknowledged part of their scope of practice, had adequate infrastructure and employed sufficient numbers of pharmacists with appropriate and relevant knowledge. Copyright © 2016 Elsevier Inc. All rights reserved.

Unravelling the spirits’ message: a study of help-seeking steps and explanatory models among patients suffering from spirit possession in Uganda

PubMed Central

2014-01-01

As in many cultures, also in Uganda spirit possession is a common idiom of distress associated with traumatic experiences. In the DSM-IV and -5, possession trance disorders can be classified as dissociative disorders. Dissociation in Western countries is associated with complicated, time-consuming and costly therapies. Patients with spirit possession in SW Uganda, however, often report partial or full recovery after treatment by traditional healers. The aim of this study is to explore how the development of symptoms concomitant help-seeking steps, and explanatory models (EM) eventually contributed to healing of patients with spirit possession in SW Uganda. Illness narratives of 119 patients with spirit possession referred by traditional healers were analysed using a mixed-method approach. Treatments of two-thirds of the patients were unsuccessful when first seeking help in the medical sector. Their initially physical symptoms subsequently developed into dissociative possession symptoms. After an average of two help-seeking steps, patients reached a healing place where 99% of them found satisfactory EM and effective healing. During healing sessions, possessing agents were summoned to identify themselves and underlying problems were addressed. Often-mentioned explanations were the following: neglect of rituals and of responsibilities towards relatives and inheritance, the call to become a healer, witchcraft, grief, and land conflicts. The results demonstrate that traditional healing processes of spirit possession can play a role in restoring connections with the supra-, inter-, intra-, and extra-human worlds. It does not always seem necessary to address individual traumatic experiences per se, which is in line with other research in this field. The study leads to additional perspectives on treatment of trauma-related dissociation in Western countries and on developing effective mental health services in low -and middle-income countries. PMID:24940355

Unravelling the spirits' message: a study of help-seeking steps and explanatory models among patients suffering from spirit possession in Uganda.

PubMed

van Duijl, Marjolein; Kleijn, Wim; de Jong, Joop

2014-01-01

As in many cultures, also in Uganda spirit possession is a common idiom of distress associated with traumatic experiences. In the DSM-IV and -5, possession trance disorders can be classified as dissociative disorders. Dissociation in Western countries is associated with complicated, time-consuming and costly therapies. Patients with spirit possession in SW Uganda, however, often report partial or full recovery after treatment by traditional healers. The aim of this study is to explore how the development of symptoms concomitant help-seeking steps, and explanatory models (EM) eventually contributed to healing of patients with spirit possession in SW Uganda. Illness narratives of 119 patients with spirit possession referred by traditional healers were analysed using a mixed-method approach. Treatments of two-thirds of the patients were unsuccessful when first seeking help in the medical sector. Their initially physical symptoms subsequently developed into dissociative possession symptoms. After an average of two help-seeking steps, patients reached a healing place where 99% of them found satisfactory EM and effective healing. During healing sessions, possessing agents were summoned to identify themselves and underlying problems were addressed. Often-mentioned explanations were the following: neglect of rituals and of responsibilities towards relatives and inheritance, the call to become a healer, witchcraft, grief, and land conflicts. The results demonstrate that traditional healing processes of spirit possession can play a role in restoring connections with the supra-, inter-, intra-, and extra-human worlds. It does not always seem necessary to address individual traumatic experiences per se, which is in line with other research in this field. The study leads to additional perspectives on treatment of trauma-related dissociation in Western countries and on developing effective mental health services in low -and middle-income countries.

Physical activity participation in community dwelling stroke survivors: synergy and dissonance between motivation and capability. A qualitative study.

PubMed

Morris, Jacqui H; Oliver, Tracey; Kroll, Thilo; Joice, Sara; Williams, Brian

2017-09-01

The evidence supporting benefits of physical activity (PA) on fitness, functioning, health and secondary prevention after stroke is compelling. However, many stroke survivors remain insufficiently active. This study explored survivors' perspectives and experiences of PA participation to develop an explanatory framework that physiotherapists and other health professionals can use to develop person-specific strategies for PA promotion. Qualitative study using semi-structured in-depth interviews. Data was audio-recorded and transcribed. Analysis followed the Framework Approach. Community setting, interviews conducted within participants' homes. Community dwelling stroke survivors (n=38) six months or more after the end of their rehabilitation, purposively selected by disability, PA participation and socio-demographic status. Findings suggest that survivors' beliefs, attitudes, and physical and social context generated synergy or dissonance between motivation (desire to be active) and capability (resources to be active) for PA participation. Dissonance occurred when motivated survivors had limited capability for activity, often leading to frustration. Confidence to achieve goals and determination to overcome barriers, acted as activity catalysts when other influences were synergistic. We illustrate these relationships in a dynamic explanatory model that can be used to support both novel interventions and personal activity plans. This study suggests a shift is required from purely pragmatic approaches to PA promotion towards conceptual solutions. Understanding how synergy or dissonance between motivation and capability influence individual survivors' behaviour will support physiotherapists and other health professionals in promoting PA. This study provides a model for developing person-centred, tailored interventions that address barriers encountered by stroke survivors. Copyright © 2016 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.

Acute care patient portals: a qualitative study of stakeholder perspectives on current practices.

PubMed

Collins, Sarah A; Rozenblum, Ronen; Leung, Wai Yin; Morrison, Constance Rc; Stade, Diana L; McNally, Kelly; Bourie, Patricia Q; Massaro, Anthony; Bokser, Seth; Dwyer, Cindy; Greysen, Ryan S; Agarwal, Priyanka; Thornton, Kevin; Dalal, Anuj K

2017-04-01

To describe current practices and stakeholder perspectives of patient portals in the acute care setting. We aimed to: (1) identify key features, (2) recognize challenges, (3) understand current practices for design, configuration, and use, and (4) propose new directions for investigation and innovation. Mixed methods including surveys, interviews, focus groups, and site visits with stakeholders at leading academic medical centers. Thematic analyses to inform development of an explanatory model and recommendations. Site surveys were administered to 5 institutions. Thirty interviews/focus groups were conducted at 4 site visits that included a total of 84 participants. Ten themes regarding content and functionality, engagement and culture, and access and security were identified, from which an explanatory model of current practices was developed. Key features included clinical data, messaging, glossary, patient education, patient personalization and family engagement tools, and tiered displays. Four actionable recommendations were identified by group consensus. Design, development, and implementation of acute care patient portals should consider: (1) providing a single integrated experience across care settings, (2) humanizing the patient-clinician relationship via personalization tools, (3) providing equitable access, and (4) creating a clear organizational mission and strategy to achieve outcomes of interest. Portals should provide a single integrated experience across the inpatient and ambulatory settings. Core functionality includes tools that facilitate communication, personalize the patient, and deliver education to advance safe, coordinated, and dignified patient-centered care. Our findings can be used to inform a "road map" for future work related to acute care patient portals. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Anxiety psychopathology in African American adults: literature review and development of an empirically informed sociocultural model.

PubMed

Hunter, Lora Rose; Schmidt, Norman B

2010-03-01

In this review, the extant literature concerning anxiety psychopathology in African American adults is summarized to develop a testable, explanatory framework with implications for future research. The model was designed to account for purported lower rates of anxiety disorders in African Americans compared to European Americans, along with other ethnoracial differences reported in the literature. Three specific beliefs or attitudes related to the sociocultural experience of African Americans are identified: awareness of racism, stigma of mental illness, and salience of physical illnesses. In our model, we propose that these psychological processes influence interpretations and behaviors relevant to the expression of nonpathological anxiety as well as features of diagnosable anxiety conditions. Moreover, differences in these processes may explain the differential assessed rates of anxiety disorders in African Americans. The model is discussed in the context of existing models of anxiety etiology. Specific follow-up research is also suggested, along with implications for clinical assessment, diagnosis, and treatment.

Business model framework applications in health care: A systematic review.

PubMed

Fredriksson, Jens Jacob; Mazzocato, Pamela; Muhammed, Rafiq; Savage, Carl

2017-11-01

It has proven to be a challenge for health care organizations to achieve the Triple Aim. In the business literature, business model frameworks have been used to understand how organizations are aligned to achieve their goals. We conducted a systematic literature review with an explanatory synthesis approach to understand how business model frameworks have been applied in health care. We found a large increase in applications of business model frameworks during the last decade. E-health was the most common context of application. We identified six applications of business model frameworks: business model description, financial assessment, classification based on pre-defined typologies, business model analysis, development, and evaluation. Our synthesis suggests that the choice of business model framework and constituent elements should be informed by the intent and context of application. We see a need for harmonization in the choice of elements in order to increase generalizability, simplify application, and help organizations realize the Triple Aim.

Considering lesbian identity from a social-psychological perspective: two different models of "being a lesbian".

PubMed

Tate, Charlotte Chuck

2012-01-01

One long-standing project within lesbian studies has been to develop a satisfactory working definition of "lesbian." This article proposes two new models of a definition using principles of social psychology. Each model (a) utilizes the premise that gender lacks a categorical essence and (b) separates behavioral adherence to cultural stereotypes of femininity and masculinity from one's gender self-categorization. From these premises, I generate and critique two internally coherent models of lesbian identity that are inclusive (to different degrees) of various gender identities. For each model, the potential inclusion of trans men, trans women, genderqueers, and lesbian-identified cisgender men is evaluated. The explanatory power of these models is twofold. One, the models can serve as theoretical perspectives for scholars who study the intersection of gender and sexual identity. Two, the models can also characterize the everyday experience of people who have tacit working definitions of lesbian identity.

Predicting daily use of urban forest recreation sites

Treesearch

John F. Dwyer

1988-01-01

A multiple linear regression model explains 90% of the variance in daily use of an urban recreation site. Explanatory variables include season, day of the week, and weather. The results offer guides for recreation site planning and management as well as suggestions for improving the model.

The use of cognitive ability measures as explanatory variables in regression analysis

PubMed Central

Junker, Brian; Schofield, Lynne Steuerle; Taylor, Lowell J

2015-01-01

Cognitive ability measures are often taken as explanatory variables in regression analysis, e.g., as a factor affecting a market outcome such as an individual’s wage, or a decision such as an individual’s education acquisition. Cognitive ability is a latent construct; its true value is unobserved. Nonetheless, researchers often assume that a test score, constructed via standard psychometric practice from individuals’ responses to test items, can be safely used in regression analysis. We examine problems that can arise, and suggest that an alternative approach, a “mixed effects structural equations” (MESE) model, may be more appropriate in many circumstances. PMID:26998417

Anthropogenic Habitats Facilitate Dispersal of an Early Successional Obligate: Implications for Restoration of an Endangered Ecosystem.

PubMed

Amaral, Katrina E; Palace, Michael; O'Brien, Kathleen M; Fenderson, Lindsey E; Kovach, Adrienne I

2016-01-01

Landscape modification and habitat fragmentation disrupt the connectivity of natural landscapes, with major consequences for biodiversity. Species that require patchily distributed habitats, such as those that specialize on early successional ecosystems, must disperse through a landscape matrix with unsuitable habitat types. We evaluated landscape effects on dispersal of an early successional obligate, the New England cottontail (Sylvilagus transitionalis). Using a landscape genetics approach, we identified barriers and facilitators of gene flow and connectivity corridors for a population of cottontails in the northeastern United States. We modeled dispersal in relation to landscape structure and composition and tested hypotheses about the influence of habitat fragmentation on gene flow. Anthropogenic and natural shrubland habitats facilitated gene flow, while the remainder of the matrix, particularly development and forest, impeded gene flow. The relative influence of matrix habitats differed between study areas in relation to a fragmentation gradient. Barrier features had higher explanatory power in the more fragmented site, while facilitating features were important in the less fragmented site. Landscape models that included a simultaneous barrier and facilitating effect of roads had higher explanatory power than models that considered either effect separately, supporting the hypothesis that roads act as both barriers and facilitators at all spatial scales. The inclusion of LiDAR-identified shrubland habitat improved the fit of our facilitator models. Corridor analyses using circuit and least cost path approaches revealed the importance of anthropogenic, linear features for restoring connectivity between the study areas. In fragmented landscapes, human-modified habitats may enhance functional connectivity by providing suitable dispersal conduits for early successional specialists.

Anthropogenic Habitats Facilitate Dispersal of an Early Successional Obligate: Implications for Restoration of an Endangered Ecosystem

PubMed Central

Amaral, Katrina E.; Palace, Michael; O’Brien, Kathleen M.; Fenderson, Lindsey E.; Kovach, Adrienne I.

2016-01-01

Landscape modification and habitat fragmentation disrupt the connectivity of natural landscapes, with major consequences for biodiversity. Species that require patchily distributed habitats, such as those that specialize on early successional ecosystems, must disperse through a landscape matrix with unsuitable habitat types. We evaluated landscape effects on dispersal of an early successional obligate, the New England cottontail (Sylvilagus transitionalis). Using a landscape genetics approach, we identified barriers and facilitators of gene flow and connectivity corridors for a population of cottontails in the northeastern United States. We modeled dispersal in relation to landscape structure and composition and tested hypotheses about the influence of habitat fragmentation on gene flow. Anthropogenic and natural shrubland habitats facilitated gene flow, while the remainder of the matrix, particularly development and forest, impeded gene flow. The relative influence of matrix habitats differed between study areas in relation to a fragmentation gradient. Barrier features had higher explanatory power in the more fragmented site, while facilitating features were important in the less fragmented site. Landscape models that included a simultaneous barrier and facilitating effect of roads had higher explanatory power than models that considered either effect separately, supporting the hypothesis that roads act as both barriers and facilitators at all spatial scales. The inclusion of LiDAR-identified shrubland habitat improved the fit of our facilitator models. Corridor analyses using circuit and least cost path approaches revealed the importance of anthropogenic, linear features for restoring connectivity between the study areas. In fragmented landscapes, human-modified habitats may enhance functional connectivity by providing suitable dispersal conduits for early successional specialists. PMID:26954014

Combined risk assessment of nonstationary monthly water quality based on Markov chain and time-varying copula.

PubMed

Shi, Wei; Xia, Jun

2017-02-01

Water quality risk management is a global hot research linkage with the sustainable water resource development. Ammonium nitrogen (NH 3 -N) and permanganate index (COD Mn ) as the focus indicators in Huai River Basin, are selected to reveal their joint transition laws based on Markov theory. The time-varying moments model with either time or land cover index as explanatory variables is applied to build the time-varying marginal distributions of water quality time series. Time-varying copula model, which takes the non-stationarity in the marginal distribution and/or the time variation in dependence structure between water quality series into consideration, is constructed to describe a bivariate frequency analysis for NH 3 -N and COD Mn series at the same monitoring gauge. The larger first-order Markov joint transition probability indicates water quality state Class V w , Class IV and Class III will occur easily in the water body of Bengbu Sluice. Both marginal distribution and copula models are nonstationary, and the explanatory variable time yields better performance than land cover index in describing the non-stationarities in the marginal distributions. In modelling the dependence structure changes, time-varying copula has a better fitting performance than the copula with the constant or the time-trend dependence parameter. The largest synchronous encounter risk probability of NH 3 -N and COD Mn simultaneously reaching Class V is 50.61%, while the asynchronous encounter risk probability is largest when NH 3 -N and COD Mn is inferior to class V and class IV water quality standards, respectively.

Airfreight forecasting methodology and results

NASA Technical Reports Server (NTRS)

1978-01-01

A series of econometric behavioral equations was developed to explain and forecast the evolution of airfreight traffic demand for the total U.S. domestic airfreight system, the total U.S. international airfreight system, and the total scheduled international cargo traffic carried by the top 44 foreign airlines. The basic explanatory variables used in these macromodels were the real gross national products of the countries involved and a measure of relative transportation costs. The results of the econometric analysis reveal that the models explain more than 99 percent of the historical evolution of freight traffic. The long term traffic forecasts generated with these models are based on scenarios of the likely economic outlook in the United States and 31 major foreign countries.

An Update on Statistical Boosting in Biomedicine.

PubMed

Mayr, Andreas; Hofner, Benjamin; Waldmann, Elisabeth; Hepp, Tobias; Meyer, Sebastian; Gefeller, Olaf

2017-01-01

Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates. They are extremely flexible, as the underlying base-learners (regression functions defining the type of effect for the explanatory variables) can be combined with any kind of loss function (target function to be optimized, defining the type of regression setting). In this review article, we highlight the most recent methodological developments on statistical boosting regarding variable selection, functional regression, and advanced time-to-event modelling. Additionally, we provide a short overview on relevant applications of statistical boosting in biomedicine.

A longitudinal model of the dynamics between HMOs' consumer-friendliness and preventive health care utilization.

PubMed

Xiao, Qian; Savage, Grant T; Zhuang, Weiling

2014-01-01

This study aims at replicating and extending Xiao and Savage's (2008) research to understand the multidimensional aspect of HMOs distinguished by HMOs' consumer-friendliness, and their relationship to consumers' preventive care utilization. This study develops a dynamic model to consider both concurrent and time lagging effects of HMOs' consumer-friendliness. Our data analysis discloses similar relationship patterns as revealed by Xiao and Savage. Additionally, our findings reveal the time-series changes of the influence of HMOs' consumer-friendliness that either the effects of early experienced HMOs' consumer-friendliness wear out totally or HMOs' consumer-friendly characteristics on the concurrent term contain most of the explanatory power.

Reproductive practices by patterns of marriage among Iranian women: study protocol for an explanatory sequential mixed methods design.

PubMed

Taghizadeh, Ziba; Vedadhir, Abouali; Behmanesh, Fereshteh; Ebadi, Abbas; Pourreza, Abulghasem; Abbasi-Shavazi, Mohammad Jalal

2015-09-18

Nowadays, nearly half of the world population lives in societies with low fertility or the below-replacement fertility. This potentially grounds the critical situation of reduction in the workforce and causes the aging of population due to an overall increase in life expectancy and standard of living. Hence, population and its transitions including the issue of fertility decline has become a topic of intense debate in the agenda-setting and policy-making processes in both the developed and developing countries. In this view, what can practically be done to respond to the fertility decline that entails effectively addressing the determinants of fertility change? In line with the literature, how people form their marriages or patterns of marriage is amongst influencing factors which potentially affect their reproductive practices as diverse societies recognize different conventions for marriage. This study is to examine women's reproductive practices by the various patterns of marriage using the explanatory sequential mixed methods design. This study has an explanatory sequential mixed methods design, the follow-up explanations variant model, with two strands. This design will be implemented in two distinct phases. In the first phase, a cross-sectional quantitative study will be done using a cluster sampling strategy on 850 married women 15-49 years old living in Babol city, Iran. In order to obtain a deeper understanding of the results of the quantitative phase, researchers will implement a qualitative research in the second phase of this study. This design will provide an explanation of the quantitative research results using the qualitative evidence. As patterns of marriage have implications for the status of women, their health and fertility, the result of this study can provide a rich source of information for the required health-related interventions and policies are required to put the demographic changes on the right track at micro and macro level and improve the reproductive practices of women at micro level.

Case formulation and management using pattern-based formulation (PBF) methodology: clinical case 1.

PubMed

Fernando, Irosh; Cohen, Martin

2014-02-01

A tool for psychiatric case formulation known as pattern-based formulation (PBF) has been recently introduced. This paper presents an application of this methodology in formulating and managing complex clinical cases. The symptomatology of the clinical presentation has been parsed into individual clinical phenomena and interpreted by selecting explanatory models. The clinical presentation demonstrates how PBF has been used as a clinical tool to guide clinicians' thinking, that takes a structured approach to manage multiple issues using a broad range of management strategies. In doing so, the paper also introduces a number of patterns related to the observed clinical phenomena that can be re-used as explanatory models when formulating other clinical cases. It is expected that this paper will assist clinicians, and particularly trainees, to better understand PBF methodology and apply it to improve their formulation skills.

Insight in Psychosis: An Indicator of Severity of Psychosis, an Explanatory Model of Illness, and a Coping Strategy

PubMed Central

Jacob, K. S.

2016-01-01

Recent studies related to insight, explanatory models (EMs) of illness and their relationship to outcome of psychosis are reviewed. The traditional argument that insight predicts outcome in psychosis is not supported by recent longitudinal data, which has been analyzed using multivariable statistics that adjust for severity and quality of illness. While all cognition will have a neurobiological representation, if “insight” is related to the primary psychotic process, then insight cannot be seen as an independent predictor of outcome but a part of the progression of illness. The evidence suggests insight, like all EMs, is belief which interacts with the trajectory of the person's illness and the local culture to produce a unique understanding of the illness for the particular individual and his/her family. PMID:27335513

Explanatory model of psychosis: impact on perception of self-stigma by patients in three sub-saharan African cities.

PubMed

Makanjuola, Victor; Esan, Yomi; Oladeji, Bibilola; Kola, Lola; Appiah-Poku, John; Harris, Benjamin; Othieno, Caleb; Price, Leshawndra; Seedat, Soraya; Gureje, Oye

2016-12-01

Most cultures in sub-Saharan Africa subscribe to the belief that the root cause of psychosis is supernatural. Individuals in the community who hold a religiomagical explanatory model of causation have been shown to exhibit more stigmatizing attitudes towards people with psychosis. Self-stigma among individuals with psychosis is less frequently studied. We used a mixed-method approach, consisting of key informant's interviews to elicit information on explanatory models of causation of psychosis and questionnaire assessment of internalized stigma with an adapted version of the Scale for Internalized Stigma of Mental Illness. Twenty-four, 31, and 30 subjects with recent experience of utilizing the service of traditional or faith healers for severe mental disorders in Ibadan (Nigeria), Kumasi (Ghana), and Nairobi (Kenya), respectively, were interviewed. About 44 % (42.1 %) of the Nigerian respondents had a high (severe) level of self-stigma with the respective proportions among Ghanaian and Kenyan respondents being 20.7 and 37.5 %. Compared with 4 out of a total of 12 respondents (33.3 %) who reported low self-stigma reported supernatural attribution, 14 out of 20 respondents (70 %) with the highest level of self-stigma reported supernatural attribution across the three sites. When low scorers ascribed supernatural causation, it was often with a religious focus. There is a greater tendency for persons with high levels of self-stigma than those with low levels to ascribe supernatural attribution to their experience of a severe mental health condition.

Quantified biotic and abiotic responses to multiple stress in freshwater, marine and ground waters.

PubMed

Nõges, Peeter; Argillier, Christine; Borja, Ángel; Garmendia, Joxe Mikel; Hanganu, Jenică; Kodeš, Vit; Pletterbauer, Florian; Sagouis, Alban; Birk, Sebastian

2016-01-01

We reviewed 219 papers and built an inventory of 532 items of ecological evidence on multiple stressor impacts in rivers, lakes, transitional and coastal waters, as well as groundwaters. Our review revealed that, despite the existence of a huge conceptual knowledge base in aquatic ecology, few studies actually provide quantitative evidence on multi-stress effects. Nutrient stress was involved in 71% to 98% of multi-stress situations in the three types of surface water environments, and in 42% of those in groundwaters. However, their impact manifested differently along the groundwater-river-lake-transitional-coastal continuum, mainly determined by the different hydro-morphological features of these ecosystems. The reviewed papers addressed two-stressor combinations most frequently (42%), corresponding with the actual status-quo of pressures acting on European surface waters as reported by the Member States in the WISE WFD Database (EEA, 2015). Across all biological groups analysed, higher explanatory power of the stress-effect models was discernible for lakes under multi-stressor compared to single stressor conditions, but generally lower for coastal and transitional waters. Across all aquatic environments, the explanatory power of stress-effect models for fish increased when multi-stressor conditions were taken into account in the analysis, qualifying this organism group as a useful indicator of multi-stress effects. In contrast, the explanatory power of models using benthic flora decreased under conditions of multiple stress. Copyright © 2015 Elsevier B.V. All rights reserved.

Analyzing Student Learning Outcomes: Usefulness of Logistic and Cox Regression Models. IR Applications, Volume 5

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2005-01-01

Logistic and Cox regression methods are practical tools used to model the relationships between certain student learning outcomes and their relevant explanatory variables. The logistic regression model fits an S-shaped curve into a binary outcome with data points of zero and one. The Cox regression model allows investigators to study the duration…

Analogical Scaffolding and the Learning of Abstract Ideas in Physics: An Example from Electromagnetic Waves

ERIC Educational Resources Information Center

Podolefsky, Noah S.; Finkelstein, Noah D.

2007-01-01

This paper describes a model of analogy, analogical scaffolding, which explains present and prior results of student learning with analogies. We build on prior models of representation, blending, and layering of ideas. Extending this model's explanatory power, we propose ways in which the model can be applied to design a curriculum directed at…

The Health Belief Model as an Explanatory Framework in Communication Research: Exploring Parallel, Serial, and Moderated Mediation

PubMed Central

Jones, Christina L.; Jensen, Jakob D.; Scherr, Courtney L.; Brown, Natasha R.; Christy, Katheryn; Weaver, Jeremy

2015-01-01

The Health Belief Model (HBM) posits that messages will achieve optimal behavior change if they successfully target perceived barriers, benefits, self-efficacy, and threat. While the model seems to be an ideal explanatory framework for communication research, theoretical limitations have limited its use in the field. Notably, variable ordering is currently undefined in the HBM. Thus, it is unclear whether constructs mediate relationships comparably (parallel mediation), in sequence (serial mediation), or in tandem with a moderator (moderated mediation). To investigate variable ordering, adults (N = 1,377) completed a survey in the aftermath of an 8-month flu vaccine campaign grounded in the HBM. Exposure to the campaign was positively related to vaccination behavior. Statistical evaluation supported a model where the indirect effect of exposure on behavior through perceived barriers and threat was moderated by self-efficacy (moderated mediation). Perceived barriers and benefits also formed a serial mediation chain. The results indicate that variable ordering in the Health Belief Model may be complex, may help to explain conflicting results of the past, and may be a good focus for future research. PMID:25010519

Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure

PubMed Central

Craig, Marlies H; Sharp, Brian L; Mabaso, Musawenkosi LH; Kleinschmidt, Immo

2007-01-01

Background Several malaria risk maps have been developed in recent years, many from the prevalence of infection data collated by the MARA (Mapping Malaria Risk in Africa) project, and using various environmental data sets as predictors. Variable selection is a major obstacle due to analytical problems caused by over-fitting, confounding and non-independence in the data. Testing and comparing every combination of explanatory variables in a Bayesian spatial framework remains unfeasible for most researchers. The aim of this study was to develop a malaria risk map using a systematic and practicable variable selection process for spatial analysis and mapping of historical malaria risk in Botswana. Results Of 50 potential explanatory variables from eight environmental data themes, 42 were significantly associated with malaria prevalence in univariate logistic regression and were ranked by the Akaike Information Criterion. Those correlated with higher-ranking relatives of the same environmental theme, were temporarily excluded. The remaining 14 candidates were ranked by selection frequency after running automated step-wise selection procedures on 1000 bootstrap samples drawn from the data. A non-spatial multiple-variable model was developed through step-wise inclusion in order of selection frequency. Previously excluded variables were then re-evaluated for inclusion, using further step-wise bootstrap procedures, resulting in the exclusion of another variable. Finally a Bayesian geo-statistical model using Markov Chain Monte Carlo simulation was fitted to the data, resulting in a final model of three predictor variables, namely summer rainfall, mean annual temperature and altitude. Each was independently and significantly associated with malaria prevalence after allowing for spatial correlation. This model was used to predict malaria prevalence at unobserved locations, producing a smooth risk map for the whole country. Conclusion We have produced a highly plausible and parsimonious model of historical malaria risk for Botswana from point-referenced data from a 1961/2 prevalence survey of malaria infection in 1–14 year old children. After starting with a list of 50 potential variables we ended with three highly plausible predictors, by applying a systematic and repeatable staged variable selection procedure that included a spatial analysis, which has application for other environmentally determined infectious diseases. All this was accomplished using general-purpose statistical software. PMID:17892584

The Unintended Significance of Race: Environmental Racial Inequality in Detroit

ERIC Educational Resources Information Center

Downey, Liam

2005-01-01

This article addresses shortcomings in the literature on environmental inequality by (a) setting forth and testing four models of environmental inequality and (b) explicitly linking environmental inequality research to spatial mismatch theory and to the debate on the declining significance of race. The explanatory models ask whether the…

"We Decided to Call It Quits": An Exercise in Applying Duck's Dissolution Model to Students' Breakup Stories

ERIC Educational Resources Information Center

Barton, Matthew H.; Turman, Paul D.

