Time Varying Effect Modeling For The Behavioral Social And Health Sciences

This book is the first to introduce applied behavioral, social, and health sciences researchers to a new analytic method, the time-varying effect model (TVEM). It details how TVEM may be used to advance research on developmental and dynamic processes by examining how associations between variables change across time. The book describes how TVEM is a direct and intuitive extension of standard linear regression; whereas standard linear regression coefficients are static estimates that do not change with time, TVEM coefficients are allowed to change as continuous functions of real time, including developmental age, historical time, time of day, days since an event, and so forth. The book introduces readers to new research questions that can be addressed by applying TVEM in their research. Readers gain the practical skills necessary for specifying a wide variety of time-varying effect models, including those with continuous, binary, and count outcomes. The book presents technical details of TVEM estimation and three novel empirical studies focused on developmental questions using TVEM to estimate age-varying effects, historical shifts in behavior and attitudes, and real-time changes across days relative to an event. The volume provides a walkthrough of the process for conducting each of these studies, presenting decisions that were made, and offering sufficient detail so that readers may embark on similar studies in their own research. The book concludes with comments about additional uses of TVEM in applied research as well as software considerations and future directions. Throughout the book, proper interpretation of the output provided by TVEM is emphasized. Time-Varying Effect Modeling for the Behavioral, Social, and Health Sciences is an essential resource for researchers, clinicians/practitioners as well as graduate students in developmental psychology, public health, statistics and methodology for the social, behavioral, developmental, and public health sciences.

This book is the first to introduce applied behavioral, social, and health sciences researchers to a new analytic method, the time-varying effect model (TVEM). It details how TVEM may be used to advance research on developmental and dynamic processes by examining how associations between variables change across time. The book describes how TVEM is a direct and intuitive extension of standard linear regression; whereas standard linear regression coefficients are static estimates that do not change with time, TVEM coefficients are allowed to change as continuous functions of real time, including developmental age, historical time, time of day, days since an event, and so forth. The book introduces readers to new research questions that can be addressed by applying TVEM in their research. Readers gain the practical skills necessary for specifying a wide variety of time-varying effect models, including those with continuous, binary, and count outcomes. The book presents technical details of TVEM estimation and three novel empirical studies focused on developmental questions using TVEM to estimate age-varying effects, historical shifts in behavior and attitudes, and real-time changes across days relative to an event. The volume provides a walkthrough of the process for conducting each of these studies, presenting decisions that were made, and offering sufficient detail so that readers may embark on similar studies in their own research. The book concludes with comments about additional uses of TVEM in applied research as well as software considerations and future directions. Throughout the book, proper interpretation of the output provided by TVEM is emphasized. Time-Varying Effect Modeling for the Behavioral, Social, and Health Sciences is an essential resource for researchers, clinicians/practitioners as well as graduate students in developmental psychology, public health, statistics and methodology for the social, behavioral, developmental, and public health sciences.

This book harnesses research to illustrate dynamic processes of recovery from alcohol use disorder. Abstinence is not the only way.

In recent years, there has been an explosion of research focused on using technology in healthcare, including web- and mobile- health assessment and intervention tools, as well as smartphone sensors and smart environments for monitoring and promoting health behavior. This work has shown that technology-based therapeutic tools offer considerable promise for monitoring and responding to individuals' health behavior in real-time. They may also function as important clinician-extenders or stand-alone tools, may be cost-effective and may offer countless opportunities for tailoring behavioral monitoring and intervention delivery in a manner that is optimally responsive to each individual's profile and health behavior trajectory over time. Additionally, informational and communication technologies may be used in the context of decision support tools to help individuals better understand and access treatment. Technology may enable entirely new models of healthcare both within and outside of formal systems of care and thus offers the opportunity to revolutionize healthcare delivery. This edited book defines the state of scientific research related to the development, experimental evaluation, and effective dissemination of technology-based therapeutic tools targeting behavioral health. Behavioral Healthcare and Technology provides an overview of current evidence-based approaches to leverage technology to promote behavioral health, including management of substance use, mental health, diet/exercise, medication adherence, as well as chronic disease self-management. Additionally, the book defines the state of implementation research examining models for deploying technology-based behavioral healthcare systems and integrating them into various care settings to increase the quality and reach of evidence-based behavioral healthcare while reducing costs.

