Modeling Intraindividual Variability With Repeated Measures Data

<p>This book examines how individuals behave across time and to what degree that behavior changes, fluctuates, or remains stable.</p><p>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.</p><p>Each chapter features:</p><ul> <li>a minimum discussion of mathematical detail;</li> <li>an empirical example applying the technique; and</li> <li>a discussion of the software related to that technique.</li> </ul><p>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.</p> <p><b>Contents: </b>Preface. <b>D.A.Kenny, N. Bolger, D.A. Kashy,</b> Traditional Methods for Estimating Multilevel Models. <b>S.W. Raudenbush,</b> Alternative Covariance Structures for Polynomial Models of Individual Growth and Change. <b>P.J. Curran, A.M. Hussong,</b> Structural Equation Modeling of Repeated Measures Data: Latent Curve Analysis. <b>J.O. Ramsay,</b> Multilevel Modeling of Longitudinal and Functional Data. <b>D. Wallace, S.B. Green,</b> Analysis of Repeated Measures Designs With Linear Mixed Models. <b>J.D. Singer,</b> Fitting Individual Growth Models Using SAS PROC MIXED. <b>T.E. Duncan, S.C. Duncan, F. Li, L.A. Strycker,</b> Multilevel Modeling of Longitudinal and Functional Data. <b>S. Hillmer,</b> Times Series Regressions. <b>J.R. Nesselroade, J.J. McArdle, S.H. Aggen, J.M. Meyers,</b> Dynamic Factor Analysis Models for Representing Process in Multivariate Time-Series.</p>