2008-01-01

Steve Duck's scholarship has made a noticeable impact on the study of interpersonal communication. Of his works, one of the most noteworthy is the explanatory model of relationship disengagement and dissolution. Duck's Dissolution Model (DDM) explains the stages that relationships pass through when they encounter stress resulting in two…

A Reformulated Model of Barriers to Parental Involvement in Education: Comment on Hornby and Lafaele (2011)

ERIC Educational Resources Information Center

Fan, Weihua; Li, Nan; Sandoval, Jaime Robert

2018-01-01

In a 2011 article in this journal, Hornby and Lafaele provided a comprehensive model to understand barriers that may adversely impact effectiveness of parental involvement (PI) in education. The proposed explanatory model provides researchers with a new comprehensive and systematic perspective of the phenomenon in question with references from an…

EXPLANATORY MODELS OF HYPERTENSION AMONG NIGERIAN PATIENTS AT A UNIVERSITY TEACHING HOSPITAL

PubMed Central

Taylor, Kelly D.; Adedokun, Ayoade; Awobusuyi, Olugbenga; Adeniran, Peju; Onyia, Elochukwu; Ogedegbe, Gbenga

2013-01-01

Objective To elicit the explanatory models (EM) of hypertension among patients in a hospital-based primary care practice in Nigeria. Design Semi-structured in-depth individual interviews and focus groups were conducted with 62 hypertensive patients. Interviews and focus groups were audio-taped and transcribed verbatim. Data analysis was guided by phenomenology and content analysis using qualitative research software ATLAS.ti 5.0. Results Patients expressed four categories of EM of hypertension: 1) perceptions of hypertension, 2) consequences, 3) effect on daily life, and 4) perception of treatment. Focus group discussions and key informant interviews yielded a wide range of insights into the social and cultural factors influencing patients’ beliefs and health behavior. Participants were aware of the risks of hypertension. There was disagreement between participants’ own understanding of the serious nature of hypertension, the need for long-term treatment, and the desire to take medication long-term. Participants acknowledged the use of traditional medicine (e.g. teas and herbs) and healers. Different themes emerged for men versus women such that women often focused on family issues while men tended to discuss external stressors stemming from work as a cause of hypertension. Men were concerned with frequent urination, decreased libido and erectile dysfunction. Conclusion Knowledge gained will inform development of patient-centered treatment plans and targeted behavioral and educational interventions. PMID:23534506

Predictive QSAR modeling workflow, model applicability domains, and virtual screening.

PubMed

Tropsha, Alexander; Golbraikh, Alexander

2007-01-01

Quantitative Structure Activity Relationship (QSAR) modeling has been traditionally applied as an evaluative approach, i.e., with the focus on developing retrospective and explanatory models of existing data. Model extrapolation was considered if only in hypothetical sense in terms of potential modifications of known biologically active chemicals that could improve compounds' activity. This critical review re-examines the strategy and the output of the modern QSAR modeling approaches. We provide examples and arguments suggesting that current methodologies may afford robust and validated models capable of accurate prediction of compound properties for molecules not included in the training sets. We discuss a data-analytical modeling workflow developed in our laboratory that incorporates modules for combinatorial QSAR model development (i.e., using all possible binary combinations of available descriptor sets and statistical data modeling techniques), rigorous model validation, and virtual screening of available chemical databases to identify novel biologically active compounds. Our approach places particular emphasis on model validation as well as the need to define model applicability domains in the chemistry space. We present examples of studies where the application of rigorously validated QSAR models to virtual screening identified computational hits that were confirmed by subsequent experimental investigations. The emerging focus of QSAR modeling on target property forecasting brings it forward as predictive, as opposed to evaluative, modeling approach.

Distinguishing the cognitive processes of mindfulness: Developing a standardised mindfulness technique for use in longitudinal randomised control trials.

PubMed

Isbel, Ben; Summers, Mathew J

2017-07-01

A capacity model of mindfulness is adopted to differentiate the cognitive faculty of mindfulness from the metacognitive processes required to cultivate this faculty in mindfulness training. The model provides an explanatory framework incorporating both the developmental progression from focussed attention to open monitoring styles of mindfulness practice, along with the development of equanimity and insight. A standardised technique for activating these processes without the addition of secondary components is then introduced. Mindfulness-based interventions currently available for use in randomised control trials introduce components ancillary to the cognitive processes of mindfulness, limiting their ability to draw clear causative inferences. The standardised technique presented here does not introduce such ancillary factors, rendering it a valuable tool with which to investigate the processes activated in mindfulness practice. Copyright © 2017 Elsevier Inc. All rights reserved.

Static and dynamic controls on fire activity at moderate spatial and temporal scales in the Alaskan boreal forest

USGS Publications Warehouse

Barrett, Kirsten; Loboda, Tatiana; McGuire, A. David; Genet, Hélène; Hoy, Elizabeth; Kasischke, Eric

2016-01-01

Wildfire, a dominant disturbance in boreal forests, is highly variable in occurrence and behavior at multiple spatiotemporal scales. New data sets provide more detailed spatial and temporal observations of active fires and the post-burn environment in Alaska. In this study, we employ some of these new data to analyze variations in fire activity by developing three explanatory models to examine the occurrence of (1) seasonal periods of elevated fire activity using the number of MODIS active fire detections data set (MCD14DL) within an 11-day moving window, (2) unburned patches within a burned area using the Monitoring Trends in Burn Severity fire severity product, and (3) short-to-moderate interval (<60 yr) fires using areas of burned area overlap in the Alaska Large Fire Database. Explanatory variables for these three models included dynamic variables that can change over the course of the fire season, such as weather and burn date, as well as static variables that remain constant over a fire season, such as topography, drainage, vegetation cover, and fire history. We found that seasonal periods of high fire activity are associated with both seasonal timing and aggregated weather conditions, as well as the landscape composition of areas that are burning. Important static inputs to the model of seasonal fire activity indicate that when fire weather conditions are suitable, areas that typically resist fire (e.g., deciduous stands) may become more vulnerable to burning and therefore less effective as fire breaks. The occurrence of short-to-moderate interval fires appears to be primarily driven by weather conditions, as these were the only relevant explanatory variables in the model. The unique importance of weather in explaining short-to-moderate interval fires implies that fire return intervals (FRIs) will be sensitive to projected climate changes in the region. Unburned patches occur most often in younger stands, which may be related to a greater deciduous fraction of vegetation as well as lower fuel loads compared with mature stands. The fraction of unburned patches may therefore increase in response to decreasing FRIs and increased deciduousness in the region, or these may decrease if fire weather conditions become more severe.

Dynamical Systems Approaches to Emotional Development

ERIC Educational Resources Information Center

Camras, Linda A.; Witherington, David C.

2005-01-01

Within the last 20 years, transitions in the conceptualization of emotion and its development have given rise to calls for an explanatory framework that captures emotional development in all its organizational complexity and variability. Recent attempts have been made to couch emotional development in terms of a dynamical systems approach through…

Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations

USGS Publications Warehouse

Gerber, Brian D.; Kendall, William L.; Hooten, Mevin B.; Dubovsky, James A.; Drewien, Roderick C.

2015-01-01

Prediction is fundamental to scientific enquiry and application; however, ecologists tend to favour explanatory modelling. We discuss a predictive modelling framework to evaluate ecological hypotheses and to explore novel/unobserved environmental scenarios to assist conservation and management decision-makers. We apply this framework to develop an optimal predictive model for juvenile (<1 year old) sandhill crane Grus canadensis recruitment of the Rocky Mountain Population (RMP). We consider spatial climate predictors motivated by hypotheses of how drought across multiple time-scales and spring/summer weather affects recruitment.Our predictive modelling framework focuses on developing a single model that includes all relevant predictor variables, regardless of collinearity. This model is then optimized for prediction by controlling model complexity using a data-driven approach that marginalizes or removes irrelevant predictors from the model. Specifically, we highlight two approaches of statistical regularization, Bayesian least absolute shrinkage and selection operator (LASSO) and ridge regression.Our optimal predictive Bayesian LASSO and ridge regression models were similar and on average 37% superior in predictive accuracy to an explanatory modelling approach. Our predictive models confirmed a priori hypotheses that drought and cold summers negatively affect juvenile recruitment in the RMP. The effects of long-term drought can be alleviated by short-term wet spring–summer months; however, the alleviation of long-term drought has a much greater positive effect on juvenile recruitment. The number of freezing days and snowpack during the summer months can also negatively affect recruitment, while spring snowpack has a positive effect.Breeding habitat, mediated through climate, is a limiting factor on population growth of sandhill cranes in the RMP, which could become more limiting with a changing climate (i.e. increased drought). These effects are likely not unique to cranes. The alteration of hydrological patterns and water levels by drought may impact many migratory, wetland nesting birds in the Rocky Mountains and beyond.Generalizable predictive models (trained by out-of-sample fit and based on ecological hypotheses) are needed by conservation and management decision-makers. Statistical regularization improves predictions and provides a general framework for fitting models with a large number of predictors, even those with collinearity, to simultaneously identify an optimal predictive model while conducting rigorous Bayesian model selection. Our framework is important for understanding population dynamics under a changing climate and has direct applications for making harvest and habitat management decisions.

Army College Fund Cost-Effectiveness Study

DTIC Science & Technology

1990-11-01

Section A.2 presents a theory of enlistment supply to provide a basis for specifying the regression model , The model Is specified in Section A.3, which...Supplementary materials are included in the final four sections. Section A.6 provides annual trends in the regression model variables. Estimates of the model ...millions, A.S. ESTIMATION OF A YOUTH EARNINGS FORECASTING MODEL Civilian pay is an important explanatory variable in the regression model . Previous

The effect of a negative mood priming challenge on dysfunctional attitudes, explanatory style, and explanatory flexibility.

PubMed

Fresco, David M; Heimberg, Richard G; Abramowitz, Adrienne; Bertram, Tara L

2006-06-01

Ninety-seven undergraduates, 48 of whom had a history of self-reported major depression, completed measures of mood and cognitive style (e.g. explanatory style, explanatory flexibility, dysfunctional attitudes) prior to and directly after a negative mood priming challenge that consisted of listening to sad music and thinking about an upsetting past event. Eighteen of the previously depressed participants endorsed baseline levels of depression, explanatory style for negative events, and dysfunctional attitudes higher than levels reported by never depressed participants or euthymic participants with a history of depression. All three groups (never depressed participants, dysphoric participants with a history of depression, euthymic participants with a history of depression) demonstrated increases in dysphoria and dysfunctional attitudes in response to the negative mood priming challenge. Dysphoric participants with a history of depression, but not the other two groups, evidenced modest increases in explanatory style following the negative mood priming challenge. Finally, euthymic participants with a history of depression, but not the other two groups, evidenced drops in explanatory flexibility. Findings from the present study suggest that the cognitive theories of depression may benefit from examining both cognitive content and cognitive flexibility when assessing risk for depression.

A geospatial model of ambient sound pressure levels in the contiguous United States.

PubMed

Mennitt, Daniel; Sherrill, Kirk; Fristrup, Kurt

2014-05-01

This paper presents a model that predicts measured sound pressure levels using geospatial features such as topography, climate, hydrology, and anthropogenic activity. The model utilizes random forest, a tree-based machine learning algorithm, which does not incorporate a priori knowledge of source characteristics or propagation mechanics. The response data encompasses 270 000 h of acoustical measurements from 190 sites located in National Parks across the contiguous United States. The explanatory variables were derived from national geospatial data layers and cross validation procedures were used to evaluate model performance and identify variables with predictive power. Using the model, the effects of individual explanatory variables on sound pressure level were isolated and quantified to reveal systematic trends across environmental gradients. Model performance varies by the acoustical metric of interest; the seasonal L50 can be predicted with a median absolute deviation of approximately 3 dB. The primary application for this model is to generalize point measurements to maps expressing spatial variation in ambient sound levels. An example of this mapping capability is presented for Zion National Park and Cedar Breaks National Monument in southwestern Utah.

Introduction to the use of regression models in epidemiology.

PubMed

Bender, Ralf

2009-01-01

Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.

Monitoring and modeling to predict Escherichia coli at Presque Isle Beach 2, City of Erie, Erie County, Pennsylvania

USGS Publications Warehouse

Zimmerman, Tammy M.

2006-01-01

The Lake Erie shoreline in Pennsylvania spans nearly 40 miles and is a valuable recreational resource for Erie County. Nearly 7 miles of the Lake Erie shoreline lies within Presque Isle State Park in Erie, Pa. Concentrations of Escherichia coli (E. coli) bacteria at permitted Presque Isle beaches occasionally exceed the single-sample bathing-water standard, resulting in unsafe swimming conditions and closure of the beaches. E. coli concentrations and other water-quality and environmental data collected at Presque Isle Beach 2 during the 2004 and 2005 recreational seasons were used to develop models using tobit regression analyses to predict E. coli concentrations. All variables statistically related to E. coli concentrations were included in the initial regression analyses, and after several iterations, only those explanatory variables that made the models significantly better at predicting E. coli concentrations were included in the final models. Regression models were developed using data from 2004, 2005, and the combined 2-year dataset. Variables in the 2004 model and the combined 2004-2005 model were log10 turbidity, rain weight, wave height (calculated), and wind direction. Variables in the 2005 model were log10 turbidity and wind direction. Explanatory variables not included in the final models were water temperature, streamflow, wind speed, and current speed; model results indicated these variables did not meet significance criteria at the 95-percent confidence level (probabilities were greater than 0.05). The predicted E. coli concentrations produced by the models were used to develop probabilities that concentrations would exceed the single-sample bathing-water standard for E. coli of 235 colonies per 100 milliliters. Analysis of the exceedence probabilities helped determine a threshold probability for each model, chosen such that the correct number of exceedences and nonexceedences was maximized and the number of false positives and false negatives was minimized. Future samples with computed exceedence probabilities higher than the selected threshold probability, as determined by the model, will likely exceed the E. coli standard and a beach advisory or closing may need to be issued; computed exceedence probabilities lower than the threshold probability will likely indicate the standard will not be exceeded. Additional data collected each year can be used to test and possibly improve the model. This study will aid beach managers in more rapidly determining when waters are not safe for recreational use and, subsequently, when to issue beach advisories or closings.

A digital spatial predictive model of land-use change using economic and environmental inputs and a statistical tree classification approach: Thailand, 1970s--1990s

NASA Astrophysics Data System (ADS)

Felkner, John Sames

The scale and extent of global land use change is massive, and has potentially powerful effects on the global climate and global atmospheric composition (Turner & Meyer, 1994). Because of this tremendous change and impact, there is an urgent need for quantitative, empirical models of land use change, especially predictive models with an ability to capture the trajectories of change (Agarwal, Green, Grove, Evans, & Schweik, 2000; Lambin et al., 1999). For this research, a spatial statistical predictive model of land use change was created and run in two provinces of Thailand. The model utilized an extensive spatial database, and used a classification tree approach for explanatory model creation and future land use (Breiman, Friedman, Olshen, & Stone, 1984). Eight input variables were used, and the trees were run on a dependent variable of land use change measured from 1979 to 1989 using classified satellite imagery. The derived tree models were used to create probability of change surfaces, and these were then used to create predicted land cover maps for 1999. These predicted 1999 maps were compared with actual 1999 landcover derived from 1999 Landsat 7 imagery. The primary research hypothesis was that an explanatory model using both economic and environmental input variables would better predict future land use change than would either a model using only economic variables or a model using only environmental. Thus, the eight input variables included four economic and four environmental variables. The results indicated a very slight superiority of the full models to predict future agricultural change and future deforestation, but a slight superiority of the economic models to predict future built change. However, the margins of superiority were too small to be statistically significant. The resulting tree structures were used, however, to derive a series of principles or "rules" governing land use change in both provinces. The model was able to predict future land use, given a series of assumptions, with 90 percent overall accuracies. The model can be used in other developing or developed country locations for future land use prediction, determination of future threatened areas, or to derive "rules" or principles driving land use change.

Parental Depressive Symptoms and Adolescent Adjustment: A Prospective Test of an Explanatory Model for the Role of Marital Conflict

PubMed Central

Cummings, E. Mark; Cheung, Rebecca Y. M.; Koss, Kalsea; Davies, Patrick T.

2014-01-01

Despite calls for process-oriented models for child maladjustment due to heightened marital conflict in the context of parental depressive symptoms, few longitudinal tests of the mechanisms underlying these relations have been conducted. Addressing this gap, the present study examined multiple factors longitudinally that link parental depressive symptoms to adolescent adjustment problems, building on a conceptual model informed by emotional security theory (EST). Participants were 320 families (158 boys, 162 girls), including mothers and fathers, who took part when their children were in kindergarten (T1), second (T2), seventh (T3), eighth (T4) and ninth (T5) grades. Parental depressive symptoms (T1) were related to changes in adolescents’ externalizing and internalizing symptoms (T5), as mediated by parents’ negative emotional expressiveness (T2), marital conflict (T3), and emotional insecurity (T4). Evidence was thus advanced for emotional insecurity as an explanatory process in the context of parental depressive symptoms. PMID:24652484

Exploring the Wisdom Structure: Validation of the Spanish New Short Three-Dimensional Wisdom Scale (3D-WS) and Its Explanatory Power on Psychological Health-Related Variables.

PubMed

García-Campayo, Javier; Del Hoyo, Yolanda L; Barceló-Soler, Alberto; Navarro-Gil, Mayte; Borao, Luis; Giarin, Veronica; Tovar-Garcia, R Raziel; Montero-Marin, Jesus

2018-01-01

Introduction: Personal wisdom has demonstrated important implications for the health of individuals. The aim of the present study was to validate a Spanish version of the Three-Dimensional Wisdom Scale (3D-WS), exploring the structure of a possible general factor, and assessing its explanatory power on psychological health-related variables. Methods: A cross-sectional study design was used, with a total sample of 624 Spanish participants recruited on the Internet and randomly split into two halves. The following instruments were applied: 3D-WS, Purpose in Life (PIL), Multidimensional State Boredom Scale (MSBS), Positive and Negative Affect Scale (PANAS), and Difficulties in Emotion Regulation Scale (DERS). Factorial structures were analyzed through exploratory and confirmatory factor analysis (EFA and CFA), and the general factor was characterized by using bifactor models. The explanatory power of the 3D-WS was established by multiple regression. Results: The original long and short versions of the 3D-WS were not replicated in the first subsample using EFA, and there was a high rate of cross-loadings. Thus, a new short 3D-WS was proposed by ordering the original items according to factorial weights. This three-correlated-factor (reflective, cognitive, and affective) proposal was tested by means of CFA in the second subsample, with adequate psychometrics and invariance, and a good fit (χ 2 /df = 1.98; CFI = 0.946; RMSEA = 0.056; 90% CI = 0.040-0.072). A bifactor structure, in which the reflective trait of wisdom was integrated into a general factor (G-Reflective) improved the model fit (χ 2 /df = 1.85; CFI = 0.959; RMSEA = 0.052; 90% CI = 0.035-0.070). The explained common variance of G-Reflective was 0.53; therefore, the new short 3D-WS should not be considered essentially unidimensional. The new short 3D-WS showed positive relationships with the PIL and PANAS-positive, and negative associations with the MSBS, PANAS-negative and DERS, contributing to explain all the referred variables. These results were consistent across subsamples. Conclusion: The new short 3D-WS appears to be a reliable instrument for measuring wisdom in the Spanish general population. The reflective facet might influence the cognitive and affective wisdom components through the G-Reflective general factor. There seems to be a high explanatory power of the 3D-WS on psychological health-related variables. This study will facilitate the development of future research and psychological knowledge regarding wisdom.

Inferential consequences of modeling rather than measuring snow accumulation in studies of animal ecology

USGS Publications Warehouse

Cross, Paul C.; Klaver, Robert W.; Brennan, Angela; Creel, Scott; Beckmann, Jon P.; Higgs, Megan D.; Scurlock, Brandon M.

2013-01-01

Abstract. It is increasingly common for studies of animal ecology to use model-based predictions of environmental variables as explanatory or predictor variables, even though model prediction uncertainty is typically unknown. To demonstrate the potential for misleading inferences when model predictions with error are used in place of direct measurements, we compared snow water equivalent (SWE) and snow depth as predicted by the Snow Data Assimilation System (SNODAS) to field measurements of SWE and snow depth. We examined locations on elk (Cervus canadensis) winter ranges in western Wyoming, because modeled data such as SNODAS output are often used for inferences on elk ecology. Overall, SNODAS predictions tended to overestimate field measurements, prediction uncertainty was high, and the difference between SNODAS predictions and field measurements was greater in snow shadows for both snow variables compared to non-snow shadow areas. We used a simple simulation of snow effects on the probability of an elk being killed by a predator to show that, if SNODAS prediction uncertainty was ignored, we might have mistakenly concluded that SWE was not an important factor in where elk were killed in predatory attacks during the winter. In this simulation, we were interested in the effects of snow at finer scales (2) than the resolution of SNODAS. If bias were to decrease when SNODAS predictions are averaged over coarser scales, SNODAS would be applicable to population-level ecology studies. In our study, however, averaging predictions over moderate to broad spatial scales (9–2200 km2) did not reduce the differences between SNODAS predictions and field measurements. This study highlights the need to carefully evaluate two issues when using model output as an explanatory variable in subsequent analysis: (1) the model’s resolution relative to the scale of the ecological question of interest and (2) the implications of prediction uncertainty on inferences when using model predictions as explanatory or predictor variables.

Color doppler in clinical cardiology

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

Duncan, W.J.

1987-01-01

A presentation of color doppler, which enables physicians to pinpoint problems and develop effective treatment. State-of-the-art illustrations and layout, with color images and explanatory text are included.

[Health promotion. Instrument development for the application of the theory of planned behavior].

PubMed

Lee, Y O

1993-01-01

The purpose of this article is to describe operationalization of the Theory of Planned Behavior (TPB). The quest to understand determinants of health behaviors has intensified as evidence accumulates concerning the impact of personal behavior on health. The majority of theory-based research has used the Health Belief Model(HBM). The HBM components have had limited success in explaining health-related behaviors. There are several advantages of the TPB over the HBM. TPB is an expansion of the Theory of Reasoned Action(TRA) with the addition of the construct, perceived behavioral control. The revised model has been shown to yield greater explanatory power than the original TRA for goal-directed behaviors. The process of TPB instrument development was described, using example form the study of smoking cessation behavior in military smokers. It was followed by a discussion of reliability and validity issues in operationalizing the TPB. The TPB is a useful model for understanding and predicting health-related behaviors when carefully operationalized. The model holds promise in the development of prescriptive nursing approaches.

Continuous water-quality monitoring and regression analysis to estimate constituent concentrations and loads in the Red River of the North at Fargo and Grand Forks, North Dakota, 2003-12

USGS Publications Warehouse

Galloway, Joel M.

2014-01-01

The Red River of the North (hereafter referred to as “Red River”) Basin is an important hydrologic region where water is a valuable resource for the region’s economy. Continuous water-quality monitors have been operated by the U.S. Geological Survey, in cooperation with the North Dakota Department of Health, Minnesota Pollution Control Agency, City of Fargo, City of Moorhead, City of Grand Forks, and City of East Grand Forks at the Red River at Fargo, North Dakota, from 2003 through 2012 and at Grand Forks, N.Dak., from 2007 through 2012. The purpose of the monitoring was to provide a better understanding of the water-quality dynamics of the Red River and provide a way to track changes in water quality. Regression equations were developed that can be used to estimate concentrations and loads for dissolved solids, sulfate, chloride, nitrate plus nitrite, total phosphorus, and suspended sediment using explanatory variables such as streamflow, specific conductance, and turbidity. Specific conductance was determined to be a significant explanatory variable for estimating dissolved solids concentrations at the Red River at Fargo and Grand Forks. The regression equations provided good relations between dissolved solid concentrations and specific conductance for the Red River at Fargo and at Grand Forks, with adjusted coefficients of determination of 0.99 and 0.98, respectively. Specific conductance, log-transformed streamflow, and a seasonal component were statistically significant explanatory variables for estimating sulfate in the Red River at Fargo and Grand Forks. Regression equations provided good relations between sulfate concentrations and the explanatory variables, with adjusted coefficients of determination of 0.94 and 0.89, respectively. For the Red River at Fargo and Grand Forks, specific conductance, streamflow, and a seasonal component were statistically significant explanatory variables for estimating chloride. For the Red River at Grand Forks, a time component also was a statistically significant explanatory variable for estimating chloride. The regression equations for chloride at the Red River at Fargo provided a fair relation between chloride concentrations and the explanatory variables, with an adjusted coefficient of determination of 0.66 and the equation for the Red River at Grand Forks provided a relatively good relation between chloride concentrations and the explanatory variables, with an adjusted coefficient of determination of 0.77. Turbidity and streamflow were statistically significant explanatory variables for estimating nitrate plus nitrite concentrations at the Red River at Fargo and turbidity was the only statistically significant explanatory variable for estimating nitrate plus nitrite concentrations at Grand Forks. The regression equation for the Red River at Fargo provided a relatively poor relation between nitrate plus nitrite concentrations, turbidity, and streamflow, with an adjusted coefficient of determination of 0.46. The regression equation for the Red River at Grand Forks provided a fair relation between nitrate plus nitrite concentrations and turbidity, with an adjusted coefficient of determination of 0.73. Some of the variability that was not explained by the equations might be attributed to different sources contributing nitrates to the stream at different times. Turbidity, streamflow, and a seasonal component were statistically significant explanatory variables for estimating total phosphorus at the Red River at Fargo and Grand Forks. The regression equation for the Red River at Fargo provided a relatively fair relation between total phosphorus concentrations, turbidity, streamflow, and season, with an adjusted coefficient of determination of 0.74. The regression equation for the Red River at Grand Forks provided a good relation between total phosphorus concentrations, turbidity, streamflow, and season, with an adjusted coefficient of determination of 0.87. For the Red River at Fargo, turbidity and streamflow were statistically significant explanatory variables for estimating suspended-sediment concentrations. For the Red River at Grand Forks, turbidity was the only statistically significant explanatory variable for estimating suspended-sediment concentration. The regression equation at the Red River at Fargo provided a good relation between suspended-sediment concentration, turbidity, and streamflow, with an adjusted coefficient of determination of 0.95. The regression equation for the Red River at Grand Forks provided a good relation between suspended-sediment concentration and turbidity, with an adjusted coefficient of determination of 0.96.

Explaining the Development of False Memories.

ERIC Educational Resources Information Center

Reyna, Valerie F.; Holliday, Robyn; Marche, Tammy

2002-01-01

Reviews explanatory dimensions of children's false memory relevant to forensic practice: measurement, development, social factors, individual differences, varieties of memories and memory judgments, and varieties of procedures inducing false memories. Asserts that recent studies fail to use techniques that separate acquiescence from memory…

Classification and regression trees

Treesearch

G. G. Moisen

2008-01-01

Frequently, ecologists are interested in exploring ecological relationships, describing patterns and processes, or making spatial or temporal predictions. These purposes often can be addressed by modeling the relationship between some outcome or response and a set of features or explanatory variables.

Experimental Philosophy of Explanation Rising: The Case for a Plurality of Concepts of Explanation.

PubMed

Colombo, Matteo

2017-03-01

This paper brings together results from the philosophy and the psychology of explanation to argue that there are multiple concepts of explanation in human psychology. Specifically, it is shown that pluralism about explanation coheres with the multiplicity of models of explanation available in the philosophy of science, and it is supported by evidence from the psychology of explanatory judgment. Focusing on the case of a norm of explanatory power, the paper concludes by responding to the worry that if there is a plurality of concepts of explanation, one will not be able to normatively evaluate what counts as good explanation. Copyright © 2016 Cognitive Science Society, Inc.

Entropy-based financial asset pricing.

PubMed

Ormos, Mihály; Zibriczky, Dávid

2014-01-01

We investigate entropy as a financial risk measure. Entropy explains the equity premium of securities and portfolios in a simpler way and, at the same time, with higher explanatory power than the beta parameter of the capital asset pricing model. For asset pricing we define the continuous entropy as an alternative measure of risk. Our results show that entropy decreases in the function of the number of securities involved in a portfolio in a similar way to the standard deviation, and that efficient portfolios are situated on a hyperbola in the expected return-entropy system. For empirical investigation we use daily returns of 150 randomly selected securities for a period of 27 years. Our regression results show that entropy has a higher explanatory power for the expected return than the capital asset pricing model beta. Furthermore we show the time varying behavior of the beta along with entropy.

Entropy-Based Financial Asset Pricing

PubMed Central

Ormos, Mihály; Zibriczky, Dávid

2014-01-01

We investigate entropy as a financial risk measure. Entropy explains the equity premium of securities and portfolios in a simpler way and, at the same time, with higher explanatory power than the beta parameter of the capital asset pricing model. For asset pricing we define the continuous entropy as an alternative measure of risk. Our results show that entropy decreases in the function of the number of securities involved in a portfolio in a similar way to the standard deviation, and that efficient portfolios are situated on a hyperbola in the expected return – entropy system. For empirical investigation we use daily returns of 150 randomly selected securities for a period of 27 years. Our regression results show that entropy has a higher explanatory power for the expected return than the capital asset pricing model beta. Furthermore we show the time varying behavior of the beta along with entropy. PMID:25545668

On the nature of explanation: A PDP approach

NASA Astrophysics Data System (ADS)

Churchland, Paul M.

