Longitudinal data analysis : a practical guide for researchers in aging, health, and social sciences / edited by Jason T. Newsom, Richard N. Jones, Scott M. Hofer.

Includes bibliographical references and index.

'This book provides accessible treatment to state-of-the-art approaches to analyzing longitudinal studies. Comprehensive coverage of the most popular analysis tools allows readers to pick and choose the techniques that best fit their research. The analyses are illustrated with examples from 12 major longitudinal data sets including practical information about their content and design. Illustrations from popular software packages offer tips on how to interpret the results. Each chapter features suggested readings fur further study and a list of articles that further illustrate how to implement the analysis and report the results. An accompanying website provides syntax examples for several software packages for each of the chapter examples. Although many of the examples address health or social science questions related to aging, readers from other disciplines will find the analyses relevant to their work. In addition to demonstrating statistical analysis of longitudinal data, the book shows how to interpret and analyze the results within the context of the research design. Although most chapters emphasize the use of large studies collected over long term periods, much of the book is also relevant to researchers who analyze data collected in shorter time periods. The book opens with issues related to using publicly available data sets including a description of the goals, designs, and measures of the data. The next 10 chapters provide non-technical, practical introductions to the concepts and issues relevant to longitudinal analysis, including: weighting samples and adjusting designs for longitudinal studies; missing data and attrition; measurement issues related to longitudinal research; the use of ANOVA and regression for averaging change over time; mediation analysis for analyzing causal processes; growth curve models using multilevel regression; longitudinal hypotheses using structural equation modeling (SEM); latent growth curve models for evaluating individual trajectories of change; dynamic SEM models of change; and survival (event) analysis. Examples from longitudinal data sets such as the Health and Retirement Study, the Longitudinal Study of Aging, and Established Populations for Epidemiologic Studies of the Elderly as well as international data sets such as the Canadian National Population Health Survey and the English Longitudinal Study of Aging, illustrate key concepts. An ideal supplement for graduate level courses on data analysis and/or longitudinal modeling taught in psychology, gerontology, human development, family studies, medicine, sociology, social work, and other behavioral, social, and health sciences, this multidisciplinary book will also appeal to researchers in these fields.'-- Provided by publisher.