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Modeling Intraindividual Variability With Repeated Measures Data

Methods and Applications

• Edited by D. S. Moskowitz, Scott L. Hershberger, D.S. Moskowitz

• Price: $69.95
• Binding: Hardback
• Pages: 296
• Published by: Psychology Press
• Publication Date: 1st October 2001
• ISBN: 978-0-8058-3125-2

About the Book

This book examines how individuals behave across time and to what degree that behavior changes, fluctuates, or remains stable.

It features the most current methods on modeling repeated measures data as reported by a distinguished group of experts in the field. The goal is to make the latest techniques used to assess intraindividual variability accessible to a wide range of researchers. Each chapter is written in a "user-friendly" style such that even the "novice" data analyst can easily apply the techniques.

Each chapter features:

• a minimum discussion of mathematical detail;
• an empirical example applying the technique; and
• a discussion of the software related to that technique.

Content highlights include analysis of mixed, multi-level, structural equation, and categorical data models. It is ideal for researchers, professionals, and students working with repeated measures data from the social and behavioral sciences, business, or biological sciences.

Table of Contents

Contents: Preface. D.A.Kenny, N. Bolger, D.A. Kashy, Traditional Methods for Estimating Multilevel Models. S.W. Raudenbush, Alternative Covariance Structures for Polynomial Models of Individual Growth and Change. P.J. Curran, A.M. Hussong, Structural Equation Modeling of Repeated Measures Data: Latent Curve Analysis. J.O. Ramsay, Multilevel Modeling of Longitudinal and Functional Data. D. Wallace, S.B. Green, Analysis of Repeated Measures Designs With Linear Mixed Models. J.D. Singer, Fitting Individual Growth Models Using SAS PROC MIXED. T.E. Duncan, S.C. Duncan, F. Li, L.A. Strycker, Multilevel Modeling of Longitudinal and Functional Data. S. Hillmer, Times Series Regressions. J.R. Nesselroade, J.J. McArdle, S.H. Aggen, J.M. Meyers, Dynamic Factor Analysis Models for Representing Process in Multivariate Time-Series.