1990-06-01

Neural network models of sensory processing and associative memory provide the resources for a new theory of what explanatory understanding consists in. That theory finds the theoretically important factors to reside not at the level of propositions and the relations between them, but at the level of the activation patterns across large populations of neurons. The theory portrays explanatory understanding, perceptual recognition, and abductive inference as being different instances of the same more general sort of cognitive achievement, viz. prototype activation. It thus effects a unification of the theories of explanation, perception, and ampliative inference. It also finds systematic unity in the wide diversity of types of explanation (causal, functional, mathematical, intentional, reductive, etc.), a chronic problem for theories of explanation in the logico-linguistic tradition. Finally, it is free of the many defects, both logical and psychological, that plague models in that older tradition.

Obligatory Effort [Hishtadlut] as an Explanatory Model: A Critique of Reproductive Choice and Control.

PubMed

Teman, Elly; Ivry, Tsipy; Goren, Heela

2016-06-01

Studies on reproductive technologies often examine women's reproductive lives in terms of choice and control. Drawing on 48 accounts of procreative experiences of religiously devout Jewish women in Israel and the US, we examine their attitudes, understandings and experiences of pregnancy, reproductive technologies and prenatal testing. We suggest that the concept of hishtadlut-"obligatory effort"-works as an explanatory model that organizes Haredi women's reproductive careers and their negotiations of reproductive technologies. As an elastic category with negotiable and dynamic boundaries, hishtadlut gives ultra-orthodox Jewish women room for effort without the assumption of control; it allows them to exercise discretion in relation to medical issues without framing their efforts in terms of individual choice. Haredi women hold themselves responsible for making their obligatory effort and not for pregnancy outcomes. We suggest that an alternative paradigm to autonomous choice and control emerges from cosmological orders where reproductive duties constitute "obligatory choices."

Eliciting the Functional Processes of Apologizing for Errors in Health Care: Developing an Explanatory Model of Apology.

PubMed

Prothero, Marie M; Morse, Janice M

2017-01-01

The purpose of this article was to analyze the concept development of apology in the context of errors in health care, the administrative response, policy and format/process of the subsequent apology. Using pragmatic utility and a systematic review of the literature, 29 articles and one book provided attributes involved in apologizing. Analytic questions were developed to guide the data synthesis and types of apologies used in different circumstances identified. The antecedents of apologizing, and the attributes and outcomes were identified. A model was constructed illustrating the components of a complete apology, other types of apologies, and ramifications/outcomes of each. Clinical implications of developing formal policies for correcting medical errors through apologies are recommended. Defining the essential elements of apology is the first step in establishing a just culture in health care. Respect for patient-centered care reduces the retaliate consequences following an error, and may even restore the physician patient relationship.

Neuroscience Meets Music Education: Exploring the Implications of Neural Processing Models on Music Education Practice

ERIC Educational Resources Information Center

Collins, Anita

2013-01-01

Over the past two decades, neuroscientists have been fascinated by the way the brain processes music. Using new technologies, neuroscientists offer us a better understanding of the human brain's structures and functions. They have further proposed explanatory models for how the brain processes music. While these models shed light on how the…

Teach, Coach, Live: The Viability of the Three-Role Teaching Model in the 21st Century

ERIC Educational Resources Information Center

Martin, Joseph Gregory

2016-01-01

This explanatory mixed-methods study is focused on the sustainability of the triple-threat model of teaching found at elite American boarding schools. In this model, faculty members are expected to teach, coach, and perform residential duties as part of their contract. While elite boarding schools have been researched in recent years, no research…

A Structural Equation Model Explaining 8th Grade Students' Mathematics Achievements

ERIC Educational Resources Information Center

Yurt, Eyüp; Sünbül, Ali Murat

2014-01-01

The purpose of this study is to investigate, via a model, the explanatory and predictive relationships among the following variables: Mathematical Problem Solving and Reasoning Skills, Sources of Mathematics Self-Efficacy, Spatial Ability, and Mathematics Achievements of Secondary School 8th Grade Students. The sample group of the study, itself…

Conflicts Management Model in School: A Mixed Design Study

ERIC Educational Resources Information Center

Dogan, Soner

2016-01-01

The object of this study is to evaluate the reasons for conflicts occurring in school according to perceptions and views of teachers and resolution strategies used for conflicts and to build a model based on the results obtained. In the research, explanatory design including quantitative and qualitative methods has been used. The quantitative part…

Violence Prevention in United States Society of Jesus Secondary Schools

ERIC Educational Resources Information Center

Simonds, Thomas Andrew

2009-01-01

Using data from a representative number of Society of Jesus secondary schools, the researcher reports what these schools are doing to prevent violence, and tests an explanatory model of school violence he created. The researcher proposes that this model can be used to explain and prevent school violence by identifying and addressing the…

University Students' Explanatory Models of the Interactions between Electric Charges and Magnetic Fields

ERIC Educational Resources Information Center

Saglam, Murat

2010-01-01

This study aimed to investigate the models that co-existed in students' cognitive structure to explain the interactions between electric charges and uniform magnetic fields. The sample consisted of 129 first-year civil engineering, geology and geophysics students from a large state university in western Turkey. The students answered five…

Making the Transition: An Explanatory Model of Special Education Students' Participation in Postsecondary Education.

ERIC Educational Resources Information Center

Butler-Nalin, Paul; And Others

The report of the National Longitudinal Transition Study presents initial findings on individual characteristics which relate to postsecondary education participation since 1985-86 among more than 8,000 youth (ages 13 to 23) with disabilities. A series of logistic regression models examines such factors as youth's background characteristics,…

Valuing improved wetland quality using choice modeling

NASA Astrophysics Data System (ADS)

Morrison, Mark; Bennett, Jeff; Blamey, Russell

1999-09-01

The main stated preference technique used for estimating environmental values is the contingent valuation method. In this paper the results of an application of an alternative technique, choice modeling, are reported. Choice modeling has been developed in the marketing and transport applications but has only been used in a handful of environmental applications, most of which have focused on use values. The case study presented here involves the estimation of the nonuse environmental values provided by the Macquarie Marshes, a major wetland in New South Wales, Australia. Estimates of the nonuse value the community places on preventing job losses are also presented. The reported models are robust, having high explanatory power and variables that are statistically significant and consistent with expectations. These results provide support for the hypothesis that choice modeling can be used to estimate nonuse values for both environmental and social consequences of resource use changes.

Research of Water Level Prediction for a Continuous Flood due to Typhoons Based on a Machine Learning Method

NASA Astrophysics Data System (ADS)

Nakatsugawa, M.; Kobayashi, Y.; Okazaki, R.; Taniguchi, Y.

2017-12-01

This research aims to improve accuracy of water level prediction calculations for more effective river management. In August 2016, Hokkaido was visited by four typhoons, whose heavy rainfall caused severe flooding. In the Tokoro river basin of Eastern Hokkaido, the water level (WL) at the Kamikawazoe gauging station, which is at the lower reaches exceeded the design high-water level and the water rose to the highest level on record. To predict such flood conditions and mitigate disaster damage, it is necessary to improve the accuracy of prediction as well as to prolong the lead time (LT) required for disaster mitigation measures such as flood-fighting activities and evacuation actions by residents. There is the need to predict the river water level around the peak stage earlier and more accurately. Previous research dealing with WL prediction had proposed a method in which the WL at the lower reaches is estimated by the correlation with the WL at the upper reaches (hereinafter: "the water level correlation method"). Additionally, a runoff model-based method has been generally used in which the discharge is estimated by giving rainfall prediction data to a runoff model such as a storage function model and then the WL is estimated from that discharge by using a WL discharge rating curve (H-Q curve). In this research, an attempt was made to predict WL by applying the Random Forest (RF) method, which is a machine learning method that can estimate the contribution of explanatory variables. Furthermore, from the practical point of view, we investigated the prediction of WL based on a multiple correlation (MC) method involving factors using explanatory variables with high contribution in the RF method, and we examined the proper selection of explanatory variables and the extension of LT. The following results were found: 1) Based on the RF method tuned up by learning from previous floods, the WL for the abnormal flood case of August 2016 was properly predicted with a lead time of 6 h. 2) Based on the contribution of explanatory variables, factors were selected for the MC method. In this way, plausible prediction results were obtained.

Dimensions of integration in interdisciplinary explanations of the origin of evolutionary novelty.

PubMed

Love, Alan C; Lugar, Gary L

2013-12-01

Many philosophers of biology have embraced a version of pluralism in response to the failure of theory reduction but overlook how concepts, methods, and explanatory resources are in fact coordinated, such as in interdisciplinary research where the aim is to integrate different strands into an articulated whole. This is observable for the origin of evolutionary novelty-a complex problem that requires a synthesis of intellectual resources from different fields to arrive at robust answers to multiple allied questions. It is an apt locus for exploring new dimensions of explanatory integration because it necessitates coordination among historical and experimental disciplines (e.g., geology and molecular biology). These coordination issues are widespread for the origin of novel morphologies observed in the Cambrian Explosion. Despite an explicit commitment to an integrated, interdisciplinary explanation, some potential disciplinary contributors are excluded. Notable among these exclusions is the physics of ontogeny. We argue that two different dimensions of integration-data and standards-have been insufficiently distinguished. This distinction accounts for why physics-based explanatory contributions to the origin of novelty have been resisted: they do not integrate certain types of data and differ in how they conceptualize the standard of uniformitarianism in historical, causal explanations. Our analysis of these different dimensions of integration contributes to the development of more adequate and integrated explanatory frameworks. Copyright © 2013 Elsevier Ltd. All rights reserved.

Design of an impact evaluation using a mixed methods model--an explanatory assessment of the effects of results-based financing mechanisms on maternal healthcare services in Malawi.

PubMed

Brenner, Stephan; Muula, Adamson S; Robyn, Paul Jacob; Bärnighausen, Till; Sarker, Malabika; Mathanga, Don P; Bossert, Thomas; De Allegri, Manuela

2014-04-22

In this article we present a study design to evaluate the causal impact of providing supply-side performance-based financing incentives in combination with a demand-side cash transfer component on equitable access to and quality of maternal and neonatal healthcare services. This intervention is introduced to selected emergency obstetric care facilities and catchment area populations in four districts in Malawi. We here describe and discuss our study protocol with regard to the research aims, the local implementation context, and our rationale for selecting a mixed methods explanatory design with a quasi-experimental quantitative component. The quantitative research component consists of a controlled pre- and post-test design with multiple post-test measurements. This allows us to quantitatively measure 'equitable access to healthcare services' at the community level and 'healthcare quality' at the health facility level. Guided by a theoretical framework of causal relationships, we determined a number of input, process, and output indicators to evaluate both intended and unintended effects of the intervention. Overall causal impact estimates will result from a difference-in-difference analysis comparing selected indicators across intervention and control facilities/catchment populations over time.To further explain heterogeneity of quantitatively observed effects and to understand the experiential dimensions of financial incentives on clients and providers, we designed a qualitative component in line with the overall explanatory mixed methods approach. This component consists of in-depth interviews and focus group discussions with providers, service user, non-users, and policy stakeholders. In this explanatory design comprehensive understanding of expected and unexpected effects of the intervention on both access and quality will emerge through careful triangulation at two levels: across multiple quantitative elements and across quantitative and qualitative elements. Combining a traditional quasi-experimental controlled pre- and post-test design with an explanatory mixed methods model permits an additional assessment of organizational and behavioral changes affecting complex processes. Through this impact evaluation approach, our design will not only create robust evidence measures for the outcome of interest, but also generate insights on how and why the investigated interventions produce certain intended and unintended effects and allows for a more in-depth evaluation approach.

Examining Explanatory Style's Relationship to Efficacy and Burnout in Teachers

ERIC Educational Resources Information Center

Fineburg, Amy Cheek

2010-01-01

Explanatory style, the ways in which people explain both good and bad events (Seligman, 1998), shares theoretical components with teachers' sense of efficacy (Tshannon-Moran & Woolfolk-Hoy, 2001), which is how capable teachers feel about teaching. According to Bandura (1994), efficacy informs explanatory style, but this assertion does not…

A Content Analysis of Acculturation Research in the Career Development Literature

ERIC Educational Resources Information Center

Miller, Matthew J.; Kerlow-Myers, Andrew E.

2009-01-01

The purpose of the present study was to highlight the importance of acculturation as an explanatory variable in career development and to provide an empirical review of acculturation research in the career development literature. Acculturation is a cultural variable that has been linked to a number of important career development outcomes for…

An outline of graphical Markov models in dentistry.

PubMed

Helfenstein, U; Steiner, M; Menghini, G

1999-12-01

In the usual multiple regression model there is one response variable and one block of several explanatory variables. In contrast, in reality there may be a block of several possibly interacting response variables one would like to explain. In addition, the explanatory variables may split into a sequence of several blocks, each block containing several interacting variables. The variables in the second block are explained by those in the first block; the variables in the third block by those in the first and the second block etc. During recent years methods have been developed allowing analysis of problems where the data set has the above complex structure. The models involved are called graphical models or graphical Markov models. The main result of an analysis is a picture, a conditional independence graph with precise statistical meaning, consisting of circles representing variables and lines or arrows representing significant conditional associations. The absence of a line between two circles signifies that the corresponding two variables are independent conditional on the presence of other variables in the model. An example from epidemiology is presented in order to demonstrate application and use of the models. The data set in the example has a complex structure consisting of successive blocks: the variable in the first block is year of investigation; the variables in the second block are age and gender; the variables in the third block are indices of calculus, gingivitis and mutans streptococci and the final response variables in the fourth block are different indices of caries. Since the statistical methods may not be easily accessible to dentists, this article presents them in an introductory form. Graphical models may be of great value to dentists in allowing analysis and visualisation of complex structured multivariate data sets consisting of a sequence of blocks of interacting variables and, in particular, several possibly interacting responses in the final block.

Combined Effects of Soil Biotic and Abiotic Factors, Influenced by Sewage Sludge Incorporation, on the Incidence of Corn Stalk Rot

PubMed Central

Fortes, Nara Lúcia Perondi; Navas-Cortés, Juan A; Silva, Carlos Alberto; Bettiol, Wagner

2016-01-01

The objectives of this study were to evaluate the combined effects of soil biotic and abiotic factors on the incidence of Fusarium corn stalk rot, during four annual incorporations of two types of sewage sludge into soil in a 5-years field assay under tropical conditions and to predict the effects of these variables on the disease. For each type of sewage sludge, the following treatments were included: control with mineral fertilization recommended for corn; control without fertilization; sewage sludge based on the nitrogen concentration that provided the same amount of nitrogen as in the mineral fertilizer treatment; and sewage sludge that provided two, four and eight times the nitrogen concentration recommended for corn. Increasing dosages of both types of sewage sludge incorporated into soil resulted in increased corn stalk rot incidence, being negatively correlated with corn yield. A global analysis highlighted the effect of the year of the experiment, followed by the sewage sludge dosages. The type of sewage sludge did not affect the disease incidence. A multiple logistic model using a stepwise procedure was fitted based on the selection of a model that included the three explanatory parameters for disease incidence: electrical conductivity, magnesium and Fusarium population. In the selected model, the probability of higher disease incidence increased with an increase of these three explanatory parameters. When the explanatory parameters were compared, electrical conductivity presented a dominant effect and was the main variable to predict the probability distribution curves of Fusarium corn stalk rot, after sewage sludge application into the soil. PMID:27176597

Spatial analysis of participation in the Waterloo Residential Energy Efficiency Project

NASA Astrophysics Data System (ADS)

Song, Ge Bella

Researchers are in broad agreement that energy-conserving actions produce economic as well as energy savings. Household energy rating systems (HERS) have been established in many countries to inform households of their house's current energy performance and to help reduce their energy consumption and greenhouse gas emissions. In Canada, the national EnerGuide for Houses (EGH) program is delivered by many local delivery agents, including non-profit green community organizations. Waterloo Region Green Solutions is the local non-profit that offers the EGH residential energy evaluation service to local households. The purpose of this thesis is to explore the determinants of household's participation in the residential energy efficiency program (REEP) in Waterloo Region, to explain the relationship between the explanatory variables and REEP participation, and to propose ways to improve this kind of program. A spatial (trend) analysis was conducted within a geographic information system (GIS) to determine the spatial patterns of the REEP participation in Waterloo Region from 1999 to 2006. The impact of sources of information on participation and relationships between participation rates and explanatory variables were identified. GIS proved successful in presenting a visual interpretation of spatial patterns of the REEP participation. In general, the participating households tend to be clustered in urban areas and scattered in rural areas. Different sources of information played significant roles in reaching participants in different years. Moreover, there was a relationship between each explanatory variable and the REEP participation rates. Statistical analysis was applied to obtain a quantitative assessment of relationships between hypothesized explanatory variables and participation in the REEP. The Poisson regression model was used to determine the relationship between hypothesized explanatory variables and REEP participation at the CDA level. The results show that all of the independent variables have a statistically significant positive relationship with REEP participation. These variables include level of education, average household income, employment rate, home ownership, population aged 65 and over, age of home, and number of eligible dwellings. The logistic regression model was used to assess the ability of the hypothesized explanatory variables to predict whether or not households would participate in a second follow-up evaluation after completing upgrades to their home. The results show all the explanatory variables have significant relationships with the dependent variable. The increased rating score, average household income, aged population, and age of home are positively related to the dependent variable. While the dwelling size and education has negative relationships with the dependent variable. In general, the contribution of this work provides a practical understanding of how the energy efficiency program operates, and insight into the type of variables that may be successful in bringing about changes in performance in the energy efficiency project in Waterloo Region. Secondly, with the completion of this research, future residential energy efficiency programs can use the information from this research and emulate or expand upon the efforts and lessons learned from the Residential Energy Efficiency Project in Waterloo Region case study. Thirdly, this research also contributes to practical experience on how to integrate different datasets using GIS.

Cost-oriented evaluation of ecosystem services under consideration of income risks and risk attitudes of farmers.

PubMed

Dörschner, T; Musshoff, O

2013-09-30

Agri-environmental measures are often not as accepted among farmers as is expected. The present study investigates whether changes in income risks and the individual risk attitudes of farmers may constitute an explanatory approach for the low acceptance of the measures. For this purpose, a normative model is developed that calculates the premia claimed by the farmers for adopting environmental measures under the consideration of income risks and different risk attitudes. We apply this model to environmental measures aiming at an increase of the faunistic diversity of species on grassland and showing that changes in income risks and the decision makers' risk attitudes can significantly influence farmers' minimum compensation claims. Copyright © 2013 Elsevier Ltd. All rights reserved.

Landscapes for Energy and Wildlife: Conservation Prioritization for Golden Eagles across Large Spatial Scales

PubMed Central

Tack, Jason D.; Fedy, Bradley C.

2015-01-01

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development. PMID:26262876

Landscapes for energy and wildlife: conservation prioritization for golden eagles across large spatial scales

USGS Publications Warehouse

Tack, Jason D.; Fedy, Bradley C.

2015-01-01

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.

Landscapes for Energy and Wildlife: Conservation Prioritization for Golden Eagles across Large Spatial Scales.

PubMed

Tack, Jason D; Fedy, Bradley C

2015-01-01

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.

Treatment Seeking and Self-Constructed Explanations of Pain and Pain Management Strategies Among Adolescents with Temporomandibular Disorder Pain.

PubMed

Nilsson, Ing-Marie; Willman, Ania

2016-01-01

To explore adolescents' explanations of their temporomandibular disorder (TMD) pain, their pain management strategies for TMD pain, and their treatment-seeking behavior. One-on-one interviews were conducted with 21 adolescents aged 15 to 19 years who had TMD pain and followed a semistructured interview guide. Subjects were strategically selected from patients referred to an orofacial pain clinic. All participants had been examined and received a pain diagnosis based on the Research Diagnostic Criteria for TMD. The interviews focused on the adolescents' experiences of TMD pain, their strategies for handling pain, and how they seek care. The interviews were recorded, transcribed verbatim, and analyzed using qualitative manifest content analysis. Qualitative manifest content analysis revealed two categories: (1) self-constructed explanations, with three subcategories (situation-based explanatory model, physical/biologic model, and psychological explanatory model); and (2) pain management strategies, with four subcategories (social support, treatment, relaxation/rest, and psychological strategies). Adolescents used physical activities and psychological and pharmacologic treatment to manage pain. Reasons for seeking treatment were to be cured, to obtain an explanation for their pain, and because their symptoms bother others. Adolescents living with TMD pain develop self-constructed explanations and pain management strategies. With access to these descriptions, dentists can be better prepared to have a dialogue with their adolescent patients about their own explanations of pain, the nature of pain, and in which situations the pain appears. Dentists can also explore adolescent patients' pain management strategies and perhaps also suggest new treatment strategies at an earlier stage.

Unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance

USGS Publications Warehouse

Fiske, I.J.; Chandler, R.B.

2011-01-01

Ecological research uses data collection techniques that are prone to substantial and unique types of measurement error to address scientic questions about species abundance and distribution. These data collection schemes include a number of survey methods in which unmarked individuals are counted, or determined to be present, at spatially- referenced sites. Examples include site occupancy sampling, repeated counts, distance sampling, removal sampling, and double observer sampling. To appropriately analyze these data, hierarchical models have been developed to separately model explanatory variables of both a latent abundance or occurrence process and a conditional detection process. Because these models have a straightforward interpretation paralleling mecha- nisms under which the data arose, they have recently gained immense popularity. The common hierarchical structure of these models is well-suited for a unied modeling in- terface. The R package unmarked provides such a unied modeling framework, including tools for data exploration, model tting, model criticism, post-hoc analysis, and model comparison.

Local-scale topoclimate effects on treeline elevations: a country-wide investigation of New Zealand's southern beech treelines.

PubMed

Case, Bradley S; Buckley, Hannah L

2015-01-01

Although treeline elevations are limited globally by growing season temperature, at regional scales treelines frequently deviate below their climatic limit. The cause of these deviations relate to a host of climatic, disturbance, and geomorphic factors that operate at multiple scales. The ability to disentangle the relative effects of these factors is currently hampered by the lack of reliable topoclimatic data, which describe how regional climatic characteristics are modified by topographic effects in mountain areas. In this study we present an analysis of the combined effects of local- and regional-scale factors on southern beech treeline elevation variability at 28 study areas across New Zealand. We apply a mesoscale atmospheric model to generate local-scale (200 m) meteorological data at these treelines and, from these data, we derive a set of topoclimatic indices that reflect possible detrimental and ameliorative influences on tree physiological functioning. Principal components analysis of meteorological data revealed geographic structure in how study areas were situated in multivariate space along gradients of topoclimate. Random forest and conditional inference tree modelling enabled us to tease apart the relative effects of 17 explanatory factors on local-scale treeline elevation variability. Overall, modelling explained about 50% of the variation in treeline elevation variability across the 28 study areas, with local landform and topoclimatic effects generally outweighing those from regional-scale factors across the 28 study areas. Further, the nature of the relationships between treeline elevation variability and the explanatory variables were complex, frequently non-linear, and consistent with the treeline literature. To our knowledge, this is the first study where model-generated meteorological data, and derived topoclimatic indices, have been developed and applied to explain treeline variation. Our results demonstrate the potential of such an approach for ecological research in mountainous environments.

Local-scale topoclimate effects on treeline elevations: a country-wide investigation of New Zealand’s southern beech treelines

PubMed Central

Buckley, Hannah L.

2015-01-01

Although treeline elevations are limited globally by growing season temperature, at regional scales treelines frequently deviate below their climatic limit. The cause of these deviations relate to a host of climatic, disturbance, and geomorphic factors that operate at multiple scales. The ability to disentangle the relative effects of these factors is currently hampered by the lack of reliable topoclimatic data, which describe how regional climatic characteristics are modified by topographic effects in mountain areas. In this study we present an analysis of the combined effects of local- and regional-scale factors on southern beech treeline elevation variability at 28 study areas across New Zealand. We apply a mesoscale atmospheric model to generate local-scale (200 m) meteorological data at these treelines and, from these data, we derive a set of topoclimatic indices that reflect possible detrimental and ameliorative influences on tree physiological functioning. Principal components analysis of meteorological data revealed geographic structure in how study areas were situated in multivariate space along gradients of topoclimate. Random forest and conditional inference tree modelling enabled us to tease apart the relative effects of 17 explanatory factors on local-scale treeline elevation variability. Overall, modelling explained about 50% of the variation in treeline elevation variability across the 28 study areas, with local landform and topoclimatic effects generally outweighing those from regional-scale factors across the 28 study areas. Further, the nature of the relationships between treeline elevation variability and the explanatory variables were complex, frequently non-linear, and consistent with the treeline literature. To our knowledge, this is the first study where model-generated meteorological data, and derived topoclimatic indices, have been developed and applied to explain treeline variation. Our results demonstrate the potential of such an approach for ecological research in mountainous environments. PMID:26528407

A conflict management scale for pharmacy.

PubMed

Austin, Zubin; Gregory, Paul A; Martin, Craig

2009-11-12

To develop and establish the validity and reliability of a conflict management scale specific to pharmacy practice and education. A multistage inventory-item development process was undertaken involving 93 pharmacists and using a previously described explanatory model for conflict in pharmacy practice. A 19-item inventory was developed, field tested, and validated. The conflict management scale (CMS) demonstrated an acceptable degree of reliability and validity for use in educational or practice settings to promote self-reflection and self-awareness regarding individuals' conflict management styles. The CMS provides a unique, pharmacy-specific method for individuals to determine and reflect upon their own conflict management styles. As part of an educational program to facilitate self-reflection and heighten self-awareness, the CMS may be a useful tool to promote discussions related to an important part of pharmacy practice.

Notions such as "truth" or "correspondence to the objective world" play no role in explanatory accounts of perception.

PubMed

Mausfeld, Rainer

2015-12-01

Hoffman, Singh, and Prakash (Psychonomic Review and Bulletin, 2015, in press) intend to show that perceptions are evolutionarily tuned to fitness rather than to truth. I argue, partly in accordance with their objective, that issues of 'truth' or 'veridicality' have no place in explanatory accounts of perception theory, and rather belong to either ordinary discourse or to philosophy. I regard, however, their general presumption that the evolutionary development of core achievements of the human perceptual system would be primarily determined by aspects of fitness and adaption as unwarranted in light of the evidence available.

The value of life according to "law as a way to survive".

PubMed

Roos, N H M

2003-01-01

Law as a Way to Survive is a comprehensive evolution-theory orientated philosophy of law and state that is tested in this article on its pertinence and explanatory power for the following issues: animal rights, abortion, euthanasia and assisted suicide. These subjects are suitable as tests precisely because they are not those for which LWS or rival theories, with which it will be compared, and for which it was primarily developed for. It will be concluded that LWS is very superior in pertinence and explanatory power both because it is much less metaphysical and much more complex than its rivals.

Ecological and cosmological coexistence thinking in a hypervariable environment: causal models of economic success and failure among farmers, foragers, and fishermen of southwestern Madagascar

PubMed Central

Tucker, Bram; Tsiazonera; Tombo, Jaovola; Hajasoa, Patricia; Nagnisaha, Charlotte

2015-01-01

A fact of life for farmers, hunter-gatherers, and fishermen in the rural parts of the world are that crops fail, wild resources become scarce, and winds discourage fishing. In this article we approach subsistence risk from the perspective of “coexistence thinking,” the simultaneous application of natural and supernatural causal models to explain subsistence success and failure. In southwestern Madagascar, the ecological world is characterized by extreme variability and unpredictability, and the cosmological world is characterized by anxiety about supernatural dangers. Ecological and cosmological causes seem to point to different risk minimizing strategies: to avoid losses from drought, flood, or heavy winds, one should diversify activities and be flexible; but to avoid losses caused by disrespected spirits one should narrow one’s range of behaviors to follow the code of taboos and offerings. We address this paradox by investigating whether southwestern Malagasy understand natural and supernatural causes as occupying separate, contradictory explanatory systems (target dependence), whether they make no categorical distinction between natural and supernatural forces and combine them within a single explanatory system (synthetic thinking), or whether they have separate natural and supernatural categories of causes that are integrated into one explanatory system so that supernatural forces drive natural forces (integrative thinking). Results from three field studies suggest that (a) informants explain why crops, prey, and market activities succeed or fail with reference to natural causal forces like rainfall and pests, (b) they explain why individual persons experience success or failure primarily with supernatural factors like God and ancestors, and (c) they understand supernatural forces as driving natural forces, so that ecology and cosmology represent distinct sets of causes within a single explanatory framework. We expect that future cross-cultural analyses may find that this form of “integrative thinking” is common in unpredictable environments and is a cognitive strategy that accompanies economic diversification. PMID:26528205

Ecological and cosmological coexistence thinking in a hypervariable environment: causal models of economic success and failure among farmers, foragers, and fishermen of southwestern Madagascar.

