Search

This valuable book is now in a fully updated second edition that presents the latest developments in longitudinal structural equation modeling (SEM) and new chapters on missing data, the random intercepts cross-lagged panel model (RI-CLPM), longitudinal mixture modeling, and Bayesian SEM. Emphasizing a decision-making approach, leading methodologist Todd D. Little describes the steps of modeling a longitudinal change process. He explains the big picture and technical how-tos of using longitudinal confirmatory factor analysis, longitudinal panel models, and hybrid models for analyzing within-person change. User-friendly features include equation boxes that translate all the elements in every equation, tips on what does and doesn't work, end-of-chapter glossaries, and annotated suggestions for further reading. The companion website provides data sets for the examples--including studies of bullying and victimization, adolescents'emotions, and healthy aging--along with syntax and output, chapter quizzes, and the book's figures. New to This Edition: •Chapter on missing data, with a spotlight on planned missing data designs and the R-based package PcAux. •Chapter on longitudinal mixture modeling, with Whitney Moore. •Chapter on the random intercept cross-lagged panel model (RI-CLPM), with Danny Osborne. •Chapter on Bayesian SEM, with Mauricio Garnier. •Revised throughout with new developments and discussions, such as how to test models of experimental effects.

Although longitudinal social network data are increasingly collected, there are few guides on how to navigate the range of available tools for longitudinal network analysis. The applied social scientist is left to wonder: Which model is most appropriate for my data? How should I get started with this modeling strategy? And how do I know if my model is any good? This book answers these questions. Author Scott Duxbury assumes that the reader is familiar with network measurement, description, and notation, and is versed in regression analysis, but is likely unfamiliar with statistical network methods. The goal of the book is to guide readers towards choosing, applying, assessing, and interpreting a longitudinal network model, and each chapter is organized with a specific data structure or research question in mind. A companion website includes data and R code to replicate the examples in the book.

This Proceedings of a Workshop summarizes the presentations and discussions at the Workshop on Improving Consent and Response in Longitudinal Studies of Aging, which was held virtually and live-streamed on September 27-28, 2021. The workshop was convened by the Committee on National Statistics of the National Academies of Science, Engineering, and Medicine to assist the National Institute on Aging (NIA) with its methodological research agenda and inform the different longitudinal survey programs sponsored by NIA about practices and research to improve response and consent in other survey programs. The workshop was structured to bring together scientists and researchers from multiple disciplines and countries to share their research and insights on how to improve response and consent in large, representative longitudinal studies on aging.

Advances in Longitudinal Survey Methodology Explore an up-to-date overview of best practices in the implementation of longitudinal surveys from leading experts in the field of survey methodologyAdvances in Longitudinal Survey Methodology delivers a thorough review of the most current knowledge in the implementation of longitudinal surveys. The book provides a comprehensive overview of the many advances that have been made in the field of longitudinal survey methodology over the past fifteen years, as well as extending the topic coverage of the earlier volume, “Methodology of Longitudinal Surveys”, published in 2009. This new edited volume covers subjects like dependent interviewing, interviewer effects, panel conditioning, rotation group bias, measurement of cognition, and weighting.New chapters discussing the recent shift to mixed-mode data collection and obtaining respondents'consent to data linkage add to the book's relevance to students and social scientists seeking to understand modern challenges facing data collectors today. Readers will also benefit from the inclusion of:A thorough introduction to refreshment sampling for longitudinal surveys, including consideration of principles, sampling frame, sample design, questionnaire design, and frequencyAn exploration of the collection of biomarker data in longitudinal surveys, including detailed measurements of ill health, biological pathways, and genetics in longitudinal studiesAn examination of innovations in participant engagement and tracking in longitudinal surveys, including current practices and new evidence on internet and social media for participant engagement.An invaluable source for post-graduate students, professors, and researchers in the field of survey methodology, Advances in Longitudinal Survey Methodology will also earn a place in the libraries of anyone who regularly works with or conducts longitudinal surveys and requires a one-stop reference for the latest developments and findings in the field.

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 book has been replaced by Longitudinal Structural Equation Modeling, Second Edition, ISBN 978-1-4625-5314-3.

The National Children's Study (NCS) is planned to be the largest long-term study of environmental and genetic effects on children's health ever conducted in the United States. It proposes to examine the effects of environmental influences on the health and development of approximately 100,000 children across the United States, following them from before birth until age 21. By archiving all of the data collected, the NCS is intended to provide a valuable resource for analyses conducted many years into the future. This book evaluates the research plan for the NCS, by assessing the scientific rigor of the study and the extent to which it is being carried out with methods, measures, and collection of data and specimens to maximize the scientific yield of the study. The book concludes that if the NCS is conducted as proposed, the database derived from the study should be valuable for investigating hypotheses described in the research plan as well as additional hypotheses that will evolve. Nevertheless, there are important weaknesses and shortcomings in the research plan that diminish the study's expected value below what it might be.

