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Latent Growth Curve Modeling (Quantitative Applications in the Social Sciences), by Dr. Kristopher J. Preacher, Aaron Lee Wichman, Robert
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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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- Sales Rank: #1227553 in Books
- Brand: Brand: SAGE Publications, Inc
- Published on: 2008-06-27
- Original language: English
- Number of items: 1
- Dimensions: 8.50" h x 5.50" w x .25" l, .30 pounds
- Binding: Paperback
- 112 pages
- Used Book in Good Condition
About 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. 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.
Most helpful customer reviews
1 of 1 people found the following review helpful.
awesome
By jf
Great and easy to understand. Not too technical but detailed enough for a great general understanding of the technique. Would buy again. Hard to find books on LGCM and this is perfect
0 of 0 people found the following review helpful.
If you are a statistician (I am not) and you enjoy drilling down into the equations to see how growth ...
By Michael R
Overall, this is a VERY math intensive resource. I have a fairly strong background in math - I had completed four calculus courses as well as linear and matrix algebra in college. But I am a bit rusty, which made getting through this book a challenge. If you are a statistician (I am not) and you enjoy drilling down into the equations to see how growth models are derived, this is probably a great resource for you. If you are looking for a straight-forward guide that gives enough of the math to make sense of the modeling but that focuses more upon the purpose of growth models and applications, I would recommend Todd Little's text.
0 of 0 people found the following review helpful.
Five Stars
By Ariya
Great book about LGC model! A Brief and detailed examples.
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