PubMed

Tucker, Bram; Tsiazonera; Tombo, Jaovola; Hajasoa, Patricia; Nagnisaha, Charlotte

2015-01-01

A fact of life for farmers, hunter-gatherers, and fishermen in the rural parts of the world are that crops fail, wild resources become scarce, and winds discourage fishing. In this article we approach subsistence risk from the perspective of "coexistence thinking," the simultaneous application of natural and supernatural causal models to explain subsistence success and failure. In southwestern Madagascar, the ecological world is characterized by extreme variability and unpredictability, and the cosmological world is characterized by anxiety about supernatural dangers. Ecological and cosmological causes seem to point to different risk minimizing strategies: to avoid losses from drought, flood, or heavy winds, one should diversify activities and be flexible; but to avoid losses caused by disrespected spirits one should narrow one's range of behaviors to follow the code of taboos and offerings. We address this paradox by investigating whether southwestern Malagasy understand natural and supernatural causes as occupying separate, contradictory explanatory systems (target dependence), whether they make no categorical distinction between natural and supernatural forces and combine them within a single explanatory system (synthetic thinking), or whether they have separate natural and supernatural categories of causes that are integrated into one explanatory system so that supernatural forces drive natural forces (integrative thinking). Results from three field studies suggest that (a) informants explain why crops, prey, and market activities succeed or fail with reference to natural causal forces like rainfall and pests, (b) they explain why individual persons experience success or failure primarily with supernatural factors like God and ancestors, and (c) they understand supernatural forces as driving natural forces, so that ecology and cosmology represent distinct sets of causes within a single explanatory framework. We expect that future cross-cultural analyses may find that this form of "integrative thinking" is common in unpredictable environments and is a cognitive strategy that accompanies economic diversification.

Student Misapplication of a Gas-Like Model to Explain Particle Movement in Heated Solids: Implications for Curriculum and Instruction towards Students' Creation and Revision of Accurate Explanatory Models

ERIC Educational Resources Information Center

Bouwma-Gearhart, Jana; Stewart, James; Brown, Keffrelyn

2009-01-01

Understanding the particulate nature of matter (PNM) is vital for participating in many areas of science. We assessed 11 students' atomic/molecular-level explanations of real-world phenomena after their participation in a modelling-based PNM unit. All 11 students offered a scientifically acceptable model regarding atomic/molecular behaviour in…

Children with Autism Spectrum Disorder Have an Exceptional Explanatory Drive

ERIC Educational Resources Information Center

Rutherford, M. D.; Subiaul, Francys

2016-01-01

An "explanatory drive" motivates children to explain ambiguity. Individuals with autism spectrum disorders are interested in how systems work, but it is unknown whether they have an explanatory drive. We presented children with and without autism spectrum disorder unsolvable problems in a physical and in a social context and evaluated…

Academic Judgment and Institutional Evaluation Made by Teachers According to Pupils' Explanatory Activity

ERIC Educational Resources Information Center

Jouffre, Stephane; Py, Jacques; Somat, Alain

2008-01-01

The influence of sixth-graders' explanatory activity was studied on their teachers' academic judgment. Concerning the pupils' explanatory activity, trait-related internal explanations were chosen more to explain positive events than negative ones, whereas the reverse was observed for effort/intention-related internal explanations. In response to…

Explanatory Typologies as a Nested Strategy of Inquiry: Combining Cross-Case and Within-Case Analyses

ERIC Educational Resources Information Center

Møller, Jørgen; Skaaning, Svend-Erik

2017-01-01

Explanatory typologies have recently experienced a renaissance as a research strategy for constructing and assessing causal explanations. However, both the new methodological works on explanatory typologies and the way such typologies have been used in practice have been affected by two shortcomings. First, no elaborate procedures for assessing…

Joint perceptual decision-making: a case study in explanatory pluralism

PubMed Central

Abney, Drew H.; Dale, Rick; Yoshimi, Jeff; Kello, Chris T.; Tylén, Kristian; Fusaroli, Riccardo

2014-01-01

Traditionally different approaches to the study of cognition have been viewed as competing explanatory frameworks. An alternative view, explanatory pluralism, regards different approaches to the study of cognition as complementary ways of studying the same phenomenon, at specific temporal and spatial scales, using appropriate methodological tools. Explanatory pluralism has been often described abstractly, but has rarely been applied to concrete cases. We present a case study of explanatory pluralism. We discuss three separate ways of studying the same phenomenon: a perceptual decision-making task (Bahrami et al., 2010), where pairs of subjects share information to jointly individuate an oddball stimulus among a set of distractors. Each approach analyzed the same corpus but targeted different units of analysis at different levels of description: decision-making at the behavioral level, confidence sharing at the linguistic level, and acoustic energy at the physical level. We discuss the utility of explanatory pluralism for describing this complex, multiscale phenomenon, show ways in which this case study sheds new light on the concept of pluralism, and highlight good practices to critically assess and complement approaches. PMID:24795679

Relationship between Sleep Disturbance and Functional Outcomes in Daily Life Habits of Children with Down Syndrome

PubMed Central

Churchill, Shervin S.; Kieckhefer, Gail M.; Bjornson, Kristie F.; Herting, Jerald R.

2015-01-01

Objectives: The goal of this study was to describe sleep patterns and accomplishment of daily life habits in children with Down syndrome (DS) and to investigate the relationship between subjective indicators of sleep disturbance with functional outcomes in daily life. Design: Cross-sectional study with an Internet sample Setting: Online survey filled out at home Participants: 110 parents of children with DS and 29 parents of children with typical development (TD), age 5 to 18 years. Interventions: N/A. Measurements and Results: Children's Sleep Habits Questionnaire was employed to collect information about sleep disturbances in 8 domains (subscales) and a total score. The Life Habits questionnaire (Life-H) sampled information about daily life habits in 11 domains. Multivariable regression modeling was used to assess the associations between sleep disturbances and the accomplishment of daily life habits. Sleep disordered breathing (SDB) was a significant explanatory factor in 10 of 11 daily life habits and the total Life-H score. Sleep anxiety and parasomnias significantly influenced the accomplishment of life habits in children with DS as compared to children with typical development. When evaluated in multivariable models in conjunction with the other 7 domains of sleep disturbances, SDB was the most dominant explanatory factor for accomplishment of life habits. Conclusions: Sleep disturbances are negatively related to accomplishment of daily life functions. Prevention and treatment of sleep problems, particularly sleep disordered breathing, in children with Down syndrome may lead to enhanced accomplishment of daily life habits and activities. Citation: Churchill SS, Kieckhefer GM, Bjornson KF, Herting JR. Relationship between sleep disturbance and functional outcomes in daily life habits of children with Down syndrome. SLEEP 2015;38(1):61–71. PMID:25325444

Political violence and child adjustment: longitudinal tests of sectarian antisocial behavior, family conflict, and insecurity as explanatory pathways.

PubMed

Cummings, Edward M; Merrilees, Christine E; Schermerhorn, Alice C; Goeke-Morey, Marcie C; Shirlow, Peter; Cairns, Ed

2012-01-01

Understanding the impact of political violence on child maladjustment is a matter of international concern. Recent research has advanced a social ecological explanation for relations between political violence and child adjustment. However, conclusions are qualified by the lack of longitudinal tests. Toward examining pathways longitudinally, mothers and their adolescents (M = 12.33, SD = 1.78, at Time 1) from 2-parent families in Catholic and Protestant working class neighborhoods in Belfast, Northern Ireland, completed measures assessing multiple levels of a social ecological model. Utilizing autoregressive controls, a 3-wave longitudinal model test (T1, n = 299; T2, n = 248; T3, n = 197) supported a specific pathway linking sectarian community violence, family conflict, children's insecurity about family relationships, and adjustment problems. © 2012 The Authors. Child Development © 2012 Society for Research in Child Development, Inc.

Pitfalls in statistical landslide susceptibility modelling

NASA Astrophysics Data System (ADS)

Schröder, Boris; Vorpahl, Peter; Märker, Michael; Elsenbeer, Helmut

2010-05-01

The use of statistical methods is a well-established approach to predict landslide occurrence probabilities and to assess landslide susceptibility. This is achieved by applying statistical methods relating historical landslide inventories to topographic indices as predictor variables. In our contribution, we compare several new and powerful methods developed in machine learning and well-established in landscape ecology and macroecology for predicting the distribution of shallow landslides in tropical mountain rainforests in southern Ecuador (among others: boosted regression trees, multivariate adaptive regression splines, maximum entropy). Although these methods are powerful, we think it is necessary to follow a basic set of guidelines to avoid some pitfalls regarding data sampling, predictor selection, and model quality assessment, especially if a comparison of different models is contemplated. We therefore suggest to apply a novel toolbox to evaluate approaches to the statistical modelling of landslide susceptibility. Additionally, we propose some methods to open the "black box" as an inherent part of machine learning methods in order to achieve further explanatory insights into preparatory factors that control landslides. Sampling of training data should be guided by hypotheses regarding processes that lead to slope failure taking into account their respective spatial scales. This approach leads to the selection of a set of candidate predictor variables considered on adequate spatial scales. This set should be checked for multicollinearity in order to facilitate model response curve interpretation. Model quality assesses how well a model is able to reproduce independent observations of its response variable. This includes criteria to evaluate different aspects of model performance, i.e. model discrimination, model calibration, and model refinement. In order to assess a possible violation of the assumption of independency in the training samples or a possible lack of explanatory information in the chosen set of predictor variables, the model residuals need to be checked for spatial auto¬correlation. Therefore, we calculate spline correlograms. In addition to this, we investigate partial dependency plots and bivariate interactions plots considering possible interactions between predictors to improve model interpretation. Aiming at presenting this toolbox for model quality assessment, we investigate the influence of strategies in the construction of training datasets for statistical models on model quality.

Spatial Patterns of Development Drive Water Use

NASA Astrophysics Data System (ADS)

Sanchez, G. M.; Smith, J. W.; Terando, A.; Sun, G.; Meentemeyer, R. K.

2018-03-01

Water availability is becoming more uncertain as human populations grow, cities expand into rural regions and the climate changes. In this study, we examine the functional relationship between water use and the spatial patterns of developed land across the rapidly growing region of the southeastern United States. We quantified the spatial pattern of developed land within census tract boundaries, including multiple metrics of density and configuration. Through non-spatial and spatial regression approaches we examined relationships and spatial dependencies between the spatial pattern metrics, socio-economic and environmental variables and two water use variables: a) domestic water use, and b) total development-related water use (a combination of public supply, domestic self-supply and industrial self-supply). Metrics describing the spatial patterns of development had the highest measure of relative importance (accounting for 53% of model's explanatory power), explaining significantly more variance in water use compared to socio-economic or environmental variables commonly used to estimate water use. Integrating metrics characterizing the spatial pattern of development into water use models is likely to increase their utility and could facilitate water-efficient land use planning.

A Theory-Based Model for Understanding Faculty Intention to Use Students Ratings to Improve Teaching in a Health Sciences Institution in Puerto Rico

ERIC Educational Resources Information Center

Collazo, Andrés A.

2018-01-01

A model derived from the theory of planned behavior was empirically assessed for understanding faculty intention to use student ratings for teaching improvement. A sample of 175 professors participated in the study. The model was statistically significant and had a very large explanatory power. Instrumental attitude, affective attitude, perceived…

Progressive Transitions from Algorithmic to Conceptual Understanding in Student Ability To Solve Chemistry Problems: A Lakatosian Interpretation.

ERIC Educational Resources Information Center

Niaz, Mansoor

The main objective of this study is to construct models based on strategies students use to solve chemistry problems and to show that these models form sequences of progressive transitions similar to what Lakatos (1970) in the history of science refers to as progressive 'problemshifts' that increase the explanatory' heuristic power of the models.…

Parenting Children With Borderline Intellectual Functioning: A Unique Risk Population

PubMed Central

Fenning, Rachel M.; Baker, Jason K.; Baker, Bruce L.; Crnic, Keith A.

2009-01-01

Parenting was examined among families of children with borderline intelligence in comparison to families of typically developing children and children with developmental delays. Parenting data were obtained at child age 5 via naturalistic home observation. Mothers of children with borderline intelligence exhibited less positive and less sensitive parenting behaviors than did other mothers and were least likely to display a style of positive engagement. Children with borderline intelligence were not observed to be more behaviorally problematic than other children; however, their mothers perceived more externalizing symptoms than did mothers of typically developing children. Findings suggest the importance of mothers’ explanatory models for child difficulties and highlight children with borderline intelligence as uniquely at risk for poor parenting. PMID:17295551

Model of Values-Based Management Process in Schools: A Mixed Design Study

ERIC Educational Resources Information Center

Dogan, Soner

2016-01-01

The aim of this paper is to evaluate the school administrators' values-based management behaviours according to the teachers' perceptions and opinions and, accordingly, to build a model of values-based management process in schools. The study was conducted using explanatory design which is inclusive of both quantitative and qualitative methods.…

Integrating Behavioral-Motive and Experiential-Requirement Perspectives on Psychological Needs: A Two Process Model

ERIC Educational Resources Information Center

Sheldon, Kennon M.

2011-01-01

Psychological need theories offer much explanatory potential for behavioral scientists, but there is considerable disagreement and confusion about what needs are and how they work. A 2-process model of psychological needs is outlined, viewing needs as evolved functional systems that provide both (a) innate psychosocial motives that tend to impel…

Using a Modeling Approach To Explore Scientific Epistemology with High School Biology Students. Research Report.

ERIC Educational Resources Information Center

Cartier, Jennifer

This paper describes a study of high school students' participation in the construction and revision of explanatory models as they attempted to account for a variety of inheritance phenomena observed in computer-generated "fruit flies". Throughout the course students were encouraged to explore epistemological issues related to the assessment and…

Improving Explanatory Inferences from Assessments

ERIC Educational Resources Information Center

Diakow, Ronli Phyllis

2013-01-01

This dissertation comprises three papers that propose, discuss, and illustrate models to make improved inferences about research questions regarding student achievement in education. Addressing the types of questions common in educational research today requires three different "extensions" to traditional educational assessment: (1)…

Determinants of Judgments of Explanatory Power: Credibility, Generality, and Statistical Relevance.

PubMed

Colombo, Matteo; Bucher, Leandra; Sprenger, Jan

2017-01-01

Explanation is a central concept in human psychology. Drawing upon philosophical theories of explanation, psychologists have recently begun to examine the relationship between explanation, probability and causality. Our study advances this growing literature at the intersection of psychology and philosophy of science by systematically investigating how judgments of explanatory power are affected by (i) the prior credibility of an explanatory hypothesis, (ii) the causal framing of the hypothesis, (iii) the perceived generalizability of the explanation, and (iv) the relation of statistical relevance between hypothesis and evidence. Collectively, the results of our five experiments support the hypothesis that the prior credibility of a causal explanation plays a central role in explanatory reasoning: first, because of the presence of strong main effects on judgments of explanatory power, and second, because of the gate-keeping role it has for other factors. Highly credible explanations are not susceptible to causal framing effects, but they are sensitive to the effects of normatively relevant factors: the generalizability of an explanation, and its statistical relevance for the evidence. These results advance current literature in the philosophy and psychology of explanation in three ways. First, they yield a more nuanced understanding of the determinants of judgments of explanatory power, and the interaction between these factors. Second, they show the close relationship between prior beliefs and explanatory power. Third, they elucidate the nature of abductive reasoning.

Determinants of Judgments of Explanatory Power: Credibility, Generality, and Statistical Relevance

PubMed Central

Colombo, Matteo; Bucher, Leandra; Sprenger, Jan

2017-01-01

Explanation is a central concept in human psychology. Drawing upon philosophical theories of explanation, psychologists have recently begun to examine the relationship between explanation, probability and causality. Our study advances this growing literature at the intersection of psychology and philosophy of science by systematically investigating how judgments of explanatory power are affected by (i) the prior credibility of an explanatory hypothesis, (ii) the causal framing of the hypothesis, (iii) the perceived generalizability of the explanation, and (iv) the relation of statistical relevance between hypothesis and evidence. Collectively, the results of our five experiments support the hypothesis that the prior credibility of a causal explanation plays a central role in explanatory reasoning: first, because of the presence of strong main effects on judgments of explanatory power, and second, because of the gate-keeping role it has for other factors. Highly credible explanations are not susceptible to causal framing effects, but they are sensitive to the effects of normatively relevant factors: the generalizability of an explanation, and its statistical relevance for the evidence. These results advance current literature in the philosophy and psychology of explanation in three ways. First, they yield a more nuanced understanding of the determinants of judgments of explanatory power, and the interaction between these factors. Second, they show the close relationship between prior beliefs and explanatory power. Third, they elucidate the nature of abductive reasoning. PMID:28928679

More Than Meets the Eye: Toward a Post-Materialist Model of Consciousness.

PubMed

Brabant, Olivier

2016-01-01

Commonly accepted models of human consciousness have substantial shortcomings, in the sense that they cannot account for the entire scope of human experiences. The goal of this article is to describe a model with higher explanatory power, by integrating ideas from psychology and quantum mechanics. In the first part, the need for a paradigm change will be justified by presenting three types of phenomena that challenge the materialistic view of consciousness. The second part is about proposing an alternative view of reality and mind-matter manifestation that is able to accommodate these phenomena. Finally, the ideas from the previous parts will be combined with the psychological concepts developed by Frederic W. H. Myers. The result is a more comprehensive model of human consciousness that offers a novel perspective on altered states of consciousness, genius, and mental health. Copyright © 2016 Elsevier Inc. All rights reserved.

Understanding Intention to Use Electronic Information Resources: A Theoretical Extension of the Technology Acceptance Model (TAM)

PubMed Central

Tao, Donghua

2008-01-01

This study extended the Technology Acceptance Model (TAM) by examining the roles of two aspects of e-resource characteristics, namely, information quality and system quality, in predicting public health students’ intention to use e-resources for completing research paper assignments. Both focus groups and a questionnaire were used to collect data. Descriptive analysis, data screening, and Structural Equation Modeling (SEM) techniques were used for data analysis. The study found that perceived usefulness played a major role in determining students’ intention to use e-resources. Perceived usefulness and perceived ease of use fully mediated the impact that information quality and system quality had on behavior intention. The research model enriches the existing technology acceptance literature by extending TAM. Representing two aspects of e-resource characteristics provides greater explanatory information for diagnosing problems of system design, development, and implementation. PMID:18999300

Understanding intention to use electronic information resources: A theoretical extension of the technology acceptance model (TAM).

PubMed

Tao, Donghua

2008-11-06

This study extended the Technology Acceptance Model (TAM) by examining the roles of two aspects of e-resource characteristics, namely, information quality and system quality, in predicting public health students' intention to use e-resources for completing research paper assignments. Both focus groups and a questionnaire were used to collect data. Descriptive analysis, data screening, and Structural Equation Modeling (SEM) techniques were used for data analysis. The study found that perceived usefulness played a major role in determining students' intention to use e-resources. Perceived usefulness and perceived ease of use fully mediated the impact that information quality and system quality had on behavior intention. The research model enriches the existing technology acceptance literature by extending TAM. Representing two aspects of e-resource characteristics provides greater explanatory information for diagnosing problems of system design, development, and implementation.

A computational model unifies apparently contradictory findings concerning phantom pain

PubMed Central

Boström, Kim J.; de Lussanet, Marc H. E.; Weiss, Thomas; Puta, Christian; Wagner, Heiko

2014-01-01

Amputation often leads to painful phantom sensations, whose pathogenesis is still unclear. Supported by experimental findings, an explanatory model has been proposed that identifies maladaptive reorganization of the primary somatosensory cortex (S1) as a cause of phantom pain. However, it was recently found that BOLD activity during voluntary movements of the phantom positively correlates with phantom pain rating, giving rise to a model of persistent representation. In the present study, we develop a physiologically realistic, computational model to resolve the conflicting findings. Simulations yielded that both the amount of reorganization and the level of cortical activity during phantom movements were enhanced in a scenario with strong phantom pain as compared to a scenario with weak phantom pain. These results suggest that phantom pain, maladaptive reorganization, and persistent representation may all be caused by the same underlying mechanism, which is driven by an abnormally enhanced spontaneous activity of deafferented nociceptive channels. PMID:24931344

Understanding transparency perception in architecture: presentation of the simplified perforated model.

PubMed

Brzezicki, Marcin

2013-01-01

Issues of transparency perception are addressed from an architectural perspective, pointing out previously neglected factors that greatly influence this phenomenon in the scale of a building. The simplified perforated model of a transparent surface presented in the paper has been based on previously developed theories and involves the balance of light reflected versus light transmitted. Its aim is to facilitate an understanding of non-intuitive phenomena related to transparency (eg dynamically changing reflectance) for readers without advanced knowledge of molecular physics. A verification of the presented model has been based on the comparison of optical performance of the model with the results of Fresnel's equations for light-transmitting materials. The presented methodology is intended to be used both in the design and explanatory stages of architectural practice and vision research. Incorporation of architectural issues could enrich the perspective of scientists representing other disciplines.

A data-centric approach to understanding the pricing of financial options

NASA Astrophysics Data System (ADS)

Healy, J.; Dixon, M.; Read, B.; Cai, F. F.

2002-05-01

We investigate what can be learned from a purely phenomenological study of options prices without modelling assumptions. We fitted neural net (NN) models to LIFFE ``ESX'' European style FTSE 100 index options using daily data from 1992 to 1997. These non-parametric models reproduce the Black-Scholes (BS) analytic model in terms of fit and performance measures using just the usual five inputs (S, X, t, r, IV). We found that adding transaction costs (bid-ask spread) to these standard five parameters gives a comparable fit and performance. Tests show that the bid-ask spread can be a statistically significant explanatory variable for option prices. The difference in option prices between the models with transaction costs and those without ranges from about -3.0 to +1.5 index points, varying with maturity date. However, the difference depends on the moneyness (S/X), being greatest in-the-money. This suggests that use of a five-factor model can result in a pricing difference of up to #10 to #30 per call option contract compared with modelling under transaction costs. We found that the influence of transaction costs varied between different yearly subsets of the data. Open interest is also a significant explanatory variable, but volume is not.

Developing a methodology to predict PM10 concentrations in urban areas using generalized linear models.

PubMed

Garcia, J M; Teodoro, F; Cerdeira, R; Coelho, L M R; Kumar, Prashant; Carvalho, M G

2016-09-01

A methodology to predict PM10 concentrations in urban outdoor environments is developed based on the generalized linear models (GLMs). The methodology is based on the relationship developed between atmospheric concentrations of air pollutants (i.e. CO, NO2, NOx, VOCs, SO2) and meteorological variables (i.e. ambient temperature, relative humidity (RH) and wind speed) for a city (Barreiro) of Portugal. The model uses air pollution and meteorological data from the Portuguese monitoring air quality station networks. The developed GLM considers PM10 concentrations as a dependent variable, and both the gaseous pollutants and meteorological variables as explanatory independent variables. A logarithmic link function was considered with a Poisson probability distribution. Particular attention was given to cases with air temperatures both below and above 25°C. The best performance for modelled results against the measured data was achieved for the model with values of air temperature above 25°C compared with the model considering all ranges of air temperatures and with the model considering only temperature below 25°C. The model was also tested with similar data from another Portuguese city, Oporto, and results found to behave similarly. It is concluded that this model and the methodology could be adopted for other cities to predict PM10 concentrations when these data are not available by measurements from air quality monitoring stations or other acquisition means.

Understanding What Is Possible across a Career: Professional Identity Development beyond Transition to Teaching

ERIC Educational Resources Information Center

Mahmoudi-Gahrouei, Vahid; Tavakoli, Mansoor; Hamman, Doug

2016-01-01

Professional identity surfaces repeatedly as an important underlying factor in teacher development. A sequential explanatory mixed methods design was used to investigate identity development in terms of teachers' expected and feared possible selves. Teachers (n = 120) representing three career groups (prospect, new, and experienced) participated.…

32 CFR Attachment 3 to Part 855 - Landing Permit Application Instructions

Code of Federal Regulations, 2010 CFR

2010-07-01

....13. Block 9c, Title. Self-explanatory. A3.1.14. Block 9d, Telephone Number. Self-explanatory. A3.1.15.... Block 3. Self-explanatory. (Users will not necessarily be denied landing rights if pilots are not... requested, it may be approved if warranted by unique circumstances. (The verification specified for each...

Use of Continuous Monitors and Autosamplers to Predict Unmeasured Water-Quality Constituents in Tributaries of the Tualatin River, Oregon

USGS Publications Warehouse

Anderson, Chauncey W.; Rounds, Stewart A.

2010-01-01

Management of water quality in streams of the United States is becoming increasingly complex as regulators seek to control aquatic pollution and ecological problems through Total Maximum Daily Load programs that target reductions in the concentrations of certain constituents. Sediment, nutrients, and bacteria, for example, are constituents that regulators target for reduction nationally and in the Tualatin River basin, Oregon. These constituents require laboratory analysis of discrete samples for definitive determinations of concentrations in streams. Recent technological advances in the nearly continuous, in situ monitoring of related water-quality parameters has fostered the use of these parameters as surrogates for the labor intensive, laboratory-analyzed constituents. Although these correlative techniques have been successful in large rivers, it was unclear whether they could be applied successfully in tributaries of the Tualatin River, primarily because these streams tend to be small, have rapid hydrologic response to rainfall and high streamflow variability, and may contain unique sources of sediment, nutrients, and bacteria. This report evaluates the feasibility of developing correlative regression models for predicting dependent variables (concentrations of total suspended solids, total phosphorus, and Escherichia coli bacteria) in two Tualatin River basin streams: one draining highly urbanized land (Fanno Creek near Durham, Oregon) and one draining rural agricultural land (Dairy Creek at Highway 8 near Hillsboro, Oregon), during 2002-04. An important difference between these two streams is their response to storm runoff; Fanno Creek has a relatively rapid response due to extensive upstream impervious areas and Dairy Creek has a relatively slow response because of the large amount of undeveloped upstream land. Four other stream sites also were evaluated, but in less detail. Potential explanatory variables included continuously monitored streamflow (discharge), stream stage, specific conductance, turbidity, and time (to account for seasonal processes). Preliminary multiple-regression models were identified using stepwise regression and Mallow's Cp, which maximizes regression correlation coefficients and accounts for the loss of additional degrees of freedom when extra explanatory variables are used. Several data scenarios were created and evaluated for each site to assess the representativeness of existing monitoring data and autosampler-derived data, and to assess the utility of the available data to develop robust predictive models. The goodness-of-fit of candidate predictive models was assessed with diagnostic statistics from validation exercises that compared predictions against a subset of the available data. The regression modeling met with mixed success. Functional model forms that have a high likelihood of success were identified for most (but not all) dependent variables at each site, but there were limitations in the available datasets, notably the lack of samples from high-flows. These limitations increase the uncertainty in the predictions of the models and suggest that the models are not yet ready for use in assessing these streams, particularly under high-flow conditions, without additional data collection and recalibration of model coefficients. Nonetheless, the results reveal opportunities to use existing resources more efficiently. Baseline conditions are well represented in the available data, and, for the most part, the models reproduced these conditions well. Future sampling might therefore focus on high flow conditions, without much loss of ability to characterize the baseline. Seasonal cycles, as represented by trigonometric functions of time, were not significant in the evaluated models, perhaps because the baseline conditions are well characterized in the datasets or because the other explanatory variables indirectly incorporate seasonal aspects. Multicollinearity among independent variabl

Tourette Syndrome and Tic Disorders

PubMed Central

Leckman, James F.

2005-01-01

Objective: This is a practical review of Tourette syndrome, including phenomenology, natural history, and state-of-the-art assessment and treatment. Method: Computerized literature searches were conducted under the keywords Tourette syndrome,tics, and children-adolescents. Results: Studies have documented the natural history of Tourette syndrome and its frequent co-occurrence with attention problems, obsessive-compulsive disorder (OCD), and a range of other mood and anxiety disorders, which are often of primary concern to patients and their families. Proper diagnosis and education are often very helpful for patients, parents, siblings, teachers, and peers. When necessary, available anti-tic treatments have proven efficacious. First-line options include the alpha adrenergic agents and the atypical neuroleptics, as well as behavioral interventions such as habit reversal. Conclusions: The study of tics and Tourette symdrome has led to the development of several pathophysiological models and helped in the development of management options. However, fully explanatory models are still needed that would allow for accurate prognostication in the course of illness and the development of improved treatments. PMID:21152158

Prediction of municipal solid waste generation using nonlinear autoregressive network.

