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Latent Growth Curve Modeling
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Latent Growth Curve Modeling



June 2008 | 112 pages | SAGE Publications, Inc
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.
 
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

Kristopher J. Preacher

Kristopher J. Preacher, Ph.D. is a Professor in the Quantitative Methods program at Vanderbilt University. His research concerns the use (and combination) of structural equation modeling and multilevel modeling to model correlational and longitudinal data. Other interests include developing techniques to test mediation and moderation hypotheses, bridging the gap between substantive theory and statistical practice, and studying model evaluation and model selection in the application of multivariate methods to social science questions. He serves on the editorial boards of Psychological Methods, Multivariate Behavioral Research, and... More About Author

Aaron L. Wichman

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. More About Author

Robert C. MacCallum

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... More About Author

Nancy E. Briggs

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. More About Author

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ISBN: 9781412939553
$42.00

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