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Longitudinal Data Analysis

A Practical Guide for Researchers in Aging, Health, and Social Sciences

Edited by Jason Newsom, Richard N. Jones, Scott M. Hofer

Routledge – 2012 – 405 pages

Series: Multivariate Applications Series

Purchasing Options:

  • Add to CartPaperback: $54.95
    978-0-415-87415-1
    July 11th 2011
  • Add to CartHardback: $125.00
    978-0-415-87414-4
    July 15th 2011

Description

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 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 for additional study and a list of articles that further illustrate how to implement the analysis and report the results. Syntax examples for several software packages for each of the chapter examples are provided at www.psypress.com/longitudinal-data-analysis.

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. The methods covered in this book are applicable to a range of applied problems including short- to long-term longitudinal studies using a range of sample sizes.

The book provides non-technical, practical introductions to the concepts and issues relevant to longitudinal analysis. Topics include use of publicly available data sets, weighting and adjusting for complex sampling designs with longitudinal studies, missing data and attrition, measurement issues related to longitudinal research, the use of ANOVA and regression for average change over time, mediation analysis, growth curve models, basic and advanced structural equation models, and survival analysis.

An ideal supplement for graduate level courses on data analysis and/or longitudinal modeling taught in psychology, gerontology, public health, 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.

Reviews

"There is an urgent need for this book. … It is very easy to recommend [Longitudinal Data Analysis] to those investigating ageing and developmental change. It is also suitable for those who work in other fields with longitudinal data. … I’m not aware of such a useful and accessible introduction in my own field or any others." - Chris Beeley, Institute of Mental Health, Nottingham, UK, in The Psychologist

"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. I certainly wish I had a book like this when I was starting out in this area!" - John J. McArdle, University 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

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.

Author Bio

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.

Name: Longitudinal Data Analysis: A Practical Guide for Researchers in Aging, Health, and Social Sciences (Paperback)Routledge 
Description: Edited by Jason Newsom, Richard N. Jones, Scott M. Hofer. 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...
Categories: Factor Analysis, SEM, Multilevel & Longitudinal Modeling, Multivariate Statistics, Quantitative Methods, Medical Statistics & Computing