PubMed

Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; Maulud, K N A

2015-12-01

Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.

Insight in psychosis: an independent predictor of outcome or an explanatory model of illness?

PubMed

Jacob, K S

2014-10-01

While the traditional view within psychiatry is that insight is independent of psychopathology and predicts the course and outcome of psychosis, recent data from India argues that insight is secondary to interaction between progression of illness on one hand and local culture and social environment on the other. The findings suggest that "insight" is an explanatory model (EM) and may reflect attempts at coping with the devastating effects of mental disorders. Most societies are pluralistic and offer multiple, divergent and contradictory explanations for illnesses. These belief systems interact with the trajectory of the person's illness to produce a unique personal understanding, often based on a set of complex and contradictory EMs. Like all EMs, insight provides meaning to explain and overcome challenges including disabling symptoms, persistent deficits, impaired social relations and difficult livelihood issues. The persistence of distress, impairment, disability and handicap, despite regular and optimal treatment, call for explanations, which go beyond the simplistic concept of disease. People tend to choose EMs, which are non-stigmatizing and which seem to help explain and rationalize their individual concerns. The frequent presence of multiple and often contradictory EMs, held simultaneously, suggest that they are pragmatic responses at coping. The results advocate a non-judgmental approach and broad based assessment of EMs of illness and their comparison with culturally appropriate beliefs, attributions and actions. The biomedical model of illness should be presented without dismissing patient beliefs or belittling local cultural explanations for illness. Clinical practice demands a negotiation of shared model of care and treatment plan between patient and physician perspectives. The diversity of patients, problems, beliefs and cultures mandates the need to educate, match, negotiate and integrate psychiatric and psychological frameworks and interventions. It calls for multifaceted and nuanced understanding of "insight" and explanatory models of illness. Copyright © 2014 Elsevier B.V. All rights reserved.

Attachment Security Balances Perspectives: Effects of Security Priming on Highly Optimistic and Pessimistic Explanatory Styles.

PubMed

Deng, Yanhe; Yan, Mengge; Chen, Henry; Sun, Xin; Zhang, Peng; Zeng, Xianglong; Liu, Xiangping; Lye, Yue

2016-01-01

Highly optimistic explanatory style (HOES) and highly pessimistic explanatory style (HPES) are two maladaptive ways to explain the world and may have roots in attachment insecurity. The current study aims to explore the effects of security priming - activating supportive representations of attachment security - on ameliorating these maladaptive explanatory styles. 57 participants with HOES and 57 participants with HPES were randomized into security priming and control conditions. Their scores of overall optimistic attribution were measured before and after priming. Security priming had a moderating effect: the security primed HOES group exhibited lower optimistic attribution, while the security primed HPES group evinced higher scores of optimistic attribution. Furthermore, the security primed HOES group attributed positive outcomes more externally, while the security primed HPES group attributed successful results more internally. The results support the application of security priming interventions on maladaptive explanatory styles. Its potential mechanism and directions for future study are also discussed.

Multivariate dynamic Tobit models with lagged observed dependent variables: An effectiveness analysis of highway safety laws.

PubMed

Dong, Chunjiao; Xie, Kun; Zeng, Jin; Li, Xia

2018-04-01

Highway safety laws aim to influence driver behaviors so as to reduce the frequency and severity of crashes, and their outcomes. For one specific highway safety law, it would have different effects on the crashes across severities. Understanding such effects can help policy makers upgrade current laws and hence improve traffic safety. To investigate the effects of highway safety laws on crashes across severities, multivariate models are needed to account for the interdependency issues in crash counts across severities. Based on the characteristics of the dependent variables, multivariate dynamic Tobit (MVDT) models are proposed to analyze crash counts that are aggregated at the state level. Lagged observed dependent variables are incorporated into the MVDT models to account for potential temporal correlation issues in crash data. The state highway safety law related factors are used as the explanatory variables and socio-demographic and traffic factors are used as the control variables. Three models, a MVDT model with lagged observed dependent variables, a MVDT model with unobserved random variables, and a multivariate static Tobit (MVST) model are developed and compared. The results show that among the investigated models, the MVDT models with lagged observed dependent variables have the best goodness-of-fit. The findings indicate that, compared to the MVST, the MVDT models have better explanatory power and prediction accuracy. The MVDT model with lagged observed variables can better handle the stochasticity and dependency in the temporal evolution of the crash counts and the estimated values from the model are closer to the observed values. The results show that more lives could be saved if law enforcement agencies can make a sustained effort to educate the public about the importance of motorcyclists wearing helmets. Motor vehicle crash-related deaths, injuries, and property damages could be reduced if states enact laws for stricter text messaging rules, higher speeding fines, older licensing age, and stronger graduated licensing provisions. Injury and PDO crashes would be significantly reduced with stricter laws prohibiting the use of hand-held communication devices and higher fines for drunk driving. Copyright © 2018 Elsevier Ltd. All rights reserved.

Human vs. Computer Diagnosis of Students' Natural Selection Knowledge: Testing the Efficacy of Text Analytic Software

NASA Astrophysics Data System (ADS)

Nehm, Ross H.; Haertig, Hendrik

2012-02-01

Our study examines the efficacy of Computer Assisted Scoring (CAS) of open-response text relative to expert human scoring within the complex domain of evolutionary biology. Specifically, we explored whether CAS can diagnose the explanatory elements (or Key Concepts) that comprise undergraduate students' explanatory models of natural selection with equal fidelity as expert human scorers in a sample of >1,000 essays. We used SPSS Text Analysis 3.0 to perform our CAS and measure Kappa values (inter-rater reliability) of KC detection (i.e., computer-human rating correspondence). Our first analysis indicated that the text analysis functions (or extraction rules) developed and deployed in SPSSTA to extract individual Key Concepts (KCs) from three different items differing in several surface features (e.g., taxon, trait, type of evolutionary change) produced "substantial" (Kappa 0.61-0.80) or "almost perfect" (0.81-1.00) agreement. The second analysis explored the measurement of human-computer correspondence for KC diversity (the number of different accurate knowledge elements) in the combined sample of all 827 essays. Here we found outstanding correspondence; extraction rules generated using one prompt type are broadly applicable to other evolutionary scenarios (e.g., bacterial resistance, cheetah running speed, etc.). This result is encouraging, as it suggests that the development of new item sets may not necessitate the development of new text analysis rules. Overall, our findings suggest that CAS tools such as SPSS Text Analysis may compensate for some of the intrinsic limitations of currently used multiple-choice Concept Inventories designed to measure student knowledge of natural selection.

An explanatory model of academic achievement based on aptitudes, goal orientations, self-concept and learning strategies.

PubMed

Miñano Pérez, Pablo; Castejón Costa, Juan-Luis; Gilar Corbí, Raquel

2012-03-01

As a result of studies examining factors involved in the learning process, various structural models have been developed to explain the direct and indirect effects that occur between the variables in these models. The objective was to evaluate a structural model of cognitive and motivational variables predicting academic achievement, including general intelligence, academic self-concept, goal orientations, effort and learning strategies. The sample comprised of 341 Spanish students in the first year of compulsory secondary education. Different tests and questionnaires were used to evaluate each variable, and Structural Equation Modelling (SEM) was applied to contrast the relationships of the initial model. The model proposed had a satisfactory fit, and all the hypothesised relationships were significant. General intelligence was the variable most able to explain academic achievement. Also important was the direct influence of academic self-concept on achievement, goal orientations and effort, as well as the mediating ability of effort and learning strategies between academic goals and final achievement.

Palm oil price forecasting model: An autoregressive distributed lag (ARDL) approach

NASA Astrophysics Data System (ADS)

Hamid, Mohd Fahmi Abdul; Shabri, Ani

2017-05-01

Palm oil price fluctuated without any clear trend or cyclical pattern in the last few decades. The instability of food commodities price causes it to change rapidly over time. This paper attempts to develop Autoregressive Distributed Lag (ARDL) model in modeling and forecasting the price of palm oil. In order to use ARDL as a forecasting model, this paper modifies the data structure where we only consider lagged explanatory variables to explain the variation in palm oil price. We then compare the performance of this ARDL model with a benchmark model namely ARIMA in term of their comparative forecasting accuracy. This paper also utilize ARDL bound testing approach to co-integration in examining the short run and long run relationship between palm oil price and its determinant; production, stock, and price of soybean as the substitute of palm oil and price of crude oil. The comparative forecasting accuracy suggests that ARDL model has a better forecasting accuracy compared to ARIMA.

Ordinal probability effect measures for group comparisons in multinomial cumulative link models.

PubMed

Agresti, Alan; Kateri, Maria

2017-03-01

We consider simple ordinal model-based probability effect measures for comparing distributions of two groups, adjusted for explanatory variables. An "ordinal superiority" measure summarizes the probability that an observation from one distribution falls above an independent observation from the other distribution, adjusted for explanatory variables in a model. The measure applies directly to normal linear models and to a normal latent variable model for ordinal response variables. It equals Φ(β/2) for the corresponding ordinal model that applies a probit link function to cumulative multinomial probabilities, for standard normal cdf Φ and effect β that is the coefficient of the group indicator variable. For the more general latent variable model for ordinal responses that corresponds to a linear model with other possible error distributions and corresponding link functions for cumulative multinomial probabilities, the ordinal superiority measure equals exp(β)/[1+exp(β)] with the log-log link and equals approximately exp(β/2)/[1+exp(β/2)] with the logit link, where β is the group effect. Another ordinal superiority measure generalizes the difference of proportions from binary to ordinal responses. We also present related measures directly for ordinal models for the observed response that need not assume corresponding latent response models. We present confidence intervals for the measures and illustrate with an example. © 2016, The International Biometric Society.

Towards a neuro-computational account of prism adaptation.

PubMed

Petitet, Pierre; O'Reilly, Jill X; O'Shea, Jacinta

2017-12-14

Prism adaptation has a long history as an experimental paradigm used to investigate the functional and neural processes that underlie sensorimotor control. In the neuropsychology literature, prism adaptation behaviour is typically explained by reference to a traditional cognitive psychology framework that distinguishes putative functions, such as 'strategic control' versus 'spatial realignment'. This theoretical framework lacks conceptual clarity, quantitative precision and explanatory power. Here, we advocate for an alternative computational framework that offers several advantages: 1) an algorithmic explanatory account of the computations and operations that drive behaviour; 2) expressed in quantitative mathematical terms; 3) embedded within a principled theoretical framework (Bayesian decision theory, state-space modelling); 4) that offers a means to generate and test quantitative behavioural predictions. This computational framework offers a route towards mechanistic neurocognitive explanations of prism adaptation behaviour. Thus it constitutes a conceptual advance compared to the traditional theoretical framework. In this paper, we illustrate how Bayesian decision theory and state-space models offer principled explanations for a range of behavioural phenomena in the field of prism adaptation (e.g. visual capture, magnitude of visual versus proprioceptive realignment, spontaneous recovery and dynamics of adaptation memory). We argue that this explanatory framework can advance understanding of the functional and neural mechanisms that implement prism adaptation behaviour, by enabling quantitative tests of hypotheses that go beyond merely descriptive mapping claims that 'brain area X is (somehow) involved in psychological process Y'. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Asessing for Structural Understanding in Childrens' Combinatorial Problem Solving.

ERIC Educational Resources Information Center

English, Lyn

1999-01-01

Assesses children's structural understanding of combinatorial problems when presented in a variety of task situations. Provides an explanatory model of students' combinatorial understandings that informs teaching and assessment. Addresses several components of children's structural understanding of elementary combinatorial problems. (Contains 50…

ASSESSING ACCURACY OF NET CHANGE DERIVED FROM LAND COVER MAPS

EPA Science Inventory

Net change derived from land-cover maps provides important descriptive information for environmental monitoring and is often used as an input or explanatory variable in environmental models. The sampling design and analysis for assessing net change accuracy differ from traditio...

Complexity: A Frontier for Management Education

ERIC Educational Resources Information Center

Axley, Stephen R.; McMahon, Timothy R.

2006-01-01

This article critiques the mechanistic grounding of traditional management education and proposes complexity science as a fitting explanatory model for an age of complexity, contributing timely and important educational content and instructional processes to management education. It highlights some of those contributions and reviews instructional…

Spatial patterns of development drive water use

USGS Publications Warehouse

Sanchez, G.M.; Smith, J.W.; Terando, Adam J.; Sun, G.; Meentemeyer, R.K.

2018-01-01

Water availability is becoming more uncertain as human populations grow, cities expand into rural regions and the climate changes. In this study, we examine the functional relationship between water use and the spatial patterns of developed land across the rapidly growing region of the southeastern United States. We quantified the spatial pattern of developed land within census tract boundaries, including multiple metrics of density and configuration. Through non‐spatial and spatial regression approaches we examined relationships and spatial dependencies between the spatial pattern metrics, socio‐economic and environmental variables and two water use variables: a) domestic water use, and b) total development‐related water use (a combination of public supply, domestic self‐supply and industrial self‐supply). Metrics describing the spatial patterns of development had the highest measure of relative importance (accounting for 53% of model's explanatory power), explaining significantly more variance in water use compared to socio‐economic or environmental variables commonly used to estimate water use. Integrating metrics characterizing the spatial pattern of development into water use models is likely to increase their utility and could facilitate water‐efficient land use planning.

Preliminary Evolutionary Explanations: A Basic Framework for Conceptual Change and Explanatory Coherence in Evolution

NASA Astrophysics Data System (ADS)

Kampourakis, Kostas; Zogza, Vasso

2009-10-01

This study aimed to explore secondary students’ explanations of evolutionary processes, and to determine how consistent these were, after a specific evolution instruction. In a previous study it was found that before instruction students provided different explanations for similar processes to tasks with different content. Hence, it seemed that the structure and the content of the task may have had an effect on students’ explanations. The tasks given to students demanded evolutionary explanations, in particular explanations for the origin of homologies and adaptations. Based on the conclusions from the previous study, we developed a teaching sequence in order to overcome students’ preconceptions, as well as to achieve conceptual change and explanatory coherence. Students were taught about fundamental biological concepts and the several levels of biological organization, as well as about the mechanisms of heredity and of the origin of genetic variation. Then, all these concepts were used to teach about evolution, by relating micro-concepts (e.g. genotypes) to macro-concepts (e.g. phenotypes). Moreover, during instruction students were brought to a conceptual conflict situation, where their intuitive explanations were challenged as emphasis was put on two concepts entirely opposed to their preconceptions: chance and unpredictability. From the explanations that students provided in the post-test it is concluded that conceptual change and explanatory coherence in evolution can be achieved to a certain degree by lower secondary school students through the suggested teaching sequence and the explanatory framework, which may form a basis for teaching further about evolution.

Laboratory-Measured and Property-Transfer Modeled Saturated Hydraulic Conductivity of Snake River Plain Aquifer Sediments at the Idaho National Laboratory, Idaho

USGS Publications Warehouse

Perkins, Kim S.

2008-01-01

Sediments are believed to comprise as much as 50 percent of the Snake River Plain aquifer thickness in some locations within the Idaho National Laboratory. However, the hydraulic properties of these deep sediments have not been well characterized and they are not represented explicitly in the current conceptual model of subregional scale ground-water flow. The purpose of this study is to evaluate the nature of the sedimentary material within the aquifer and to test the applicability of a site-specific property-transfer model developed for the sedimentary interbeds of the unsaturated zone. Saturated hydraulic conductivity (Ksat) was measured for 10 core samples from sedimentary interbeds within the Snake River Plain aquifer and also estimated using the property-transfer model. The property-transfer model for predicting Ksat was previously developed using a multiple linear-regression technique with bulk physical-property measurements (bulk density [pbulk], the median particle diameter, and the uniformity coefficient) as the explanatory variables. The model systematically underestimates Ksat,typically by about a factor of 10, which likely is due to higher bulk-density values for the aquifer samples compared to the samples from the unsaturated zone upon which the model was developed. Linear relations between the logarithm of Ksat and pbulk also were explored for comparison.

Pro-anorexia, weight-loss drugs and the internet: an "anti-recovery" explanatory model of anorexia.

PubMed

Fox, Nick; Ward, Katie; O'Rourke, Alan

2005-11-01

This paper explores the online "pro-anorexia" underground, a movement that supports those with anorexia and adopts an "anti-recovery" perspective on the disease. While encouraging a "healthy" diet to sustain an anorexic way-of-life, the movement also recommends the radical use of weight-loss pharmaceuticals to pursue and maintain low body weight, in contrast to their conventional use to treat obesity. Using ethnographic and interview data collected from participants in the "Anagrrl" website and online forum, we analyse the pro-anorexia (or "pro-ana") movement in terms of its underlying "explanatory model" of the disease, and contrast it with medical, psychosocial, sociocultural and feminist models that encourage a "normalisation" of body shape and weight. We suggest that for participants in pro-ana, anorexia represents stability and control, and Anagrrl offers support and guidance for those who wish to remain in this "sanctuary". We discuss the pro-anorexia movement's use of the internet to facilitate resistance to medical and social theories of disease, and its subversion of pharmaceutical technologies.

Independent Assessment Plan: LAV-25

DTIC Science & Technology

1989-06-27

Pages. Enter the total Block 7. Performing Organization Name(s) and number of pages. Address(es. Self -explanatory. Block 16. Price Code, Enter...organization Blocks 17. - 19. Security Classifications. performing the report. Self -explanatory. Enter U.S. Security Classification in accordance with U.S...Security Block 9. S oonsorina/Monitoring Acenc Regulations (i.e., UNCLASSIFIED). If form .Names(s) and Address(es). Self -explanatory. contains classified

[Poison and the mosquito: epistemological aspects of the etiology and prophylactics of yellow fever].

PubMed

Caponi, S

2000-01-01

The strategies against yellow fever developed by Argentina and Brazil were discussed at the Second Medical Congress of Latin America which was held in Buenos Aires in 1904. The study of the controversy between physicians from Argentina and Brazil around the existing explanatory models of this illness and the international prophylactic strategies in use at the time enables an epistemological understanding of the breakthrough brought about by the emergence of medicine of vectors. This chapter of Latin American medicine history constitutes a unique opportunity to analyze that reorganization of knowledge, which permitted the inclusion of intermediary living beings into the medical and epidemiological discourse.

Online Statistical Modeling (Regression Analysis) for Independent Responses

NASA Astrophysics Data System (ADS)

Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus

2017-06-01

Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.

Temporal self-regulation theory: a neurobiologically informed model for physical activity behavior

PubMed Central

Hall, Peter A.; Fong, Geoffrey T.

2015-01-01

Dominant explanatory models for physical activity behavior are limited by the exclusion of several important components, including temporal dynamics, ecological forces, and neurobiological factors. The latter may be a critical omission, given the relevance of several aspects of cognitive function for the self-regulatory processes that are likely required for consistent implementation of physical activity behavior in everyday life. This narrative review introduces temporal self-regulation theory (TST; Hall and Fong, 2007, 2013) as a new explanatory model for physical activity behavior. Important features of the model include consideration of the default status of the physical activity behavior, as well as the disproportionate influence of temporally proximal behavioral contingencies. Most importantly, the TST model proposes positive feedback loops linking executive function (EF) and the performance of physical activity behavior. Specifically, those with relatively stronger executive control (and optimized brain structures supporting it, such as the dorsolateral prefrontal cortex (PFC)) are able to implement physical activity with more consistency than others, which in turn serves to strengthen the executive control network itself. The TST model has the potential to explain everyday variants of incidental physical activity, sport-related excellence via capacity for deliberate practice, and variability in the propensity to schedule and implement exercise routines. PMID:25859196

Examining Development of Curriculum Knowledge of Prospective Mathematics Teachers

ERIC Educational Resources Information Center

Sahin, Ömer; Soylu, Yasin

2017-01-01

Explanatory-confirmatory research design, one of the mixed methods research designs, was used in this study to investigate Curriculum Knowledge developments of prospective teachers regarding algebra. Cross-sectional study method, as a type of descriptive research and one of the non-experimental research designs, was used to collect quantitative…

The Science ELF: Assessing the Enquiry Levels Framework as a Heuristic for Professional Development

ERIC Educational Resources Information Center

Wheeler, Lindsay B.; Bell, Randy L.; Whitworth, Brooke A.; Maeng, Jennifer L.

2015-01-01

This study utilized an explanatory sequential mixed methods approach to explore randomly assigned treatment and control participants' frequency of inquiry instruction in secondary science classrooms. Eleven treatment participants received professional development (PD) that emphasized a structured approach to inquiry instruction, while 10 control…

Online Challenge versus Offline ACT

ERIC Educational Resources Information Center

Peckham, Irvin

2010-01-01

This article compares essays written in response to the ACT Essay prompt and a locally developed prompt used for placement. The two writing situations differ by time and genre: the ACT Essay is timed and argumentative; the locally developed is untimed and explanatory. The article analyzes the differences in student performance and predictive…

Marital Conflict and Children's Emotional Security in the Context of Parental Depression

ERIC Educational Resources Information Center

Kouros, Chrystyna D.; Merrilees, Christine E.; Cummings, E. Mark

2008-01-01

Evidence has emerged for emotional security as an explanatory variable linking marital conflict to children's adjustment. Further evidence suggests parental psychopathology is a key factor in child development. To advance understanding of the pathways by which these family risk factors impact children's development, the mediational role of…

Identity processes and the positive youth development of African Americans: an explanatory framework.

PubMed

Swanson, Dena Phillips; Spencer, Margaret Beale; Dell'Angelo, Tabitha; Harpalani, Vinay; Spencer, Tirzah R

2002-01-01

This chapter presents Spencer's phenomenological variant of ecological systems theory, or PVEST (1995), as a conceptual framework for examining positive youth development. Contextual factors affecting racial and gender identity of African American youth are discussed, with the focus on the influence of schools and religious institutions.

The Promise of Dynamic Systems Approaches for an Integrated Account of Human Development.

ERIC Educational Resources Information Center

Lewis, Marc D.

2000-01-01

Argues that dynamic systems approaches may provide an explanatory framework based on general scientific principles for developmental psychology, using principles of self-organization to explain how novel forms emerge without predetermination and become increasingly complex with development. Contends that self-organization provides a single…

The Relationship between Building Teacher Leadership Capacity and Campus Culture

ERIC Educational Resources Information Center

Harris, Dawn R.; Kemp-Graham, Kriss Y.

2017-01-01

The purpose of this explanatory sequential mixed methods research study was to explore the relationship between building teacher leadership capacity and campus culture in a suburban East Texas school district. Developing teacher leaders by building leadership capacity depends on administrators' abilities to develop leaders from within the existing…

'Food Sticking in My Throat': Videofluoroscopic Evaluation of a Common Symptom.

PubMed

Madhavan, Aarthi; Carnaby, Giselle D; Crary, Michael A

2015-06-01

Prevalence of the symptom of food 'sticking' during swallowing has been reported to range from 5 to 50%, depending on the assessment setting. However, limited objective evidence has emerged to clarify factors that contribute to this symptom. Three hundred and fifteen patient records from an outpatient dysphagia clinic were reviewed to identify patients with symptoms of 'food sticking in the throat.' Corresponding videofluoroscopic swallowing studies for patients with this complaint were reviewed for the following variables: accuracy of symptom localization, identification and characteristics (anatomic, physiologic) of an explanatory cause for the symptom, and the specific swallowed material that identified the explanatory cause. One hundred and forty one patients (45%) were identified with a complaint of food 'sticking' in their throat during swallowing. Prevalence of explanatory findings on fluoroscopy was 76% (107/141). Eighty five percent (91/107) of explanatory causes were physiologic in nature, while 15% (16/107) were anatomic. The majority of explanatory causes were identified in the esophagus (71%). Symptom localization was more accurate when the explanatory cause was anatomic versus physiologic (75 vs. 18%). A non-masticated marshmallow presented with the highest diagnostic yield in identification of explanatory causes (71%). Patients complaining of 'food sticking in the throat' are likely to present with esophageal irregularities. Thus, imaging studies of swallowing function should include the esophagus. A range of materials, including a non-masticated marshmallow, is helpful in determining the location and characteristics of swallowing deficits contributing to this symptom.

Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations.

PubMed

Gerber, Brian D; Kendall, William L; Hooten, Mevin B; Dubovsky, James A; Drewien, Roderick C

2015-09-01

1. Prediction is fundamental to scientific enquiry and application; however, ecologists tend to favour explanatory modelling. We discuss a predictive modelling framework to evaluate ecological hypotheses and to explore novel/unobserved environmental scenarios to assist conservation and management decision-makers. We apply this framework to develop an optimal predictive model for juvenile (<1 year old) sandhill crane Grus canadensis recruitment of the Rocky Mountain Population (RMP). We consider spatial climate predictors motivated by hypotheses of how drought across multiple time-scales and spring/summer weather affects recruitment. 2. Our predictive modelling framework focuses on developing a single model that includes all relevant predictor variables, regardless of collinearity. This model is then optimized for prediction by controlling model complexity using a data-driven approach that marginalizes or removes irrelevant predictors from the model. Specifically, we highlight two approaches of statistical regularization, Bayesian least absolute shrinkage and selection operator (LASSO) and ridge regression. 3. Our optimal predictive Bayesian LASSO and ridge regression models were similar and on average 37% superior in predictive accuracy to an explanatory modelling approach. Our predictive models confirmed a priori hypotheses that drought and cold summers negatively affect juvenile recruitment in the RMP. The effects of long-term drought can be alleviated by short-term wet spring-summer months; however, the alleviation of long-term drought has a much greater positive effect on juvenile recruitment. The number of freezing days and snowpack during the summer months can also negatively affect recruitment, while spring snowpack has a positive effect. 4. Breeding habitat, mediated through climate, is a limiting factor on population growth of sandhill cranes in the RMP, which could become more limiting with a changing climate (i.e. increased drought). These effects are likely not unique to cranes. The alteration of hydrological patterns and water levels by drought may impact many migratory, wetland nesting birds in the Rocky Mountains and beyond. 5. Generalizable predictive models (trained by out-of-sample fit and based on ecological hypotheses) are needed by conservation and management decision-makers. Statistical regularization improves predictions and provides a general framework for fitting models with a large number of predictors, even those with collinearity, to simultaneously identify an optimal predictive model while conducting rigorous Bayesian model selection. Our framework is important for understanding population dynamics under a changing climate and has direct applications for making harvest and habitat management decisions. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Buying a Better Air Force

DTIC Science & Technology

2006-03-01

identify if an explanatory variable may have been omitted due to model misspecification ( Ramsey , 1979). The RESET test resulted in failure to...Prob > F 0.0094 This model was also regressed using Huber-White estimators. Again, the Ramsey RESET test was done to ensure relevant...Aircraft. Annapolis, MD: Naval Institute Press, 2004. Ramsey , J. B. “ Tests for Specification Errors in Classical Least-Squares Regression Analysis

The Influence of Self-Efficacy and Motivational Factors on Academic Performance in General Chemistry Course: A Modeling Study

ERIC Educational Resources Information Center

Alci, Bulent

2015-01-01

This study aims to determine the predictive and explanatory model in terms of university students' academic performance in "General Chemistry" course and their motivational features. The participants were 169 university students in the 1st grade at university. Of the participants, 132 were female and 37 were male students. Regarding…

Why Is Childhood Maltreatment Associated with Adolescent Substance Abuse? A Critical Review of Explanatory Models

ERIC Educational Resources Information Center

Hovdestad, Wendy E.; Tonmyr, Lil; Wekerle, Christine; Thornton, Tiffany

2011-01-01

Childhood maltreatment and adolescent substance abuse are important health issues that have been linked by research and theory for at least 50 years. Considering the intricacies of child maltreatment research, this paper aims to answer the question: which models show the most promise to explain why child maltreatment is a risk indicator for…

What Does It Mean to Be Pragmatic? Pragmatic Methods, Measures, and Models to Facilitate Research Translation

ERIC Educational Resources Information Center

Glasgow, Russell E.

2013-01-01

Background: One of the reasons for the slow and uncertain translation of research into practice is likely due to the emphasis in science on explanatory models and efficacy designs rather than more pragmatic approaches. Methods: Following a brief definition of what constitutes a pragmatic approach, I provide examples of pragmatic methods, measures,…

The Impact of Student Teaching Experience on Pre-Service Teachers' Readiness for Technology Integration: A Mixed Methods Study with Growth Curve Modeling

ERIC Educational Resources Information Center

Sun, Yan; Strobel, Johannes; Newby, Timothy J.

