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Longitudinal Data Analysis


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Table of Contents

N. Huguet, S. D. Cunningham, J. T. Newsom, Existing Longitudinal Data Sets for the Study of Health and Social Aspects of Aging. S. D. Cunningham, N. Huguet, Weighting and Complex Sampling Design Adjustments in Longitudinal Studies. D. Feng, Z. Cong, M. Silverstein, Missing Data and Attrition. D. E. Bontempo, F. M.E. Grouzet, S. M. Hofer, Measurement Issues in the Analysis of Within-Person Change. J. T. Newsom, Basic Longitudinal Analysis Approaches for Continuous and Categorical Variables. D. L. Roth, D. P. MacKinnon, Mediation Analysis with Longitudinal Data. B. A. Shaw, J. Liang, Growth Models with Multilevel Regression. M. J. Rovine, S. Liu, Structural Equation Modeling Approaches to Longitudinal Data. R. N. Jones, Latent Growth Curve Models. A. Jajodia, Dynamic Structural Equation Models of Change. S. E. Graham, J. B. Willett, J. D. Singer, Using Discrete-Time Survival Analysis to Study Event Occurrence.

About the Author

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.


"This first-rate, easily accessible volume is way ahead of the pack. The clear, pragmatic discussion puts even the most challenging longitudinal data analytic techniques within the grasp of graduate students and faculty alike. It's all right here - everything from the identification of data sets to the location of the best software packages to analyze them. What a service to the field!" - Neal Krause, University of Michigan, USA "There are many diverse topics that should be called longitudinal data analysis, and many of the newest are represented in this book -- it runs the gamut from weighting data to the measurement of change to using dynamic and discrete models in analyses. ! I expect this book will help generate really good longitudinal analyses of our most pressing substantive problems.a I certainly wish I had a book like this when I was starting out in this area!" - John J. McArdle, aUniversity of Southern California, USA "[This] book ! covers all [the] important methodological issues in longitudinal age research and incorporates current best methods. ! The book presents recent research methodology in an accessible manner, which should result in general improvements in the way substantive researchers ! approach their research problems. ! Other recent books on longitudinal modeling ! are generally too difficult. ... It combines a series of relatively new techniques in a manageable format." -- Joop Hox, Utrecht University, The Netherlands "I like the book's approach and the focus on 'best practices' as that will be a good fit with the educational goals of many academics who would adopt it for use in their classes on methodology and statistical analysis." - Duane Alwin, Pennsylvania State University, USA "A must-have compendium for scientists ! who work with developmental longitudinal data. ! The recommended further readings are very good. ! I definitely would buy it for personal use ! and would consider making it a required reading for my graduate seminar." -- Kai S. Cortina, University of Michigan, USA

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