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:
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.
"…a 'good-to-read' book for all researchers who are interested in analyzing repeated measures data in general and a 'must-read' book for those interested in modeling intraindividual variability in particular, be it in their actual substantive research or in teaching others in the classroom…the chapters in the book provide an important contribution in terms of bridging the gap between advances in repeated-measures data analysis and the current knowledge levels (on these techniques) of the majority of researchers in the wide variety of social and behavioral sciences who are not methodological experts."
—Organizational Research Methods
"There is evidence that the chapters underwent some review and revision. In addition, the editors have gone to the trouble to compile author and subject indexes and to write a preface that gives a good overview of the book. These features greatly enhance the text's accessibility and usefulness."
—Journal of the American Statistical Association
"This collection provides a valuable set of articles on modeling repeated measures data. It is especially interesting in that it presents a great deal of variation in approaches, while stressing the links between these approaches. Thus, it is a more comprehensive treatment of repeated-measure analysis than other collections that focus on only one of these methods….this is an excellent addition to the bookshelf of any researcher who is interested in modeling intraindividual variability."
—Contemporary Psychology APA REVIEW OF BOOKS
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.
This series of books offers highly accessible and widely applicable methodological topics that have broad appeal and are written in easy-to understand language. Sponsored by the Society of Multivariate Experimental Psychology http://www.smep.org/, it welcomes methodological applications from a variety of disciplines, such as psychology, public health, sociology, education, and business. Authored or edited volumes should feature one of several approaches:
Interested persons should e-mail: Lisa L. Harlow at LHarlow@uri.edu.