2017-01-01

Adopting a two-phase explanatory sequential mixed methods research design, the current study examined the impact of student teaching experiences on pre-service teachers' readiness for technology integration. In phase-1 of quantitative investigation, 2-level growth curve models were fitted using online repeated measures survey data collected from…

Fuel load modeling from mensuration attributes in temperate forests in northern Mexico

Treesearch

Maricela Morales-Soto; Marín Pompa-Garcia

2013-01-01

The study of fuels is an important factor in defining the vulnerability of ecosystems to forest fires. The aim of this study was to model a dead fuel load based on forest mensuration attributes from forest management inventories. A scatter plot analysis was performed and, from explanatory trends between the variables considered, correlation analysis was carried out...

The Four-Three-Four Model: Drawing on Partitioning, Equivalence, and Unit-Forming in a Quotient Sub-Construct Fraction Task

ERIC Educational Resources Information Center

Mitchell, Annie

2012-01-01

This paper demonstrates the explanatory power of Kieren's framework for rational number knowing (1988, 1992, 1993, 1995), renamed here the four-three-four model, by describing the different approaches of Grade 6 students to a quotient context task (sharing three or seven custard tarts between five people) using Kieren's terminology of…

An Explanatory Model of Poverty from the Perspective of Social Psychology and Human Rights.

PubMed

Pérez-Muñoz, Alfonso; Chacón, Fernando; Martínez Arias, Rosario

2015-12-09

Poverty is a social problem, entailing not only an economical perspective but above all a human and social issue. Poverty is promoted, justified and maintained by unique individuals and groups by means of our own attitudes, interests and behavior, as well as with our social structures and social relationships. From this interactive, psychosocial and sociostructural perspective, and also considering poverty as a denial of basic human rights (UNDP, 1998), we carried out a study with the primary objective to design and verify an Explanatory Model of Poverty. This research may helps to increase the validity of diagnostics and the effectiveness of interventions. Most of the hypotheses were accepted during the analysis and verification of the Model (p < .001), with data fitting the Model (CFI: 1 RMSEA: .025: LO90: 0 - HI90: .061. RMR: .008). These results, if replicated in new investigations, could have the following implications: (a) the need for a broad and comprehensive definition of poverty including its effects, processes and causes; (b) the need for everybody to accept the social responsibility in the prevention and solution to poverty; and (c) the need to conduct longitudinal interventions with scientific methodology and social participation.

Sensitivity of Alpine and Subalpine Lakes to Atmospheric Deposition in Grand Teton National Park and Yellowstone National Park, Wyoming

NASA Astrophysics Data System (ADS)

Nanus, L.; Campbell, D. H.; Williams, M. W.

2004-12-01

Acidification of high-elevation lakes in the Western United States is of concern because of the storage and release of pollutants in snowmelt runoff combined with steep topography, granitic bedrock, and limited soils and biota. Land use managers have limited resources for sampling and thus need direction on how best to design monitoring programs. We evaluated the sensitivity of 400 lakes in Grand Teton (GRTE) and Yellowstone (YELL) National Parks to acidification from atmospheric deposition of nitrogen and sulfur based on statistical relations between acid-neutralizing capacity (ANC) concentrations and basin characteristics to aid in the design of a long-term monitoring plan for Outstanding Natural Resource Waters. ANC concentrations that were measured at 52 lakes in GRTE and 23 lakes in YELL during synoptic surveys were used to calibrate the statistical models. Basin-characteristic information was derived from Geographic Information System data sets. The explanatory variables that were considered included bedrock type, basin slope, basin aspect, basin elevation, lake area, basin area, inorganic nitrogen (N) deposition, sulfate deposition, hydrogen ion deposition, basin precipitation, soil type, and vegetation type. A logistic regression model was developed and applied to lake basins greater than 1 hectare (ha) in GRTE (n=106) and YELL (n=294). For GRTE, 36 percent of lakes had a greater than 60-percent probability of having ANC concentrations less than 100 microequivalents per liter, and 14 percent of lakes had a greater than 80-percent probability of having ANC concentrations less than 100 microequivalents per liter. The elevation of the lake outlet and the area of the basin with northeast aspects were determined to be statistically significant and were used as the explanatory variables in the multivariate logistic regression model. For YELL, results indicated that 13 percent of lakes had a greater than 60-percent probability of having ANC concentrations less than 100 microequivalents per liter, and 9 percent of lakes had a greater than 80-percent probability of having ANC concentrations less than 100 microequivalents per liter. Only the elevation of the lake outlet was determined to be statistically significant and was used as the explanatory variable in the multivariate logistic regression model. The lakes that exceeded 80-percent probability of having an ANC concentration less than 100 microequivalents per liter, and therefore had the greatest sensitivity to acidification from atmospheric deposition, are located at elevations greater than 2,810 meters (m) in GRTE, and greater than 2,655 m in YELL.

Commentary on: Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research

PubMed Central

Van Der Linden, Martial

2015-01-01

This commentary proposes a complementary perspective to that developed by Billieux, Schimmenti, Khazaal, Maurage and Heeren (2015). The addiction-as-disease approach tends to sideline explanatory factors of a psychosocial, cultural, political, or historical nature. I therefore suggest taking into account not only the personal characteristics (loss of self-control, impulsivity) related to the disease model, but also the social determinants of addictive behaviors (weak social ties, social exclusion, hyperindividualism, poverty, unemployment, etc.). Moreover, the disease model of addiction removes addictive behaviors from the cultural and historical contexts that shape them. I argue that the cultural and historical reasons for which certain factors (such as loss of self-control) became so important in the explanation of addictive behaviors should be more thoroughly considered. PMID:26551902

The Hedwig van Ameringen Executive Leadership in Academic MedicineRTM Program for Women: An Explanatory Study Regarding Its Development and Persistence

ERIC Educational Resources Information Center

Mensel, Ruth

2010-01-01

This study was designed to determine which factors contributed to the development and persistence of a women's leadership development program in higher education. The "Hedwig van Ameringen" Executive Leadership in Academic Medicine[R] "Program for Women" was the basis for this single-case study. To speculate about ELAM's development and…

The determinations of remote sensing satellite data delivery service quality: A positivistic case study in Chinese context

NASA Astrophysics Data System (ADS)

Jin, Jiahua; Yan, Xiangbin; Tan, Qiaoqiao; Li, Yijun

2014-03-01

With the development of remote sensing technology, remote-sensing satellite has been widely used in many aspects of national construction. Big data with different standards and massive users with different needs, make the satellite data delivery service to be a complex giant system. How to deliver remote-sensing satellite data efficiently and effectively is a big challenge. Based on customer service theory, this paper proposes a hierarchy conceptual model for examining the determinations of remote-sensing satellite data delivery service quality in the Chinese context. Three main dimensions: service expectation, service perception and service environment, and 8 sub-dimensions are included in the model. Large amount of first-hand data on the remote-sensing satellite data delivery service have been obtained through field research, semi-structured questionnaire and focused interview. A positivist case study is conducted to validate and develop the proposed model, as well as to investigate the service status and related influence mechanisms. Findings from the analysis demonstrate the explanatory validity of the model, and provide potentially helpful insights for future practice.

Correlations of turbidity to suspended-sediment concentration in the Toutle River Basin, near Mount St. Helens, Washington, 2010-11

USGS Publications Warehouse

Uhrich, Mark A.; Kolasinac, Jasna; Booth, Pamela L.; Fountain, Robert L.; Spicer, Kurt R.; Mosbrucker, Adam R.

2014-01-01

Researchers at the U.S. Geological Survey, Cascades Volcano Observatory, investigated alternative methods for the traditional sample-based sediment record procedure in determining suspended-sediment concentration (SSC) and discharge. One such sediment-surrogate technique was developed using turbidity and discharge to estimate SSC for two gaging stations in the Toutle River Basin near Mount St. Helens, Washington. To provide context for the study, methods for collecting sediment data and monitoring turbidity are discussed. Statistical methods used include the development of ordinary least squares regression models for each gaging station. Issues of time-related autocorrelation also are evaluated. Addition of lagged explanatory variables was used to account for autocorrelation in the turbidity, discharge, and SSC data. Final regression model equations and plots are presented for the two gaging stations. The regression models support near-real-time estimates of SSC and improved suspended-sediment discharge records by incorporating continuous instream turbidity. Future use of such models may potentially lower the costs of sediment monitoring by reducing time it takes to collect and process samples and to derive a sediment-discharge record.

A Conflict Management Scale for Pharmacy

PubMed Central

Gregory, Paul A.; Martin, Craig

2009-01-01

Objectives To develop and establish the validity and reliability of a conflict management scale specific to pharmacy practice and education. Methods A multistage inventory-item development process was undertaken involving 93 pharmacists and using a previously described explanatory model for conflict in pharmacy practice. A 19-item inventory was developed, field tested, and validated. Results The conflict management scale (CMS) demonstrated an acceptable degree of reliability and validity for use in educational or practice settings to promote self-reflection and self-awareness regarding individuals' conflict management styles. Conclusions The CMS provides a unique, pharmacy-specific method for individuals to determine and reflect upon their own conflict management styles. As part of an educational program to facilitate self-reflection and heighten self-awareness, the CMS may be a useful tool to promote discussions related to an important part of pharmacy practice. PMID:19960081

Critical Loads of Atmospheric Nitrogen Deposition for Aquatic Ecosystems in Yosemite and Sequoia and Kings Canyon National Parks

NASA Astrophysics Data System (ADS)

Nanus, L.; Clow, D. W.; Sickman, J. O.

2016-12-01

High-elevation aquatic ecosystems in Yosemite (YOSE) and Sequoia and Kings Canyon (SEKI) National Parks are impacted by atmospheric nitrogen (N) deposition associated with local and regional air pollution. Documented effects include elevated surface water nitrate concentrations, increased algal productivity, and changes in diatom species assemblages. Annual wet inorganic N deposition maps, developed at 1-km resolution for YOSE and SEKI to quantify N deposition to sensitive high-elevation ecosystems, range from 1.0 to over 5.0 kg N ha-1 yr-1. Critical loads of N deposition for nutrient enrichment of aquatic ecosystems were quantified and mapped using a geostatistical approach, with N deposition, topography, vegetation, geology, and climate as potential explanatory variables. Multiple predictive models were created using various combinations of explanatory variables; this approach allowed us to better quantify uncertainty and more accurately identify the areas most sensitive to atmospherically deposited N. The lowest critical loads estimates and highest exceedances identified within YOSE and SEKI occurred in high-elevation basins with steep slopes, sparse vegetation, and areas of neoglacial till and talus. These results are consistent with previous analyses in the Rocky Mountains, and highlight the sensitivity of alpine ecosystems to atmospheric N deposition.

Peak flow regression equations For small, ungaged streams in Maine: Comparing map-based to field-based variables

USGS Publications Warehouse

Lombard, Pamela J.; Hodgkins, Glenn A.

2015-01-01

Regression equations to estimate peak streamflows with 1- to 500-year recurrence intervals (annual exceedance probabilities from 99 to 0.2 percent, respectively) were developed for small, ungaged streams in Maine. Equations presented here are the best available equations for estimating peak flows at ungaged basins in Maine with drainage areas from 0.3 to 12 square miles (mi2). Previously developed equations continue to be the best available equations for estimating peak flows for basin areas greater than 12 mi2. New equations presented here are based on streamflow records at 40 U.S. Geological Survey streamgages with a minimum of 10 years of recorded peak flows between 1963 and 2012. Ordinary least-squares regression techniques were used to determine the best explanatory variables for the regression equations. Traditional map-based explanatory variables were compared to variables requiring field measurements. Two field-based variables—culvert rust lines and bankfull channel widths—either were not commonly found or did not explain enough of the variability in the peak flows to warrant inclusion in the equations. The best explanatory variables were drainage area and percent basin wetlands; values for these variables were determined with a geographic information system. Generalized least-squares regression was used with these two variables to determine the equation coefficients and estimates of accuracy for the final equations.

Teachers Fostering the Co-Development of Science Literacy and Language Literacy with English Language Learners

ERIC Educational Resources Information Center

Carrejo, David J.; Reinhartz, Judy

2014-01-01

Thirty-five elementary teachers participated in a yearlong professional development (PD) program that was designed to foster a culture of on-going teacher learning to promote the co-development of science and language literacy for English language learners (ELL). An explanatory design methodology was used to determine the degree to which science…

Examination of the Relation between TEOG Score of Turkish Revolution History and Kemalism Course and Reading Comprehension Skill (An Example of Explanatory Sequential Mixed Design)

ERIC Educational Resources Information Center

Yuvaci, Ibrahim; Demir, Selçuk Besir

2016-01-01

This paper is aimed to determine the relation between reading comprehension skill and TEOG success. In this research, a mixed research method, sequential explanatory mixed design, is utilized to examine the relation between reading comprehension skills and TEOG success of 8th grade students throughly. In explanatory sequential mixed design…

Time-Frequency Analysis of Beach Bacteria Variations and its Implication for Recreational Water Quality Modeling

EPA Science Inventory

This paper explores the potential of time-frequency wavelet analysis in resolving beach bacteria concentration and possible explanatory variables across multiple time scales with temporal information still preserved. The wavelet scalograms of E. coli concentrations and the explan...

Characterizing Resilience and Growth Among Soldiers: A Trajectory Study

DTIC Science & Technology

2012-04-01

casualties of cruel environments or bad genetics . Positive psychology challenges the assumptions of the disease model. It calls for as much focus on...by psychologists, under several different rubrics : dispositional optimism by Carver and Scheier (1981), hope by Snyder (2000), and explanatory style

[Different explanatory models for addictive behavior in Turkish and German youths in Germany: significance for prevention and treatment].

PubMed

Penka, S; Krieg, S; Hunner, Ch; Heinz, A

2003-07-01

Due to cultural and social barriers, immigrants seldom frequent centers for information, counseling, and treatment of addictive disorders. We examine cultural differences in the explanatory models of addictive behavior among Turkish and German youths in Germany with statistical devices that map the concepts associated with problems of addiction. Relevant differences were found between the disorder concepts of Turkish and German youth. German but not Turkish youths classified eating disorders among severe addictive disorders and associated them with embarrassment and shame. Concerning substance abuse, German but not Turkish youths clearly differentiated between illegal drug abuse and the abuse of alcohol and nicotine. Nearly half of all Turkish youths rejected central medical concepts such as "physical dependence" or "reduced control of substance intake" as completely inadequate to characterize problems of addictive behavior. Preventive information programs must consider these differences and use concepts that are accepted and clearly associated with addictive behavior by immigrant populations.

Incomes, Attitudes, and Occurrences of Invasive Species: An Application to Signal Crayfish in Sweden

NASA Astrophysics Data System (ADS)

Gren, Ing-Marie; Campos, Monica; Edsman, Lennart; Bohman, Patrik

2009-02-01

This article analyzes and carries out an econometric test of the explanatory power of economic and attitude variables for occurrences of the nonnative signal crayfish in Swedish waters. Signal crayfish are a carrier of plague which threatens the native noble crayfish with extinction. Crayfish are associated with recreational and cultural traditions in Sweden, which may run against environmental preferences for preserving native species. Econometric analysis is carried out using panel data at the municipality level with economic factors and attitudes as explanatory variables, which are derived from a simple dynamic harvesting model. A log-normal model is used for the regression analysis, and the results indicate significant impacts on occurrences of waters with signal crayfish of changes in both economic and attitude variables. Variables reflecting environmental and recreational preferences have unexpected signs, where the former variable has a positive and the latter a negative impact on occurrences of waters with signal crayfish. These effects are, however, counteracted by their respective interaction effect with income.

Parents' and speech and language therapists' explanatory models of language development, language delay and intervention.

PubMed

Marshall, Julie; Goldbart, Juliet; Phillips, Julie

2007-01-01

Parental and speech and language therapist (SLT) explanatory models may affect engagement with speech and language therapy, but there has been dearth of research in this area. This study investigated parents' and SLTs' views about language development, delay and intervention in pre-school children with language delay. The aims were to describe, explore and explain the thoughts, understandings, perceptions, beliefs, knowledge and feelings held by: a group of parents from East Manchester, UK, whose pre-school children had been referred with suspected language delay; and SLTs working in the same area, in relation to language development, language delay and language intervention. A total of 24 unstructured interviews were carried out: 15 with parents whose children had been referred for speech and language therapy and nine with SLTs who worked with pre-school children. The interviews were transcribed verbatim and coded using Atlas/ti. The data were analysed, subjected to respondent validation, and grounded theories and principled descriptions developed to explain and describe parents' and SLTs' beliefs and views. Parent and SLT data are presented separately. There are commonalities and differences between the parents and the SLTs. Both groups believe that language development and delay are influenced by both external and internal factors. Parents give more weight to the role of gender, imitation and personality and value television and videos, whereas the SLTs value the 'right environment' and listening skills and consider that health/disability and socio-economic factors are important. Parents see themselves as experts on their child and have varied ideas about the role of SLTs, which do not always accord with SLTs' views. The parents and SLTs differ in their views of the roles of imitation and play in intervention. Parents typically try strategies before seeing an SLT. These data suggest that parents' ideas vary and that, although parents and SLTs may share some views, there are some important differences. These views have implications for the provision of appropriate services. Although this is a small sample from one group in the UK, the results indicate the need to investigate the views of other groups of parents.

Association between prenatal exposure to poliovirus infection and adult schizophrenia.

PubMed

Suvisaari, J; Haukka, J; Tanskanen, A; Hovi, T; Lönnqvist, J

1999-07-01

The authors' goal was to determine whether there is an association between prenatal exposure to poliovirus infection and later development of schizophrenia. All Finnish patients born between 1951 and 1969 with discharge diagnoses of schizophrenia (N = 13,559) were identified from the Finnish Hospital Discharge Register. Information on the monthly number of cases of paralytic poliomyelitis was obtained for each province in Finland. The authors analyzed the incidence of births of individuals who later developed schizophrenia by using a Poisson regression model with year and place of birth, age, sex, season of birth, and smoothed incidence of poliomyelitis in different gestational periods as explanatory variables. An association between the incidence of poliomyelitis and the incidence of births 5 months later of individuals who later developed schizophrenia was observed. Without controlling for seasonality, the effect was significant throughout the second trimester. Second-trimester exposure to poliovirus infection may increase the risk for the later development of schizophrenia.

Unmodeled observation error induces bias when inferring patterns and dynamics of species occurrence via aural detections

USGS Publications Warehouse

McClintock, Brett T.; Bailey, Larissa L.; Pollock, Kenneth H.; Simons, Theodore R.

2010-01-01

The recent surge in the development and application of species occurrence models has been associated with an acknowledgment among ecologists that species are detected imperfectly due to observation error. Standard models now allow unbiased estimation of occupancy probability when false negative detections occur, but this is conditional on no false positive detections and sufficient incorporation of explanatory variables for the false negative detection process. These assumptions are likely reasonable in many circumstances, but there is mounting evidence that false positive errors and detection probability heterogeneity may be much more prevalent in studies relying on auditory cues for species detection (e.g., songbird or calling amphibian surveys). We used field survey data from a simulated calling anuran system of known occupancy state to investigate the biases induced by these errors in dynamic models of species occurrence. Despite the participation of expert observers in simplified field conditions, both false positive errors and site detection probability heterogeneity were extensive for most species in the survey. We found that even low levels of false positive errors, constituting as little as 1% of all detections, can cause severe overestimation of site occupancy, colonization, and local extinction probabilities. Further, unmodeled detection probability heterogeneity induced substantial underestimation of occupancy and overestimation of colonization and local extinction probabilities. Completely spurious relationships between species occurrence and explanatory variables were also found. Such misleading inferences would likely have deleterious implications for conservation and management programs. We contend that all forms of observation error, including false positive errors and heterogeneous detection probabilities, must be incorporated into the estimation framework to facilitate reliable inferences about occupancy and its associated vital rate parameters.

Explanatory style, dispositional optimism, and reported parental behavior.

PubMed

Hjelle, L A; Busch, E A; Warren, J E

1996-12-01

The relationship between two cognitive personality constructs (explanatory style and dispositional optimism) and retrospective self-reports of maternal and paternal behavior were investigated. College students (62 men and 145 women) completed the Life Orientation Test, Attributional Style Questionnaire, and Parental Acceptance-Rejection Questionnaire in a single session. As predicted, dispositional optimism was positively correlated with reported maternal and paternal warmth/acceptance and negatively correlated with aggression/hostility, neglect/indifference, and undifferentiated rejection during middle childhood. Unexpectedly, explanatory style was found to be more strongly associated with retrospective reports of paternal as opposed to maternal behavior. The implications of these results for future research concerning the developmental antecedents of differences in explanatory style and dispositional optimism are discussed.

Robust inference under the beta regression model with application to health care studies.

PubMed

Ghosh, Abhik

2017-01-01

Data on rates, percentages, or proportions arise frequently in many different applied disciplines like medical biology, health care, psychology, and several others. In this paper, we develop a robust inference procedure for the beta regression model, which is used to describe such response variables taking values in (0, 1) through some related explanatory variables. In relation to the beta regression model, the issue of robustness has been largely ignored in the literature so far. The existing maximum likelihood-based inference has serious lack of robustness against outliers in data and generate drastically different (erroneous) inference in the presence of data contamination. Here, we develop the robust minimum density power divergence estimator and a class of robust Wald-type tests for the beta regression model along with several applications. We derive their asymptotic properties and describe their robustness theoretically through the influence function analyses. Finite sample performances of the proposed estimators and tests are examined through suitable simulation studies and real data applications in the context of health care and psychology. Although we primarily focus on the beta regression models with a fixed dispersion parameter, some indications are also provided for extension to the variable dispersion beta regression models with an application.

Concepts in Change

NASA Astrophysics Data System (ADS)

Rusanen, Anna-Mari; Pöyhönen, Samuli

2013-06-01

In this article we focus on the concept of concept in conceptual change. We argue that (1) theories of higher learning must often employ two different notions of concept that should not be conflated: psychological and scientific concepts. The usages for these two notions are partly distinct and thus straightforward identification between them is unwarranted. Hence, the strong analogy between scientific theory change and individual learning should be approached with caution. In addition, we argue that (2) research in psychology and cognitive science provides a promising theoretical basis for developing explanatory mechanistic models of conceptual change. Moreover, we argue that (3) arguments against deeper integration between the fields of psychology and conceptual change are not convincing, and that recent theoretical developments in the cognitive sciences might prove indispensable in filling in the details in mechanisms of conceptual change.

Conceptual Model of Weight Management in Overweight and Obese African-American Females.

PubMed

Sutton, Suzanne M; Magwood, Gayenell S; Nemeth, Lynne S; Jenkins, Carolyn M

2017-04-01

Weight management of overweight and obese (OWO) African-American females (AAFs) is a poorly defined concept, leading to ineffective treatment of overweight and obesity, prevention of health sequelae, and risk reduction. A conceptual model of the phenomenon of weight management in OWO AAFs was developed through dimensional analysis of the literature. Constructs were identified and sorted into the dimensions of perspective, context, conditions, process, and consequences and integrated into an explanatory matrix. Through dimensional analysis, weight management in OWO AAFs was characterized as a multidimensional concept, defined from the perspective of weight loss in community-dwelling AAFs. Behaviors associated with weight management are strongly influenced by intrinsic factors and extrinsic conditions, which influence engagement in the processes and consequences of weight management. The resulting conceptual model of weight management in OWO AAFs provides a framework for research interventions applicable in a variety of settings. © 2016 Wiley Periodicals, Inc.

Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2.

PubMed

de Ávila, Maurício Boff; Xavier, Mariana Morrone; Pintro, Val Oliveira; de Azevedo, Walter Filgueira

2017-12-09

Here we report the development of a machine-learning model to predict binding affinity based on the crystallographic structures of protein-ligand complexes. We used an ensemble of crystallographic structures (resolution better than 1.5 Å resolution) for which half-maximal inhibitory concentration (IC 50 ) data is available. Polynomial scoring functions were built using as explanatory variables the energy terms present in the MolDock and PLANTS scoring functions. Prediction performance was tested and the supervised machine learning models showed improvement in the prediction power, when compared with PLANTS and MolDock scoring functions. In addition, the machine-learning model was applied to predict binding affinity of CDK2, which showed a better performance when compared with AutoDock4, AutoDock Vina, MolDock, and PLANTS scores. Copyright © 2017 Elsevier Inc. All rights reserved.

Regression model development and computational procedures to support estimation of real-time concentrations and loads of selected constituents in two tributaries to Lake Houston near Houston, Texas, 2005-9

USGS Publications Warehouse

Lee, Michael T.; Asquith, William H.; Oden, Timothy D.

2012-01-01

In December 2005, the U.S. Geological Survey (USGS), in cooperation with the City of Houston, Texas, began collecting discrete water-quality samples for nutrients, total organic carbon, bacteria (Escherichia coli and total coliform), atrazine, and suspended sediment at two USGS streamflow-gaging stations that represent watersheds contributing to Lake Houston (08068500 Spring Creek near Spring, Tex., and 08070200 East Fork San Jacinto River near New Caney, Tex.). Data from the discrete water-quality samples collected during 2005–9, in conjunction with continuously monitored real-time data that included streamflow and other physical water-quality properties (specific conductance, pH, water temperature, turbidity, and dissolved oxygen), were used to develop regression models for the estimation of concentrations of water-quality constituents of substantial source watersheds to Lake Houston. The potential explanatory variables included discharge (streamflow), specific conductance, pH, water temperature, turbidity, dissolved oxygen, and time (to account for seasonal variations inherent in some water-quality data). The response variables (the selected constituents) at each site were nitrite plus nitrate nitrogen, total phosphorus, total organic carbon, E. coli, atrazine, and suspended sediment. The explanatory variables provide easily measured quantities to serve as potential surrogate variables to estimate concentrations of the selected constituents through statistical regression. Statistical regression also facilitates accompanying estimates of uncertainty in the form of prediction intervals. Each regression model potentially can be used to estimate concentrations of a given constituent in real time. Among other regression diagnostics, the diagnostics used as indicators of general model reliability and reported herein include the adjusted R-squared, the residual standard error, residual plots, and p-values. Adjusted R-squared values for the Spring Creek models ranged from .582–.922 (dimensionless). The residual standard errors ranged from .073–.447 (base-10 logarithm). Adjusted R-squared values for the East Fork San Jacinto River models ranged from .253–.853 (dimensionless). The residual standard errors ranged from .076–.388 (base-10 logarithm). In conjunction with estimated concentrations, constituent loads can be estimated by multiplying the estimated concentration by the corresponding streamflow and by applying the appropriate conversion factor. The regression models presented in this report are site specific, that is, they are specific to the Spring Creek and East Fork San Jacinto River streamflow-gaging stations; however, the general methods that were developed and documented could be applied to most perennial streams for the purpose of estimating real-time water quality data.

Reconstruction of a windborne insect invasion using a particle dispersal model, historical wind data, and Bayesian analysis of genetic data

PubMed Central

Lander, Tonya A; Klein, Etienne K; Oddou-Muratorio, Sylvie; Candau, Jean-Noël; Gidoin, Cindy; Chalon, Alain; Roig, Anne; Fallour, Delphine; Auger-Rozenberg, Marie-Anne; Boivin, Thomas

2014-01-01

Understanding how invasive species establish and spread is vital for developing effective management strategies for invaded areas and identifying new areas where the risk of invasion is highest. We investigated the explanatory power of dispersal histories reconstructed based on local-scale wind data and a regional-scale wind-dispersed particle trajectory model for the invasive seed chalcid wasp Megastigmus schimitscheki (Hymenoptera: Torymidae) in France. The explanatory power was tested by: (1) survival analysis of empirical data on M. schimitscheki presence, absence and year of arrival at 52 stands of the wasp's obligate hosts, Cedrus (true cedar trees); and (2) Approximate Bayesian analysis of M. schimitscheki genetic data using a coalescence model. The Bayesian demographic modeling and traditional population genetic analysis suggested that initial invasion across the range was the result of long-distance dispersal from the longest established sites. The survival analyses of the windborne expansion patterns derived from a particle dispersal model indicated that there was an informative correlation between the M. schimitscheki presence/absence data from the annual surveys and the scenarios based on regional-scale wind data. These three very different analyses produced highly congruent results supporting our proposal that wind is the most probable vector for passive long-distance dispersal of this invasive seed wasp. This result confirms that long-distance dispersal from introduction areas is a likely driver of secondary expansion of alien invasive species. Based on our results, management programs for this and other windborne invasive species may consider (1) focusing effort at the longest established sites and (2) monitoring outlying populations remains critically important due to their influence on rates of spread. We also suggest that there is a distinct need for new analysis methods that have the capacity to combine empirical spatiotemporal field data, genetic data, and environmental data to investigate dispersal and invasion. PMID:25558356

Decision tree analysis of factors influencing rainfall-related building damage

NASA Astrophysics Data System (ADS)

Spekkers, M. H.; Kok, M.; Clemens, F. H. L. R.; ten Veldhuis, J. A. E.

