An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications

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Lawrence Erlbaum Associates, 2006 - Mathematics - 261 pages
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This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader's familiarity with analysis of variance and structural equation modeling (SEM) in introducing LGM techniques. Sample data, syntax, input and output, are provided for EQS, Amos, LISREL, and Mplus on the book's CD. Throughout the book, the authors present a variety of LGM techniques that are useful for many different research designs, and numerous figures provide helpful diagrams of the examples.

Updated throughout, the second edition features three new chapters--growth modeling with ordered categorical variables, growth mixture modeling, and pooled interrupted time series LGM approaches. Following a new organization, the book now covers the development of the LGM, followed by chapters on multiple-group issues (analyzing growth in multiple populations, accelerated designs, and multi-level longitudinal approaches), and then special topics such as missing data models, LGM power and Monte Carlo estimation, and latent growth interaction models. The model specifications previously included in the appendices are now available on the CD so the reader can more easily adapt the models to their own research.

This practical guide is ideal for a wide range of social and behavioral researchers interested in the measurement of change over time, including social, developmental, organizational, educational, consumer, personality and clinical psychologists, sociologists, and quantitative methodologists, as well as for a text on latent variable growth curve modeling or as a supplement for a course on multivariate statistics. A prerequisite of graduate level statistics is recommended.

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About the author (2006)

Terry E. Duncan is a Senior Research Scientist at the Oregon Research Institute. He received his Ph.D. from the University of Oregon and is an active researcher in statistical methods for longitudinal and multilevel designs, structural equation and generalized linear models, approaches for the analysis of missing data, and the etiology of substance use and development.
Susan C. Duncan is a Senior Research Scientist at the Oregon Research Institute. She received her Ph.D. from the University of Oregon. She is known for her work on substantive, statistical, and methodological issues related to youth health risk and health promoting behaviors.
Lisa A. Strycker is a Senior Research Associate and Co-Investigator at the Oregon Research Institute. She received her M.S. in psychology in 1997 from the University of Oregon. Her research interests include statistical methods, chronic illness, and health behaviors.

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