Latent Growth Curve Modeling

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Overview

Latent growth curve modeling (LGM)—a special case of confirmatory factor analysis designed to model change over time—is an indispensable and increasingly ubiquitous approach for modeling longitudinal data. This volume 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.

The book covers the basic linear LGM, and builds from there to describe more complex functional forms (e.g., polynomial latent curves), multivariate latent growth curves used to model simultaneous change in multiple variables, the inclusion of time-varying covariates, predictors of aspects of change, cohort-sequential designs, and multiple-group models. The authors also highlight approaches to dealing with missing data, different estimation methods, and incorporate discussion of model evaluation and comparison within the context of LGM. The models demonstrate how they may be applied to longitudinal data derived from the NICHD Study of Early Child Care and Youth Development (SECCYD)..

Key Features

· Provides easy-to-follow, didactic examples of several common growth modeling approaches

· Highlights recent advancements regarding the treatment of missing data, parameter estimation, and model fit

· Explains the commonalities and differences between latent growth model and multilevel modeling of repeated measures data

· Covers the basic linear latent growth model, and builds from there to describe more complex functional forms such as polynomial latent curves, multivariate latent growth curves, time-varying covariates, predictors of aspects of change, cohort-sequential designs, and multiple-group models

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Product Details

Meet the Author

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. Mac Callum, 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.

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Table of Contents

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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