2014-04-01

Flood damage prediction models are essential building blocks in flood risk assessments. Little research has been dedicated so far to damage of small-scale urban floods caused by heavy rainfall, while there is a need for reliable damage models for this flood type among insurers and water authorities. The aim of this paper is to investigate a wide range of damage-influencing factors and their relationships with rainfall-related damage, using decision tree analysis. For this, district-aggregated claim data from private property insurance companies in the Netherlands were analysed, for the period of 1998-2011. The databases include claims of water-related damage, for example, damages related to rainwater intrusion through roofs and pluvial flood water entering buildings at ground floor. Response variables being modelled are average claim size and claim frequency, per district per day. The set of predictors include rainfall-related variables derived from weather radar images, topographic variables from a digital terrain model, building-related variables and socioeconomic indicators of households. Analyses were made separately for property and content damage claim data. Results of decision tree analysis show that claim frequency is most strongly associated with maximum hourly rainfall intensity, followed by real estate value, ground floor area, household income, season (property data only), buildings age (property data only), ownership structure (content data only) and fraction of low-rise buildings (content data only). It was not possible to develop statistically acceptable trees for average claim size, which suggest that variability in average claim size is related to explanatory variables that cannot be defined at the district scale. Cross-validation results show that decision trees were able to predict 22-26% of variance in claim frequency, which is considerably better compared to results from global multiple regression models (11-18% of variance explained). Still, a large part of the variance in claim frequency is left unexplained, which is likely to be caused by variations in data at subdistrict scale and missing explanatory variables.

The roots of violence: converging psychoanalytic explanatory models for power struggles and violence in schools.

PubMed

Twemlow, S W

2000-10-01

This paper demonstrates that several psychoanalytic models taken together converge to collectively explain school violence and power struggles better than each does alone. Using my own experience in doing psychoanalytically informed community intervention, I approach the problem of school violence from a combination of Adlerian, Stollerian, dialectical social systems, and Klein-Bion perspectives. This integrated model is then applied to the Columbine High School massacre in Littleton, Colorado.

Public Views on the Gendering of Mathematics and Related Careers: International Comparisons

ERIC Educational Resources Information Center

Forgasz, Helen; Leder, Gilah; Tan, Hazel

2014-01-01

Mathematics continues to be an enabling discipline for Science, Technology, Engineering, and Mathematics (STEM)-based university studies and related careers. Explanatory models for females' underrepresentation in higher level mathematics and STEM-based courses comprise learner-related and environmental variables--including societal beliefs. Using…

Improving Adolescent Judgment and Decision Making

PubMed Central

Dansereau, Donald F.; Knight, Danica K.; Flynn, Patrick M.

2013-01-01

Human judgment and decision making (JDM) has substantial room for improvement, especially among adolescents. Increased technological and social complexity “ups the ante” for developing impactful JDM interventions and aids. Current explanatory advances in this field emphasize dual processing models that incorporate both experiential and analytic processing systems. According to these models, judgment and decisions based on the experiential system are rapid and stem from automatic reference to previously stored episodes. Those based on the analytic system are viewed as slower and consciously developed. These models also hypothesize that metacognitive (self-monitoring) activities embedded in the analytic system influence how and when the two systems are used. What is not included in these models is the development of an intersection between the two systems. Because such an intersection is strongly suggested by memory and educational research as the basis of wisdom/expertise, the present paper describes an Integrated Judgment and Decision-Making Model (IJDM) that incorporates this component. Wisdom/expertise is hypothesized to contain a collection of schematic structures that can emerge from the accumulation of similar episodes or repeated analytic practice. As will be argued, in comparisons to dual system models, the addition of this component provides a broader basis for selecting and designing interventions to improve adolescent JDM. Its development also has implications for generally enhancing cognitive interventions by adopting principles from athletic training to create automated, expert behaviors. PMID:24391350

Multi-scale Mexican spotted owl (Strix occidentalis lucida) nest/roost habitat selection in Arizona and a comparison with single-scale modeling results

Treesearch

Brad C. Timm; Kevin McGarigal; Samuel A. Cushman; Joseph L. Ganey

2016-01-01

Efficacy of future habitat selection studies will benefit by taking a multi-scale approach. In addition to potentially providing increased explanatory power and predictive capacity, multi-scale habitat models enhance our understanding of the scales at which species respond to their environment, which is critical knowledge required to implement effective...

Non-Equilbrium Fermi Gases

DTIC Science & Technology

2016-02-02

understanding is the experimental verification of a new model of light-induced loss spectra, employing continuum-dressed basis states, which agrees in...and additional qualifiers separated by commas, e.g. Smith, Richard, J, Jr. 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES). Self -explanatory... verification of a new model of light-induced loss spectra, employing continuum-dressed basis states, which agrees in shape and magnitude with all of our

A database for propagation models

NASA Technical Reports Server (NTRS)

Kantak, Anil V.; Suwitra, Krisjani; Le, Chuong

1995-01-01

A database of various propagation phenomena models that can be used by telecommunications systems engineers to obtain parameter values for systems design is presented. This is an easy-to-use tool and is currently available for either a PC using Excel software under Windows environment or a Macintosh using Excel software for Macintosh. All the steps necessary to use the software are easy and many times self explanatory.

Modeling Alcohol Use Intensity among Students at a Historically Black University: The Role of Social Norms, Perceptions for Risk, and Selected Demographic Variables

ERIC Educational Resources Information Center

Lewis, Todd F.; Likis-Werle, Elizabeth; Fulton, Cheryl L.

2012-01-01

Drinking patterns and rates at historically Black colleges and universities (HBCU) are not well understood. Social norms and perceptions of risk are two explanatory mechanisms that have accounted for a significant amount of variance in college student drinking at predominantly White campuses. However, these models have not been examined among…

Societal Projection: Beliefs Concerning the Relationship between Development and Inequality in China

PubMed Central

Xie, Yu; Thornton, Arland; Wang, Guangzhou; Lai, Qing

2012-01-01

We examine how the relationship between development and inequality at the societal level is perceived and evaluated by ordinary Chinese people. We hypothesize that because the Chinese have recently experienced rapid increases in both economic growth and social inequality, they tend to view economic development as a driving force of social inequality. To address this question, we conducted a social survey in 2006 in six Chinese provinces (n = 4,898). The survey data reveal that a large proportion of Chinese people have internalized a causal model in which they project high levels of inequality onto countries they view as more developed and low levels of inequality onto countries they see as less developed. However, results also show that a smaller proportion of Chinese believe in a negative relationship between development and inequality. Hence, the study reveals heterogeneity among ordinary Chinese in their perceptions of the causal relationship between development and inequality. Surprisingly, socioeconomic and demographic characteristics provide no explanatory power in explaining this heterogeneity. PMID:23017918

Interdisciplinary and Cross-Cultural Perspectives on Explanatory Coexistence.

PubMed

Watson-Jones, Rachel E; Busch, Justin T A; Legare, Cristine H

2015-10-01

Natural and supernatural explanations are used to interpret the same events in a number of predictable and universal ways. Yet little is known about how variation in diverse cultural ecologies influences how people integrate natural and supernatural explanations. Here, we examine explanatory coexistence in three existentially arousing domains of human thought: illness, death, and human origins using qualitative data from interviews conducted in Tanna, Vanuatu. Vanuatu, a Melanesian archipelago, provides a cultural context ideal for examining variation in explanatory coexistence due to the lack of industrialization and the relatively recent introduction of Christianity and Western education. We argue for the integration of interdisciplinary methodologies from cognitive science and anthropology to inform research on explanatory coexistence. Copyright © 2015 Cognitive Science Society, Inc.

Fraying connections of caring women: an exemplar of including difference in the development of explanatory frameworks.

PubMed

Wuest, J

1997-01-01

While research exploring diverse groups enhances understanding of their unique perspectives and experiences, it also contributes to the exclusion of such groups from mainstream frameworks and solutions. The feminist grounded theory method allows for inclusion of marginalized groups through theoretical sensitivity to feminist theory and theoretical sampling. This paper demonstrates how this approach results in an explanatory framework that accounts for diverse realities in a study of women's caring. Fraying connections were identified as women's initial response to competing and changing caring demands. The range of dimensions and properties of fraying connections was identified through theoretical sampling guided by the emerging themes and theoretical sensitivity to issues of gender, culture, age, ability, class, and sexual orientation.

The Association between Regional Environmental Factors and Road Trauma Rates: A Geospatial Analysis of 10 Years of Road Traffic Crashes in British Columbia, Canada

PubMed Central

Brubacher, Jeffrey R.; Chan, Herbert; Erdelyi, Shannon; Schuurman, Nadine; Amram, Ofer

2016-01-01

Background British Columbia, Canada is a geographically large jurisdiction with varied environmental and socio-cultural contexts. This cross-sectional study examined variation in motor vehicle crash rates across 100 police patrols to investigate the association of crashes with key explanatory factors. Methods Eleven crash outcomes (total crashes, injury crashes, fatal crashes, speed related fatal crashes, total fatalities, single-vehicle night-time crashes, rear-end collisions, and collisions involving heavy vehicles, pedestrians, cyclists, or motorcyclists) were identified from police collision reports and insurance claims and mapped to police patrols. Six potential explanatory factors (intensity of traffic law enforcement, speed limits, climate, remoteness, socio-economic factors, and alcohol consumption) were also mapped to police patrols. We then studied the association between crashes and explanatory factors using negative binomial models with crash count per patrol as the response variable and explanatory factors as covariates. Results Between 2003 and 2012 there were 1,434,239 insurance claim collisions, 386,326 police reported crashes, and 3,404 fatal crashes. Across police patrols, there was marked variation in per capita crash rate and in potential explanatory factors. Several factors were associated with crash rates. Percent roads with speed limits ≤ 60 km/hr was positively associated with total crashes, injury crashes, rear end collisions, and collisions involving pedestrians, cyclists, and heavy vehicles; and negatively associated with single vehicle night-time crashes, fatal crashes, fatal speeding crashes, and total fatalities. Higher winter temperature was associated with lower rates of overall collisions, single vehicle night-time collisions, collisions involving heavy vehicles, and total fatalities. Lower socio-economic status was associated with higher rates of injury collisions, pedestrian collisions, fatal speeding collisions, and fatal collisions. Regions with dedicated traffic officers had fewer fatal crashes and fewer fatal speed related crashes but more rear end crashes and more crashes involving cyclists or pedestrians. The number of traffic citations per 1000 drivers was positively associated with total crashes, fatal crashes, total fatalities, fatal speeding crashes, injury crashes, single vehicle night-time crashes, and heavy vehicle crashes. Possible explanations for these associations are discussed. Conclusions There is wide variation in per capita rates of motor vehicle crashes across BC police patrols. Some variation is explained by factors such as climate, road type, remoteness, socioeconomic variables, and enforcement intensity. The ability of explanatory factors to predict crash rates would be improved if considered with local traffic volume by all travel modes. PMID:27099930

Extending the explanatory utility of the EPPM beyond fear-based persuasion.

PubMed

Lewis, Ioni; Watson, Barry; White, Katherine M

2013-01-01

In the 20 years since its inception, the Extended Parallel Process Model (EPPM) has attracted much empirical support. Currently, and unsurprisingly, given that is a model of fear-based persuasion, the EPPM's explanatory utility has been based only upon fear-based messages. However, an argument is put forth herein that draws upon existing evidence that the EPPM may be an efficacious framework for explaining the persuasive process and outcomes of emotion-based messages more broadly when such messages are addressing serious health topics. For the current study, four different types of emotional appeals were purposefully devised and included a fear-, an annoyance/agitation-, a pride-, and a humor-based message. All messages addressed the serious health issue of road safety, and in particular the risky behavior of speeding. Participants (n = 551) were exposed to only one of the four messages and subsequently provided responses within a survey. A series of 2 (threat: low, high) × 2 (efficacy: low, high) analysis of variance was conducted for each of the appeals based on the EPPM's message outcomes of acceptance and rejection. Support was found for the EPPM with a number of main effects of threat and efficacy emerging, reflecting that, irrespective of emotional appeal type, high levels of threat and efficacy enhanced message outcomes via maximizing acceptance and minimizing rejection. Theoretically, the findings provide support for the explanatory utility of the EPPM for emotion-based health messages more broadly. In an applied sense, the findings highlight the value of adopting the EPPM as a framework when devising and evaluating emotion-based health messages for serious health topics.

Identifying the physical and anthropometric qualities explanatory of paddling adolescents.

PubMed

Sinclair, Wade H; Leicht, Anthony S; Eady, Troy W; Marshall, Nick J; Woods, Carl T

2017-12-01

This study aimed to identify the physical and/or anthropometric qualities explanatory of adolescent surf lifesavers participating in paddling activities. Cross-sectional observational study. A total of 53 (14-18years) male participants were recruited and classified into two groups; paddlers (n=30; actively participating in paddling), non-paddlers (n=23; not actively participating in paddling). All participants completed a testing battery that consisted of 16 physical (isometric strength and muscular endurance) and anthropometric (height, mass, segment lengths and breadths) assessments. Binary logistic regression models and receiver operating characteristic curves were built to identify the physical and/or anthropometric qualities most explanatory of paddling status (two levels: 1=paddlers, 0=non-paddlers). Significant between group differences were noted for 14 of the 16 assessments (P<0.05; d=0.59-1.29). However, it was the combination of horizontal shoulder abduction isometric strength, body mass, and sitting height that provided the greatest association with paddling status (Akaike Information Criterion=47.13). This full model successfully detected 87% and 70% of the paddlers and non-paddlers, respectively, with an area under the curve of 84.2%. These results indicate that there are distinctive physical and anthropometric qualities that may be advantageous for prospective paddling athletes to possess. Practitioners should integrate assessments of horizontal shoulder abduction isometric strength, body mass, and sitting height, as well as their subsequent cut-off thresholds, into talent detection programs focused toward the recognition of performance potential in paddling-oriented sports. Copyright © 2017 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Re-orienting discussions of scientific explanation: A functional perspective.

PubMed

Woody, Andrea I

2015-08-01

Philosophy of science offers a rich lineage of analysis concerning the nature of scientific explanation, but the vast majority of this work, aiming to provide an analysis of the relation that binds a given explanans to its corresponding explanandum, presumes the proper analytic focus rests at the level of individual explanations. There are, however, other questions we could ask about explanation in science, such as: What role(s) does explanatory practice play in science? Shifting focus away from explanations, as achievements, toward explaining, as a coordinated activity of communities, the functional perspective aims to reveal how the practice of explanatory discourse functions within scientific communities given their more comprehensive aims and practices. In this paper, I outline the functional perspective, argue that taking the functional perspective can reveal important methodological roles for explanation in science, and consequently, that beginning here provides resources for developing more adequate responses to traditional concerns. In particular, through an examination of the ideal gas law, I emphasize the normative status of explanations within scientific communities and discuss how such status underwrites a compelling rationale for explanatory power as a theoretical virtue. Copyright © 2015 Elsevier Ltd. All rights reserved.

Social Inequality and Labor Force Participation.

ERIC Educational Resources Information Center

King, Jonathan

The labor force participation rates of whites, blacks, and Spanish-Americans, grouped by sex, are explained in a linear regression model fitted with 1970 U. S. Census data on Standard Metropolitan Statistical Area (SMSA). The explanatory variables are: average age, average years of education, vocational training rate, disabled rate, unemployment…

Fostering Hooks and Shifts: Tutorial Tactics for Guided Mathematical Discovery

ERIC Educational Resources Information Center

Abrahamson, Dor; Gutierrez, Jose; Charoenying, Timothy; Negrete, Andrea; Bumbacher, Engin

2012-01-01

How do instructors guide students to discover mathematical content? Are current explanatory models of pedagogical practice suitable to capture pragmatic essentials of discovery-based instruction? We examined videographed data from the implementation of a natural user interface design for proportions, so as to determine one constructivist tutor's…

Food Perceptions and Concerns of Aboriginal Women Coping with Gestational Diabetes in Winnipeg, Manitoba

ERIC Educational Resources Information Center

Neufeld, Hannah Tait

2011-01-01

Objective: To describe how Aboriginal women in an urban setting perceive dietary treatment recommendations associated with gestational diabetes mellitus (GDM). Design: Semi-structured explanatory model interviews explored Aboriginal women's illness experiences with GDM. Setting and Participants: Twenty-nine self-declared Aboriginal women who had…

Educational Research in Educational Practice: Predictors of Use

ERIC Educational Resources Information Center

Lysenko, Larysa V.; Abrami, Philip C.; Dagenais, Christian; Janosz, Michel

2014-01-01

This study investigates the predictors of school practitioners' (N = 2,425) use of educational research. The suggested model explained significantly but modestly the infrequent use of educational research by practitioners. Of the four factors in the study, "opinions about research" had the most explanatory power. The results are…

Revista de Investigacion Educativa, 1999 (Journal of Educational Research, 1999).

ERIC Educational Resources Information Center

Revista de Investigacion Educativa, 1999

1999-01-01

Articles in this volume, written in Spanish, focus on the following: intellectual style and academic performance; an explanatory integrated model of academic goals, learning strategies, and academic performance; a comparative situational study of drug addiction; early childhood depression and academic performance: a comparative study of patients…

The Relationship between the Emotional Intelligence of Secondary Public School Principals and School Performance

ERIC Educational Resources Information Center

Ashworth, Stephanie R.

2013-01-01

The study examined the relationship between secondary public school principals' emotional intelligence and school performance. The correlational study employed an explanatory sequential mixed methods model. The non-probability sample consisted of 105 secondary public school principals in Texas. The emotional intelligence characteristics of the…

Support, Belonging, Motivation, and Engagement in the College Classroom: A Mixed Method Study

ERIC Educational Resources Information Center

Zumbrunn, Sharon; McKim, Courtney; Buhs, Eric; Hawley, Leslie R.

2014-01-01

This explanatory sequential mixed methods study examined how belonging perceptions, academic motivation, and engagement might mediate the relationship between academic contextual characteristics and achievement using structural equation modeling and qualitative follow-up interviews with college students from a large, Midwestern university. In the…

"The Theory behind How Students Learn": Applying Developmental Theory to Research on Children's Historical Thinking

ERIC Educational Resources Information Center

Dulberg, Nancy

2005-01-01

Recent research on children's historical thinking has produced rich descriptions of instruction. However, the research literature is largely lacking a theoretical model of learning. This article asserts that developmental constructivist theory informs research design and interpretation, provides explanatory power, and promises more useful…

[Adult mortality differentials in Argentina].

PubMed

Rofman, R

1994-06-01

Adult mortality differentials in Argentina are estimated and analyzed using data from the National Social Security Administration. The study of adult mortality has attracted little attention in developing countries because of the scarcity of reliable statistics and the greater importance assigned to demographic phenomena traditionally associated with development, such as infant mortality and fertility. A sample of 39,421 records of retired persons surviving as of June 30, 1988, was analyzed by age, sex, region of residence, relative amount of pension, and social security fund of membership prior to the consolidation of the system in 1967. The thirteen former funds were grouped into the five categories of government, commerce, industry, self-employed, and other, which were assumed to be proxies for the activity sector in which the individual spent his active life. The sample is not representative of the Argentine population, since it excludes the lowest and highest socioeconomic strata and overrepresents men and urban residents. It is, however, believed to be adequate for explaining mortality differentials for most of the population covered by the social security system. The study methodology was based on the technique of logistic analysis and on the use of regional model life tables developed by Coale and others. To evaluate the effect of the study variables on the probability of dying, a regression model of maximal verisimilitude was estimated. The model relates the logit of the probability of death between ages 65 and 95 to the available explanatory variables, including their possible interactions. Life tables were constructed by sex, region of residence, previous pension fund, and income. As a test of external consistency, a model including only age and sex as explanatory variables was constructed using the methodology. The results confirmed consistency between the estimated values and other published estimates. A significant conclusion of the study was that social security data are a satisfactory source for study of adult mortality, a finding of importance in cases where vital statistics systems are deficient. Mortality differentials by income level and activity sector were significant, representing up to 11.5 years in life expectancy at age 20 and 4.4 years at age 65. Mortality differentials by region were minor, probably due to the nature of the sample. The lowest observed mortality levels were in own-account workers, independent professionals, and small businessmen.

Adherence to physical activity in an unsupervised setting: Explanatory variables for high attrition rates among fitness center members.

PubMed

Sperandei, Sandro; Vieira, Marcelo C; Reis, Arianne C

2016-11-01

To evaluate the attrition rate of members of a fitness center in the city of Rio de Janeiro and the potential explanatory variables for the phenomenon. An exploratory, observational study using a retrospective longitudinal frame. The records of 5240 individuals, members of the fitness center between January-2005 and June-2014, were monitored for 12 months or until cancellation of membership, whichever occurred first. A Cox proportional hazard regression model was adjusted to identify variables associated to higher risk of 'abandonment' of activities. This study was approved by Southern Cross University's Human Research Ethics Committee (approval number: ECN-15-176). The general survival curve shows that 63% of new members will abandon activities before the third month, and less than 4% will remain for more than 12 months of continuous activity. The regression model showed that age, previous level of physical activity, initial body mass index and motivations related to weight loss, hypertrophy, health, and aesthetics are related to risk of abandonment. Combined, those variables represent an important difference in the probability to abandon the gym between individuals with the best and worse combination of variables. Even individuals presenting the best combination of variables still present a high risk of abandonment before completion of 12 months of fitness center membership. Findings can assist in the identification of high risk individuals and therefore help in the development of strategies to prevent abandonment of physical activity practice. Copyright © 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Developing and Validating a Tablet Version of an Illness Explanatory Model Interview for a Public Health Survey in Pune, India

PubMed Central

Giduthuri, Joseph G.; Maire, Nicolas; Joseph, Saju; Kudale, Abhay; Schaetti, Christian; Sundaram, Neisha; Schindler, Christian; Weiss, Mitchell G.

2014-01-01

Background Mobile electronic devices are replacing paper-based instruments and questionnaires for epidemiological and public health research. The elimination of a data-entry step after an interview is a notable advantage over paper, saving investigator time, decreasing the time lags in managing and analyzing data, and potentially improving the data quality by removing the error-prone data-entry step. Research has not yet provided adequate evidence, however, to substantiate the claim of fewer errors for computerized interviews. Methodology We developed an Android-based illness explanatory interview for influenza vaccine acceptance and tested the instrument in a field study in Pune, India, for feasibility and acceptability. Error rates for tablet and paper were compared with reference to the voice recording of the interview as gold standard to assess discrepancies. We also examined the preference of interviewers for the classical paper-based or the electronic version of the interview and compared the costs of research with both data collection devices. Results In 95 interviews with household respondents, total error rates with paper and tablet devices were nearly the same (2.01% and 1.99% respectively). Most interviewers indicated no preference for a particular device; but those with a preference opted for tablets. The initial investment in tablet-based interviews was higher compared to paper, while the recurring costs per interview were lower with the use of tablets. Conclusion An Android-based tablet version of a complex interview was developed and successfully validated. Advantages were not compromised by increased errors, and field research assistants with a preference preferred the Android device. Use of tablets may be more costly than paper for small samples and less costly for large studies. PMID:25233212

Spatial Modeling of Flood Duration in Amazonian Floodplains Through Radar Remote Sensing and Generalized Linear Models

NASA Astrophysics Data System (ADS)

Ferreira-Ferreira, J.; Francisco, M. S.; Silva, T. S. F.

2017-12-01

Amazon floodplains play an important role in biodiversity maintenance and provide important ecosystem services. Flood duration is the prime factor modulating biogeochemical cycling in Amazonian floodplain systems, as well as influencing ecosystem structure and function. However, due to the absence of accurate terrain information, fine-scale hydrological modeling is still not possible for most of the Amazon floodplains, and little is known regarding the spatio-temporal behavior of flooding in these environments. Our study presents an new approach for spatial modeling of flood duration, using Synthetic Aperture Radar (SAR) and Generalized Linear Modeling. Our focal study site was Mamirauá Sustainable Development Reserve, in the Central Amazon. We acquired a series of L-band ALOS-1/PALSAR Fine-Beam mosaics, chosen to capture the widest possible range of river stage heights at regular intervals. We then mapped flooded area on each image, and used the resulting binary maps as the response variable (flooded/non-flooded) for multiple logistic regression. Explanatory variables were accumulated precipitation 15 days prior and the water stage height recorded in the Mamirauá lake gauging station observed for each image acquisition date, Euclidean distance from the nearest drainage, and slope, terrain curvature, profile curvature, planform curvature and Height Above the Nearest Drainage (HAND) derived from the 30-m SRTM DEM. Model results were validated with water levels recorded by ten pressure transducers installed within the floodplains, from 2014 to 2016. The most accurate model included water stage height and HAND as explanatory variables, yielding a RMSE of ±38.73 days of flooding per year when compared to the ground validation sites. The largest disagreements were 57 days and 83 days for two validation sites, while remaining locations achieved absolute errors lower than 38 days. In five out of nine validation sites, the model predicted flood durations with disagreements lower than 20 days. The method extends our current capability to answer relevant scientific questions regarding floodplain ecological structure and functioning, and allows forecasting of ecological and biogeochemical alterations under climate change scenarios, using readily available datasets.

A hierarchical spatial model of avian abundance with application to Cerulean Warblers

USGS Publications Warehouse

Thogmartin, Wayne E.; Sauer, John R.; Knutson, Melinda G.

2004-01-01

Surveys collecting count data are the primary means by which abundance is indexed for birds. These counts are confounded, however, by nuisance effects including observer effects and spatial correlation between counts. Current methods poorly accommodate both observer and spatial effects because modeling these spatially autocorrelated counts within a hierarchical framework is not practical using standard statistical approaches. We propose a Bayesian approach to this problem and provide as an example of its implementation a spatial model of predicted abundance for the Cerulean Warbler (Dendroica cerulea) in the Prairie-Hardwood Transition of the upper midwestern United States. We used an overdispersed Poisson regression with fixed and random effects, fitted by Markov chain Monte Carlo methods. We used 21 years of North American Breeding Bird Survey counts as the response in a loglinear function of explanatory variables describing habitat, spatial relatedness, year effects, and observer effects. The model included a conditional autoregressive term representing potential correlation between adjacent route counts. Categories of explanatory habitat variables in the model included land cover composition and configuration, climate, terrain heterogeneity, and human influence. The inherent hierarchy in the model was from counts occurring, in part, as a function of observers within survey routes within years. We found that the percentage of forested wetlands, an index of wetness potential, and an interaction between mean annual precipitation and deciduous forest patch size best described Cerulean Warbler abundance. Based on a map of relative abundance derived from the posterior parameter estimates, we estimated that only 15% of the species' population occurred on federal land, necessitating active engagement of public landowners and state agencies in the conservation of the breeding habitat for this species. Models of this type can be applied to any data in which the response is counts, such as animal counts, activity (e.g.,nest) counts, or species richness. The most noteworthy practical application of this spatial modeling approach is the ability to map relative species abundance. The functional relationships that we elucidated for the Cerulean Warbler provide a basis for the development of management programs and may serve to focus management and monitoring on areas and habitat variables important to Cerulean Warblers.

Self-rated health and health-strengthening factors in community-living frail older people.

PubMed

Ebrahimi, Zahra; Dahlin-Ivanoff, Synneve; Eklund, Kajsa; Jakobsson, Annika; Wilhelmson, Katarina

2015-04-01

The aim of this study was to analyse the explanatory power of variables measuring health-strengthening factors for self-rated health among community-living frail older people. Frailty is commonly constructed as a multi-dimensional geriatric syndrome ascribed to the multi-system deterioration of the reserve capacity in older age. Frailty in older people is associated with decreased physical and psychological well-being. However, knowledge about the experiences of health in frail older people is still limited. The design of the study was cross-sectional. The data were collected between October 2008 and November 2010 through face-to-face structured interviews with older people aged 65-96 years (N = 161). Binary logistic regression was used to analyse whether a set of explanatory relevant variables is associated with self-rated health. The results from the final model showed that satisfaction with one's ability to take care of oneself, having 10 or fewer symptoms and not feeling lonely had the best explanatory power for community-living frail older peoples' experiences of good health. The results indicate that a multi-disciplinary approach is desirable, where the focus should not only be on medical problems but also on providing supportive services to older people to maintain their independence and experiences of health despite frailty. © 2014 John Wiley & Sons Ltd.

Explanatory factors and predictors of fatigue in persons with rheumatoid arthritis: A longitudinal study.