Longitudinal Analysis provides an accessible, application-oriented treatment of introductory and advanced linear models for within-person fluctuation and change. Organized by research design and data type, the text uses in-depth examples to provide a complete description of the model-building process. The core longitudinal models and their extensions are presented within a multilevel modeling framework, paying careful attention to the modeling concerns that are unique to longitudinal data. Written in a conversational style, the text provides verbal and visual interpretation of model equations to aid in their translation to empirical research results. Overviews and summaries, boldfaced key terms, and review questions will help readers synthesize the key concepts in each chapter. Written for non-mathematically-oriented readers, this text features: A description of the data manipulation steps required prior to model estimation so readers can more easily apply the steps to their own data An emphasis on how the terminology, interpretation, and estimation of familiar general linear models relates to those of more complex models for longitudinal data Integrated model comparisons, effect sizes, and statistical inference in each example to strengthen readers’ understanding of the overall model-building process Sample results sections for each example to provide useful templates for published reports Examples using both real and simulated data in the text, along with syntax and output for SPSS, SAS, STATA, and Mplus at www.PilesOfVariance.com to help readers apply the models to their own data The book opens with the building blocks of longitudinal analysis—general ideas, the general linear model for between-person analysis, and between- and within-person models for the variance and the options within repeated measures analysis of variance. Section 2 introduces unconditional longitudinal models including alternative covariance structure models to describe within-person fluctuation over time and random effects models for within-person change. Conditional longitudinal models are presented in section 3, including both time-invariant and time-varying predictors. Section 4 reviews advanced applications, including alternative metrics of time in accelerated longitudinal designs, three-level models for multiple dimensions of within-person time, the analysis of individuals in groups over time, and repeated measures designs not involving time. The book concludes with additional considerations and future directions, including an overview of sample size planning and other model extensions for non-normal outcomes and intensive longitudinal data. Class-tested at the University of Nebraska-Lincoln and in intensive summer workshops, this is an ideal text for graduate-level courses on longitudinal analysis or general multilevel modeling taught in psychology, human development and family studies, education, business, and other behavioral, social, and health sciences. The book’s accessible approach will also help those trying to learn on their own. Only familiarity with general linear models (regression, analysis of variance) is needed for this text.

This unique book provides an overview of continuous time modeling in the behavioral and related sciences. It argues that the use of discrete time models for processes that are in fact evolving in continuous time produces problems that make their application in practice highly questionable. One main issue is the dependence of discrete time parameter estimates on the chosen time interval, which leads to incomparability of results across different observation intervals. Continuous time modeling by means of differential equations offers a powerful approach for studying dynamic phenomena, yet the use of this approach in the behavioral and related sciences such as psychology, sociology, economics and medicine, is still rare. This is unfortunate, because in these fields often only a few discrete time (sampled) observations are available for analysis (e.g., daily, weekly, yearly, etc.). However, as emphasized by Rex Bergstrom, the pioneer of continuous-time modeling in econometrics, neither human beings nor the economy cease to exist in between observations. In 16 chapters, the book addresses a vast range of topics in continuous time modeling, from approaches that closely mimic traditional linear discrete time models to highly nonlinear state space modeling techniques. Each chapter describes the type of research questions and data that the approach is most suitable for, provides detailed statistical explanations of the models, and includes one or more applied examples. To allow readers to implement the various techniques directly, accompanying computer code is made available online. The book is intended as a reference work for students and scientists working with longitudinal data who have a Master's- or early PhD-level knowledge of statistics.

A modern, comprehensive treatment of latent class and latent transition analysis for categorical data On a daily basis, researchers in the social, behavioral, and health sciences collect information and fit statistical models to the gathered empirical data with the goal of making significant advances in these fields. In many cases, it can be useful to identify latent, or unobserved, subgroups in a population, where individuals' subgroup membership is inferred from their responses on a set of observed variables. Latent Class and Latent Transition Analysis provides a comprehensive and unified introduction to this topic through one-of-a-kind, step-by-step presentations and coverage of theoretical, technical, and practical issues in categorical latent variable modeling for both cross-sectional and longitudinal data. The book begins with an introduction to latent class and latent transition analysis for categorical data. Subsequent chapters delve into more in-depth material, featuring: A complete treatment of longitudinal latent class models Focused coverage of the conceptual underpinnings of interpretation and evaluationof a latent class solution Use of parameter restrictions and detection of identification problems Advanced topics such as multi-group analysis and the modeling and interpretation of interactions between covariates The authors present the topic in a style that is accessible yet rigorous. Each method is presented with both a theoretical background and the practical information that is useful for any data analyst. Empirical examples showcase the real-world applications of the discussed concepts and models, and each chapter concludes with a "Points to Remember" section that contains a brief summary of key ideas. All of the analyses in the book are performed using Proc LCA and Proc LTA, the authors' own software packages that can be run within the SAS® environment. A related Web site houses information on these freely available programs and the book's data sets, encouraging readers to reproduce the analyses and also try their own variations. Latent Class and Latent Transition Analysis is an excellent book for courses on categorical data analysis and latent variable models at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners in the social, behavioral, and health sciences who conduct latent class and latent transition analysis in their everyday work.

In their later years, Americans of different racial and ethnic backgrounds are not in equally good-or equally poor-health. There is wide variation, but on average older Whites are healthier than older Blacks and tend to outlive them. But Whites tend to be in poorer health than Hispanics and Asian Americans. This volume documents the differentials and considers possible explanations. Selection processes play a role: selective migration, for instance, or selective survival to advanced ages. Health differentials originate early in life, possibly even before birth, and are affected by events and experiences throughout the life course. Differences in socioeconomic status, risk behavior, social relations, and health care all play a role. Separate chapters consider the contribution of such factors and the biopsychosocial mechanisms that link them to health. This volume provides the empirical evidence for the research agenda provided in the separate report of the Panel on Race, Ethnicity, and Health in Later Life.