The study of social dynamics using quantitative methodology is complex and calls for cutting-edge technical and methodological approaches in social science research. This book presents the existing statistical models and methods available for understanding social change over time. It provides step-by-step instructions for designing and conducting longitudinal research, with special focus on the longitudinal analysis of both quantitative outcomes (for the modeling of change in continuous variables) and qualitative outcomes (for the modeling of events occurring over time). Readers will learn how to study change in variables over time and how to formulate and estimate multivariate longitudinal models to predict such change, mainly using cross-sectional, cross-time and event history analyzes. This text also teaches how to design and implement a study using longitudinal data from the selection and collection of appropriate variables to the most effective ways to analyze and present data for publication in top social science journals.

Longitudinal data are ubiquitous across Medicine, Public Health, Public Policy, Psychology, Political Science, Biology, Sociology and Education, yet many longitudinal data sets remain improperly analyzed. This book teaches the art and statistical science of modern longitudinal data analysis. The author emphasizes specifying, understanding, and interpreting longitudinal data models. He inspects the longitudinal data graphically, analyzes the time trend and covariates, models the covariance matrix, and then draws conclusions. Covariance models covered include random effects, autoregressive, autoregressive moving average, antedependence, factor analytic, and completely unstructured models among others. Longer expositions explore: an introduction to and critique of simple non-longitudinal analyses of longitudinal data, missing data concepts, diagnostics, and simultaneous modeling of two longitudinal variables. Applications and issues for random effects models cover estimation, shrinkage, clustered data, models for binary and count data and residuals and residual plots. Shorter sections include a general discussion of how computational algorithms work, handling transformed data, and basic design issues. This book requires a solid regression course as background and is particularly intended for the final year of a Biostatistics or Statistics Masters degree curriculum. The mathematical prerequisite is generally low, mainly assuming familiarity with regression analysis in matrix form. Doctoral students in Biostatistics or Statistics, applied researchers and quantitative doctoral students in disciplines such as Medicine, Public Health, Public Policy, Psychology, Political Science, Biology, Sociology and Education will find this book invaluable. The book has many figures and tables illustrating longitudinal data and numerous homework problems. The associated web site contains many longitudinal data sets, examples of computer code, and labs to re-enforce thematerial. From the reviews:'...This book is extremely well presented and it has been written in a style that makes its reading really pleasant and enjoyable...I highly recommend Modeling Longitudinal Data as a good reference book for anyone interested in looking into the art and statistical science of modern longitudinal data analysis.'Journal of Applied Statistics, December 2005'The book is clearly written and well presented. The author's accumulated experience in presenting the material comes over. On balance, this is one of the books which anyone about to teach a practical course in longitudinal data analysis should consider adopting as the course text.'Short Book Reviews of the ISI, June 2006'...Modeling Longitudinal Data is a welcome addition to the vast literature on longitudinal data analysis. The book requires little in terms of prerequisites but offers a great deal.'Zhigang Zhang for the Journal of the American Statistical Association, December 2006'Overall, Robert Weiss's book can be used as an excellent textbook for a first master-level course in longitudinal data analysis in a statistics or biostatistics program, or as a self-study book for applied researchers interested in this area...The style is very clear, concepts are explained in an engaging way and amply illustrated, and the chapters on covariate selection and modeling the variance-covariance matrix are definite assets.'Ralitza Gueorgueiva for Biostatistics, September 2006

This book provides a systematic exposition of a group-based statistical method for analyzing longitudinal data in the social and behavioral sciences and in medicine. The methods can be applied to a wide range of data, such as that describing the progression of delinquency and criminality over the life course, changes in income over time, the course of a disease or physiological condition, or the evolution of the socioeconomic status of communities. Using real-world research data from longitudinal studies, the book explains and applies this method for identifying distinctive time-based progressions called developmental trajectories. Rather than assuming the existence of developmental trajectories of a specific form before statistical data analysis begins, the method allows the trajectories to emerge from the data itself. Thus, in an analysis of data on Montreal school children, it teases apart four distinct trajectories of physical aggression over the ages 6 to 15, examines predictors of these trajectories, and identifies events that may alter the trajectories.Aimed at consumers of statistical methodology, including social scientists, criminologists, psychologists, and medical researchers, the book presents the statistical theory underlying the method with a mixture of intuition and technical development.