PubMed

Feldthusen, Caroline; Grimby-Ekman, Anna; Forsblad-d'Elia, Helena; Jacobsson, Lennart; Mannerkorpi, Kaisa

2016-04-28

To investigate the impact of disease-related aspects on long-term variations in fatigue in persons with rheumatoid arthritis. Observational longitudinal study. Sixty-five persons with rheumatoid arthritis, age range 20-65 years, were invited to a clinical examination at 4 time-points during the 4 seasons. Outcome measures were: general fatigue rated on visual analogue scale (0-100) and aspects of fatigue assessed by the Bristol Rheumatoid Arthritis Fatigue Multidimensional Questionnaire. Disease-related variables were: disease activity (erythrocyte sedimentation rate), pain threshold (pressure algometer), physical capacity (six-minute walk test), pain (visual analogue scale (0-100)), depressive mood (Hospital Anxiety and Depression scale, depression subscale), personal factors (age, sex, body mass index) and season. Multivariable regression analysis, linear mixed effects models were applied. The strongest explanatory factors for all fatigue outcomes, when recorded at the same time-point as fatigue, were pain threshold and depressive mood. Self-reported pain was an explanatory factor for physical aspects of fatigue and body mass index contributed to explaining the consequences of fatigue on everyday living. For predicting later fatigue pain threshold and depressive mood were the strongest predictors. Pain threshold and depressive mood were the most important factors for fatigue in persons with rheumatoid arthritis.

Social support and clinical and functional outcome in people with schizophrenia.

PubMed

Vázquez Morejón, Antonio J; León Rubio, Jose Mª; Vázquez-Morejón, Raquel

2018-05-01

The impact of Social Support (SS) on the clinical and functional evolution of patients diagnosed with schizophrenia was studied from a multidimensional concept of SS in the framework of the vulnerability-stress model. In total, 152 patients diagnosed with schizophrenia according to the International Classification of Diseases, Tenth Edition (ICD-10) treated in a Community Mental Health Unit were assessed using the Mannheim Interview on Social Support (MISS) and the Brief Psychiatric Rating Scale (BPRS). Then they were followed up for 3 years with a final assessment for the period using the Social Functioning Scale. The impact of SS was explored in clinical and functional measurements with a multiple regression analysis in a 3-year longitudinal prospective design. The quality of Global Social Support (GSS) and satisfaction with GSS appeared to be protective factors from frequency and duration of hospital admissions, with explanatory intensity varying from 9% in survival time to relapse to 13% in number of relapses. Concerning functional measurements, GSS quantity, quality and satisfaction showed an explanatory power for several different dimensions of social functioning, varying from 12% in isolation to 20% in communication. The results confirm SS as a protective factor in the evolution of schizophrenia patients and enable the SS variables with the most explanatory power in their clinical and functional evolution to be identified.

Regression models for estimating salinity and selenium concentrations at selected sites in the Upper Colorado River Basin, Colorado, 2009-2012

USGS Publications Warehouse

Linard, Joshua I.; Schaffrath, Keelin R.

2014-01-01

Elevated concentrations of salinity and selenium in the tributaries and main-stem reaches of the Colorado River are a water-quality concern and have been the focus of remediation efforts for many years. Land-management practices with the objective of limiting the amount of salt and selenium that reaches the stream have focused on improving the methods by which irrigation water is conveyed and distributed. Federal land managers implement improvements in accordance with the Colorado River Basin Salinity Control Act of 1974, which directs Federal land managers to enhance and protect the quality of water available in the Colorado River. In an effort to assist in evaluating and mitigating the detrimental effects of salinity and selenium, the U.S. Geological Survey, in cooperation with the Bureau of Reclamation, the Colorado River Water Resources District, and the Bureau of Land Management, analyzed salinity and selenium data collected at sites to develop regression models. The study area and sites are on the Colorado River or in one of three small basins in Western Colorado: the White River Basin, the Lower Gunnison River Basin, and the Dolores River Basin. By using data collected from water years 2009 through 2011, regression models able to estimate concentrations were developed for salinity at six sites and selenium at six sites. At a minimum, data from discrete measurement of salinity or selenium concentration, streamflow, and specific conductance at each of the sites were needed for model development. Comparison of the Adjusted R2 and standard error statistics of the two salinity models developed at each site indicated the models using specific conductance as the explanatory variable performed better than those using streamflow. The addition of multiple explanatory variables improved the ability to estimate selenium concentration at several sites compared with use of solely streamflow or specific conductance. The error associated with the log-transformed salinity and selenium estimates is consistent in log space; however, when the estimates are transformed into non-log values, the error increases as the estimates decrease. Continuous streamflow and specific conductance data collected at study sites provide the means to examine temporal variability in constituent concentration and load. The regression models can estimate continuous concentrations or loads on the basis of continuous specific conductance or streamflow data. Similar estimates are available for other sites at the USGS National Real-Time Water Quality Web page (http://nrtwq.usgs.gov) and provide water-resource managers with a means of improving their general understanding of how constituent concentration or load can change annually, seasonally, or in real time.

Human influence on California fire regimes.

PubMed

Syphard, Alexandra D; Radeloff, Volker C; Keeley, Jon E; Hawbaker, Todd J; Clayton, Murray K; Stewart, Susan I; Hammer, Roger B

2007-07-01

Periodic wildfire maintains the integrity and species composition of many ecosystems, including the mediterranean-climate shrublands of California. However, human activities alter natural fire regimes, which can lead to cascading ecological effects. Increased human ignitions at the wildland-urban interface (WUI) have recently gained attention, but fire activity and risk are typically estimated using only biophysical variables. Our goal was to determine how humans influence fire in California and to examine whether this influence was linear, by relating contemporary (2000) and historic (1960-2000) fire data to both human and biophysical variables. Data for the human variables included fine-resolution maps of the WUI produced using housing density and land cover data. Interface WUI, where development abuts wildland vegetation, was differentiated from intermix WUI, where development intermingles with wildland vegetation. Additional explanatory variables included distance to WUI, population density, road density, vegetation type, and ecoregion. All data were summarized at the county level and analyzed using bivariate and multiple regression methods. We found highly significant relationships between humans and fire on the contemporary landscape, and our models explained fire frequency (R2 = 0.72) better than area burned (R2 = 0.50). Population density, intermix WUI, and distance to WUI explained the most variability in fire frequency, suggesting that the spatial pattern of development may be an important variable to consider when estimating fire risk. We found nonlinear effects such that fire frequency and area burned were highest at intermediate levels of human activity, but declined beyond certain thresholds. Human activities also explained change in fire frequency and area burned (1960-2000), but our models had greater explanatory power during the years 1960-1980, when there was more dramatic change in fire frequency. Understanding wildfire as a function of the spatial arrangement of ignitions and fuels on the landscape, in addition to nonlinear relationships, will be important to fire managers and conservation planners because fire risk may be related to specific levels of housing density that can be accounted for in land use planning. With more fires occurring in close proximity to human infrastructure, there may also be devastating ecological impacts if development continues to grow farther into wildland vegetation.

Human influence on California fire regimes

USGS Publications Warehouse

Syphard, A.D.; Radeloff, V.C.; Keeley, J.E.; Hawbaker, T.J.; Clayton, M.K.; Stewart, S.I.; Hammer, R.B.

2007-01-01

Periodic wildfire maintains the integrity and species composition of many ecosystems, including the mediterranean-climate shrublands of California. However, human activities alter natural fire regimes, which can lead to cascading ecological effects. Increased human ignitions at the wildland-urban interface (WUI) have recently gained attention, but fire activity and risk are typically estimated using only biophysical variables. Our goal was to determine how humans influence fire in California and to examine whether this influence was linear, by relating contemporary (2000) and historic (1960-2000) fire data to both human and biophysical variables. Data for the human variables included fine-resolution maps of the WUI produced using housing density and land cover data. Interface WUI, where development abuts wildland vegetation, was differentiated from intermix WUI, where development intermingles with wildland vegetation. Additional explanatory variables included distance to WUI, population density, road density, vegetation type, and ecoregion. All data were summarized at the county level and analyzed using bivariate and multiple regression methods. We found highly significant relationships between humans and fire on the contemporary landscape, and our models explained fire frequency (R2 = 0.72) better than area burned (R2 = 0.50). Population density, intermix WUI, and distance to WUI explained the most variability in fire frequency, suggesting that the spatial pattern of development may be an important variable to consider when estimating fire risk. We found nonlinear effects such that fire frequency and area burned were highest at intermediate levels of human activity, but declined beyond certain thresholds. Human activities also explained change in fire frequency and area burned (1960-2000), but our models had greater explanatory power during the years 1960-1980, when there was more dramatic change in fire frequency. Understanding wildfire as a function of the spatial arrangement of ignitions and fuels on the landscape, in addition to nonlinear relationships, will be important to fire managers and conservation planners because fire risk may be related to specific levels of housing density that can be accounted for in land use planning. With more fires occurring in close proximity to human infrastructure, there may also be devastating ecological impacts if development continues to grow farther into wildland vegetation. ?? 2007 by the Ecological Society of America.

Scientists' internal models of the greenhouse effect

NASA Astrophysics Data System (ADS)

Libarkin, J. C.; Miller, H.; Thomas, S. R.

2013-12-01

A prior study utilized exploratory factor analysis to identify models underlying drawings of the greenhouse effect made by entering university freshmen. This analysis identified four archetype models of the greenhouse effect that appear within the college enrolling population. The current study collected drawings made by 144 geoscientists, from undergraduate geoscience majors through professionals. These participants scored highly on a standardized assessment of climate change understanding and expressed confidence in their understanding; many also indicated that they teach climate change in their courses. Although geoscientists held slightly more sophisticated greenhouse effect models than entering freshmen, very few held complete, explanatory models. As with freshmen, many scientists (44%) depict greenhouse gases in a layer in the atmosphere; 52% of participants depicted this or another layer as a physical barrier to escaping energy. In addition, 32% of participants indicated that incoming light from the Sun remains unchanged at Earth's surface, in alignment with a common model held by students. Finally, 3-20% of scientists depicted physical greenhouses, ozone, or holes in the atmosphere, all of which correspond to non-explanatory models commonly seen within students and represented in popular literature. For many scientists, incomplete models of the greenhouse effect are clearly enough to allow for reasoning about climate change. These data suggest that: 1) better representations about interdisciplinary concepts, such as the greenhouse effect, are needed for both scientist and public understanding; and 2) the scientific community needs to carefully consider how much understanding of a model is needed before necessary reasoning can occur.

A database for propagation models

NASA Technical Reports Server (NTRS)

Kantak, Anil V.; Suwitra, Krisjani; Le, Choung

1994-01-01

A database of various propagation phenomena models that can be used by telecommunications systems engineers to obtain parameter values for systems design is presented. This is an easy-to-use tool and is currently available for either a PC using Excel software under Windows environment or a Macintosh using Excel software for Macintosh. All the steps necessary to use the software are easy and many times self-explanatory; however, a sample run of the CCIR rain attenuation model is presented.

Distancing, not embracing, the Distancing-Embracing model of art reception.

PubMed

Davies, Stephen

2017-01-01

Despite denials in the target article, the Distancing-Embracing model appeals to compensatory ideas in explaining the appeal of artworks that elicit negative affect. The model also appeals to the deflationary effects of psychological distancing. Having pointed to the famous rejection in the 1960s of the view that aesthetic experience involves psychological distancing, I suggest that "distance" functions here as a weak metaphor that cannot sustain the explanatory burden the theory demands of it.

Nitrate variability in groundwater of North Carolina using monitoring and private well data models.

PubMed

Messier, Kyle P; Kane, Evan; Bolich, Rick; Serre, Marc L

2014-09-16

Nitrate (NO3-) is a widespread contaminant of groundwater and surface water across the United States that has deleterious effects to human and ecological health. This study develops a model for predicting point-level groundwater NO3- at a state scale for monitoring wells and private wells of North Carolina. A land use regression (LUR) model selection procedure is developed for determining nonlinear model explanatory variables when they are known to be correlated. Bayesian Maximum Entropy (BME) is used to integrate the LUR model to create a LUR-BME model of spatial/temporal varying groundwater NO3- concentrations. LUR-BME results in a leave-one-out cross-validation r2 of 0.74 and 0.33 for monitoring and private wells, effectively predicting within spatial covariance ranges. Results show significant differences in the spatial distribution of groundwater NO3- contamination in monitoring versus private wells; high NO3- concentrations in the southeastern plains of North Carolina; and wastewater treatment residuals and swine confined animal feeding operations as local sources of NO3- in monitoring wells. Results are of interest to agencies that regulate drinking water sources or monitor health outcomes from ingestion of drinking water. Lastly, LUR-BME model estimates can be integrated into surface water models for more accurate management of nonpoint sources of nitrogen.

Methodological development for selection of significant predictors explaining fatal road accidents.

PubMed

Dadashova, Bahar; Arenas-Ramírez, Blanca; Mira-McWilliams, José; Aparicio-Izquierdo, Francisco

2016-05-01

Identification of the most relevant factors for explaining road accident occurrence is an important issue in road safety research, particularly for future decision-making processes in transport policy. However model selection for this particular purpose is still an ongoing research. In this paper we propose a methodological development for model selection which addresses both explanatory variable and adequate model selection issues. A variable selection procedure, TIM (two-input model) method is carried out by combining neural network design and statistical approaches. The error structure of the fitted model is assumed to follow an autoregressive process. All models are estimated using Markov Chain Monte Carlo method where the model parameters are assigned non-informative prior distributions. The final model is built using the results of the variable selection. For the application of the proposed methodology the number of fatal accidents in Spain during 2000-2011 was used. This indicator has experienced the maximum reduction internationally during the indicated years thus making it an interesting time series from a road safety policy perspective. Hence the identification of the variables that have affected this reduction is of particular interest for future decision making. The results of the variable selection process show that the selected variables are main subjects of road safety policy measures. Published by Elsevier Ltd.

Commentary on: Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research. Addictions as a psychosocial and cultural construction.

PubMed

van der Linden, Martial

2015-09-01

This commentary proposes a complementary perspective to that developed by Billieux, Schimmenti, Khazaal, Maurage and Heeren (2015). The addiction-as-disease approach tends to sideline explanatory factors of a psychosocial, cultural, political, or historical nature. I therefore suggest taking into account not only the personal characteristics (loss of self-control, impulsivity) related to the disease model, but also the social determinants of addictive behaviors (weak social ties, social exclusion, hyperindividualism, poverty, unemployment, etc.). Moreover, the disease model of addiction removes addictive behaviors from the cultural and historical contexts that shape them. I argue that the cultural and historical reasons for which certain factors (such as loss of self-control) became so important in the explanation of addictive behaviors should be more thoroughly considered.

Soft matter perspective on protein crystal assembly.

PubMed

Fusco, Diana; Charbonneau, Patrick

2016-01-01

Crystallography may be the gold standard of protein structure determination, but obtaining the necessary high-quality crystals is also in some ways akin to prospecting for the precious metal. The tools and models developed in soft matter physics to understand colloidal assembly offer some insights into the problem of crystallizing proteins. This topical review describes the various analogies that have been made between proteins and colloids in that context. We highlight the explanatory power of patchy particle models, but also the challenges of providing guidance for crystallizing specific proteins. We conclude with a presentation of possible future research directions. This review is intended for soft matter scientists interested in protein crystallization as a self-assembly problem, and as an introduction to the pertinent physics literature for protein scientists more generally. Copyright © 2015 Elsevier B.V. All rights reserved.

Topological self-organization and prediction learning support both action and lexical chains in the brain.

PubMed

Chersi, Fabian; Ferro, Marcello; Pezzulo, Giovanni; Pirrelli, Vito

2014-07-01

A growing body of evidence in cognitive psychology and neuroscience suggests a deep interconnection between sensory-motor and language systems in the brain. Based on recent neurophysiological findings on the anatomo-functional organization of the fronto-parietal network, we present a computational model showing that language processing may have reused or co-developed organizing principles, functionality, and learning mechanisms typical of premotor circuit. The proposed model combines principles of Hebbian topological self-organization and prediction learning. Trained on sequences of either motor or linguistic units, the network develops independent neuronal chains, formed by dedicated nodes encoding only context-specific stimuli. Moreover, neurons responding to the same stimulus or class of stimuli tend to cluster together to form topologically connected areas similar to those observed in the brain cortex. Simulations support a unitary explanatory framework reconciling neurophysiological motor data with established behavioral evidence on lexical acquisition, access, and recall. Copyright © 2014 Cognitive Science Society, Inc.

Process, mechanism, and explanation related to externalizing behavior in developmental psychopathology.

PubMed

Hinshaw, Stephen P

2002-10-01

Advances in conceptualization and statistical modeling, on the one hand, and enhanced appreciation of transactional pathways, gene-environment correlations and interactions, and moderator and mediator variables, on the other, have heightened awareness of the need to consider factors and processes that explain the development and maintenance of psychopathology. With a focus on attentional problems, impulsivity, and disruptive behavior patterns, I address the kinds of conceptual approaches most likely to lead to advances regarding explanatory models in the field. Findings from my own research program on processes and mechanisms reveal both promise and limitations. Progress will emanate from use of genetically informative designs, blends of variable and person-centered research, explicit testing of developmental processes, systematic approaches to moderation and mediation, exploitation of "natural experiments," and the conduct of prevention and intervention trials designed to accentuate explanation as well as outcome. In all, breakthroughs will occur only with advances in translational research-linking basic and applied science-and with the further development of transactional, systemic approaches to explanation.

Performance of comorbidity, risk adjustment, and functional status measures in expenditure prediction for patients with diabetes.

PubMed

Maciejewski, Matthew L; Liu, Chuan-Fen; Fihn, Stephan D

2009-01-01

To compare the ability of generic comorbidity and risk adjustment measures, a diabetes-specific measure, and a self-reported functional status measure to explain variation in health care expenditures for individuals with diabetes. This study included a retrospective cohort of 3,092 diabetic veterans participating in a multisite trial. Two comorbidity measures, four risk adjusters, a functional status measure, a diabetes complication count, and baseline expenditures were constructed from administrative and survey data. Outpatient, inpatient, and total expenditure models were estimated using ordinary least squares regression. Adjusted R(2) statistics and predictive ratios were compared across measures to assess overall explanatory power and explanatory power of low- and high-cost subgroups. Administrative data-based risk adjusters performed better than the comorbidity, functional status, and diabetes-specific measures in all expenditure models. The diagnostic cost groups (DCGs) measure had the greatest predictive power overall and for the low- and high-cost subgroups, while the diabetes-specific measure had the lowest predictive power. A model with DCGs and the diabetes-specific measure modestly improved predictive power. Existing generic measures can be useful for diabetes-specific research and policy applications, but more predictive diabetes-specific measures are needed.

Exploring the Synergy between Science Literacy and Language Literacy with English Language Learners: Lessons Learned within a Sustained Professional Development Program

ERIC Educational Resources Information Center

Carrejo, David J.; Reinhartz, Judy

2012-01-01

Thirty-five elementary teachers participated in a yearlong professional development (PD) program whose goal was to foster science content learning while promoting language literacy for English Language Learners (ELL). The researchers utilized an explanatory design methodology to determine the degree to which science and language literacy…

Short-Term Energy Outlook Model Documentation: Macro Bridge Procedure to Update Regional Macroeconomic Forecasts with National Macroeconomic Forecasts

EIA Publications

2010-01-01

The Regional Short-Term Energy Model (RSTEM) uses macroeconomic variables such as income, employment, industrial production and consumer prices at both the national and regional1 levels as explanatory variables in the generation of the Short-Term Energy Outlook (STEO). This documentation explains how national macroeconomic forecasts are used to update regional macroeconomic forecasts through the RSTEM Macro Bridge procedure.

The psychological interdependence of family, school, and bureaucracy in Japan.

PubMed

Kiefer, C W

1970-02-01

The Japanese "examination hell" phenomenon is viewed as a series of crisis rites through which the child passes from family-centered to peer group - centered values in a "particularistic" society. It is held that this model has greater explanatory power than the "minimization of competition" model proposed by others and that it also helps to explain the phenomenon of student radicalism and centrifugal relationships in middle-class communities.

Comparing hospital costs: what is gained by accounting for more than a case-mix index?

PubMed

Hvenegaard, Anne; Street, Andrew; Sørensen, Torben Højmark; Gyrd-Hansen, Dorte

2009-08-01

We explore what effect controlling for various patient characteristics beyond a case-mix index (DRG) has on inferences drawn about the relative cost performance of hospital departments. We estimate fixed effect cost models in which 3754 patients are clustered within six Danish vascular departments. We compare a basic model including a DRG index only with models also including age and gender, health related characteristics, such as smoking status, diabetes, and American Society of Anesthesiogists score (ASA-score), and socioeconomic characteristics such as income, employment and whether the patient lives alone. We find that the DRG index is a robust and important explanatory factor and adding other routinely collected characteristics such as age and gender and other health related or socioeconomic characteristics do not seem to alter the results significantly. The results are more sensitive to choice of functional form, i.e. in particular to whether costs are log transformed. Our results suggest that the routinely collected characteristics such as DRG index, age and gender are sufficient when drawing inferences about relative cost performance. Adding health related or socioeconomic patient characteristics only slightly improves our model in terms of explanatory power but not when drawing inferences about relative performance. The results are, however, sensitive to whether costs are log transformed.

Moderation analysis using a two-level regression model.

PubMed

Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

2014-10-01

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

Input variable selection and calibration data selection for storm water quality regression models.

PubMed

Sun, Siao; Bertrand-Krajewski, Jean-Luc

2013-01-01

Storm water quality models are useful tools in storm water management. Interest has been growing in analyzing existing data for developing models for urban storm water quality evaluations. It is important to select appropriate model inputs when many candidate explanatory variables are available. Model calibration and verification are essential steps in any storm water quality modeling. This study investigates input variable selection and calibration data selection in storm water quality regression models. The two selection problems are mutually interacted. A procedure is developed in order to fulfil the two selection tasks in order. The procedure firstly selects model input variables using a cross validation method. An appropriate number of variables are identified as model inputs to ensure that a model is neither overfitted nor underfitted. Based on the model input selection results, calibration data selection is studied. Uncertainty of model performances due to calibration data selection is investigated with a random selection method. An approach using the cluster method is applied in order to enhance model calibration practice based on the principle of selecting representative data for calibration. The comparison between results from the cluster selection method and random selection shows that the former can significantly improve performances of calibrated models. It is found that the information content in calibration data is important in addition to the size of calibration data.

Modeling Passive Propagation of Malwares on the WWW

NASA Astrophysics Data System (ADS)

Chunbo, Liu; Chunfu, Jia

Web-based malwares host in websites fixedly and download onto user's computers automatically while users browse. This passive propagation pattern is different from that of traditional viruses and worms. A propagation model based on reverse web graph is proposed. In this model, propagation of malwares is analyzed by means of random jump matrix which combines orderness and randomness of user browsing behaviors. Explanatory experiments, which has single or multiple propagation sources respectively, prove the validity of the model. Using this model, people can evaluate the hazardness of specified websites and take corresponding countermeasures.

Modeling of the financial market using the two-dimensional anisotropic Ising model

NASA Astrophysics Data System (ADS)

Lima, L. S.

2017-09-01

We have used the two-dimensional classical anisotropic Ising model in an external field and with an ion single anisotropy term as a mathematical model for the price dynamics of the financial market. The model presented allows us to test within the same framework the comparative explanatory power of rational agents versus irrational agents with respect to the facts of financial markets. We have obtained the mean price in terms of the strong of the site anisotropy term Δ which reinforces the sensitivity of the agent's sentiment to external news.

The Development of Accounting Education and Practice in an Environment of Socio-Economic Transformation in the Middle East: The Case of Jordan

ERIC Educational Resources Information Center

Alsharari, Nizar Mohammad

2017-01-01

Purpose: The purpose of this paper is to explore the development of accounting education and practice as influenced by the socio-economic transformation in Jordan. Design/methodology/approach: The paper presents an explanatory study of how accounting education and practice has developed in relation to socio-economic change in Jordan, using the…

A Log Logistic Survival Model Applied to Hypobaric Decompression Sickness

NASA Technical Reports Server (NTRS)

Conkin, Johnny

2001-01-01

Decompression sickness (DCS) is a complex, multivariable problem. A mathematical description or model of the likelihood of DCS requires a large amount of quality research data, ideas on how to define a decompression dose using physical and physiological variables, and an appropriate analytical approach. It also requires a high-performance computer with specialized software. I have used published DCS data to develop my decompression doses, which are variants of equilibrium expressions for evolved gas plus other explanatory variables. My analytical approach is survival analysis, where the time of DCS occurrence is modeled. My conclusions can be applied to simple hypobaric decompressions - ascents lasting from 5 to 30 minutes - and, after minutes to hours, to denitrogenation (prebreathing). They are also applicable to long or short exposures, and can be used whether the sufferer of DCS is at rest or exercising at altitude. Ultimately I would like my models to be applied to astronauts to reduce the risk of DCS during spacewalks, as well as to future spaceflight crews on the Moon and Mars.

Building a computer program to support children, parents, and distraction during healthcare procedures.

PubMed

Hanrahan, Kirsten; McCarthy, Ann Marie; Kleiber, Charmaine; Ataman, Kaan; Street, W Nick; Zimmerman, M Bridget; Ersig, Anne L

2012-10-01

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children's responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model outputs to identify overall risk for distress. A decision tree was then applied to evidence-based instructions for tailoring distraction to characteristics and preferences of the parent and child. The resulting decision support computer application, titled Children, Parents and Distraction, is being used in research. Future use will support practitioners in deciding the level and type of distraction intervention needed by a child undergoing a healthcare procedure.

First-rank symptoms in schizophrenia: reexamining mechanisms of self-recognition.

PubMed

Waters, Flavie A V; Badcock, Johanna C

2010-05-01

Disturbances of self are a common feature of schizophrenic psychopathology, with patients reporting that their thoughts and actions are controlled by external forces, as shown in first-rank symptoms (FRS). One widely accepted explanatory model of FRS suggests a deficiency in the internal forward model system. Recent studies in the field of cognitive sciences, however, have generated new insights into how complex sensory and motor systems contribute to the sense of self-recognition, and it is becoming clear that the forward model conceptualization does not have unique access to representations about the self. We briefly evaluate the forward model explanation of FRS, reassess the distinction made between the sense of agency and body ownership, and outline recent developments in 4 domains of sensory-motor control that have supplemented our understanding of the processes underlying the sense of self-recognition. The application of these findings to FRS will open up new research directions into the processes underlying these symptoms.

A new multiple regression model to identify multi-family houses with a high prevalence of sick building symptoms "SBS", within the healthy sustainable house study in Stockholm (3H).

PubMed

Engvall, Karin; Hult, M; Corner, R; Lampa, E; Norbäck, D; Emenius, G

2010-01-01

The aim was to develop a new model to identify residential buildings with higher frequencies of "SBS" than expected, "risk buildings". In 2005, 481 multi-family buildings with 10,506 dwellings in Stockholm were studied by a new stratified random sampling. A standardised self-administered questionnaire was used to assess "SBS", atopy and personal factors. The response rate was 73%. Statistical analysis was performed by multiple logistic regressions. Dwellers owning their building reported less "SBS" than those renting. There was a strong relationship between socio-economic factors and ownership. The regression model, ended up with high explanatory values for age, gender, atopy and ownership. Applying our model, 9% of all residential buildings in Stockholm were classified as "risk buildings" with the highest proportion in houses built 1961-1975 (26%) and lowest in houses built 1985-1990 (4%). To identify "risk buildings", it is necessary to adjust for ownership and population characteristics.

Selection of relevant input variables in storm water quality modeling by multiobjective evolutionary polynomial regression paradigm

NASA Astrophysics Data System (ADS)

Creaco, E.; Berardi, L.; Sun, Siao; Giustolisi, O.; Savic, D.

2016-04-01

The growing availability of field data, from information and communication technologies (ICTs) in "smart" urban infrastructures, allows data modeling to understand complex phenomena and to support management decisions. Among the analyzed phenomena, those related to storm water quality modeling have recently been gaining interest in the scientific literature. Nonetheless, the large amount of available data poses the problem of selecting relevant variables to describe a phenomenon and enable robust data modeling. This paper presents a procedure for the selection of relevant input variables using the multiobjective evolutionary polynomial regression (EPR-MOGA) paradigm. The procedure is based on scrutinizing the explanatory variables that appear inside the set of EPR-MOGA symbolic model expressions of increasing complexity and goodness of fit to target output. The strategy also enables the selection to be validated by engineering judgement. In such context, the multiple case study extension of EPR-MOGA, called MCS-EPR-MOGA, is adopted. The application of the proposed procedure to modeling storm water quality parameters in two French catchments shows that it was able to significantly reduce the number of explanatory variables for successive analyses. Finally, the EPR-MOGA models obtained after the input selection are compared with those obtained by using the same technique without benefitting from input selection and with those obtained in previous works where other data-modeling techniques were used on the same data. The comparison highlights the effectiveness of both EPR-MOGA and the input selection procedure.