Longitudinal Data Analysis：A Practical Guide for Researchers in Aging, Health, and Social Sciences

"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 datasets 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."--

Jason T. Newsom is an Associate Professor in the Institute on Aging at Portland State University. He received his Ph.D. in Social Psychology from Arizona State University in 1993. Dr. Newsom teaches data analysis, advanced data analysis, and research methods. His research focuses on care among physically impaired older adults, social interaction and support among older adults, health behaviors among older adults, and longitudinal research design and analysis.

Richard N. Jones, Sc.D., is an epidemiologist who studies issues regarding mental health and aging. His research focuses on social and environmental correlates and possible modifiers of cognitive aging, and the epidemiology of depression among older adults. He leads several Data Management and Statistical Analysis cores for research based at the Institute for Aging Research at the Hebrew Rehabilitation Center, a Harvard Medical School affiliated long term care hospital in Boston, MA.

Scott M. Hofer is a Professor of Psychology and the Harold Mohr, M.D. and Wilhelma Mohr, M.D. Research Chair in Adult Development and Aging at the University of Victoria, Canada. He received his Ph.D. in Psychology, Adult Development and Aging, from the University of Southern California in 1994. His research focuses on the identification and explanation of individual differences in developmental and aging-related processes and involves analysis of existing longitudinal studies, new data collection efforts using intensive measurement designs, and developments in research methodology focused on measurement and analysis of change. He co-directs the Integrative Analysis of Longitudinal Studies on Aging (IALSA) research network for the coordinated analysis and synthesis of longitudinal research on aging-related change in cognition, health, and personality.