Latent growth curve modeling (LGM) is an indispensable and increasingly ubiquitous approach for modeling longitudinal data. This book introduces LGM techniques to researchers, provides easy-to-follow, didactic examples of several common growth modeling approaches and highlights recent advancements regarding the treatment of missing data, parameter estimation, and model fit.
Kristopher J. Preacher, Ph.D. is an assistant professor of Quantitative Psychology at the University of Kansas. His research focuses primarily on the use of factor analysis, structural equation modeling, and multilevel modeling to analyze longitudinal and correlational data. Other interests include developing techniques to test mediation and moderation hypotheses, bridging the gap between theory and practice, and studying model evaluation and model selection in the application of multivariate methods to social science questions. Aaron L. Wichman is a doctoral candidate in the Social Psychology program at The Ohio State University, where he serves as coordinator for the department's introductory social psychology courses. His research interests focus on social cognition and the application of quantitative techniques to individual differences research, including personality assessment. Robert C. MacCallum, Ph.D. has had a long and distinguished career as a respected quantitative psychologist. His primary research interests involve the study of quantitative models and methods for the study of correlational data, especially factor analysis, structural equation modeling, and multilevel modeling. Of particular interest is the use of such methods for the analysis of longitudinal data, with a focus on individual differences in patterns of change over time. He teaches courses in factor analysis and introductory and advanced structural equation modeling. He currently serves as the program chair of the L. L. Thurstone Psychometric Laboratory at the University of North Carolina at Chapel Hill. Nancy E. Briggs, Ph.D. is a statistician in the Discipline of Public Health at the University of Adelaide. She serves primarily as a data analyst in various research projects in the health and behavioral sciences. Her research and professional interests involve the application of advanced multivariate statistical techniques, such as linear and nonlinear multilevel models and latent variable models, to empirical data.
About the Authors Series Editor Introduction Acknowledgements 1. Introduction 2. Applying LGM to Empirical Data 3. Specialized Extensions 4. Relationships Between LGM and Multilevel Modeling 5. Summary Appendix References
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