8. Structural Equation Modeling for Latent Curve Models

  1. Xin-Yuan Song and
  2. Sik-Yum Lee

Published Online: 18 JUL 2012

DOI: 10.1002/9781118358887.ch8

Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences

Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences

How to Cite

Song, X.-Y. and Lee, S.-Y. (2012) Structural Equation Modeling for Latent Curve Models, in Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781118358887.ch8

Author Information

  1. Department of Statistics, The Chinese University of Hong Kong

Publication History

  1. Published Online: 18 JUL 2012
  2. Published Print: 24 AUG 2012

Book Series:

  1. Wiley Series in Probability and Statistics

Book Series Editors:

  1. Walter A. Shewhart and
  2. Samuel S. Wilks

ISBN Information

Print ISBN: 9780470669525

Online ISBN: 9781118358887

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Keywords:

  • Bayesian methods;
  • categorical variables;
  • latent curve models (LCMs);
  • longitudinal latent variables;
  • structural equation model (SEM)

Summary

Latent curve models (LCMs) are popular longitudinal methods in the analysis of individual differences in the patterns of change, which usually involves a random intercept and a random slope with each pair forming a different trajectory over time. Structural equation models (SEMs) techniques provide powerful tools for developing more useful LCMs for analyzing complex dynamic changes. This chapter introduces the basic LCM and some of its extensions, for example the LCM involving second-order latent variables with ordered categorical variables. Bayesian methods that utilize data augmentation and Markov chain Monte Carlo (MCMC) methods are introduced to analyze the LCMs. The chapter describes the backgrounds of the aforementioned real studies and some LCMs using the longitudinal study of cocaine use as an illustrative example. It presents the Bayesian approach and provides the Bayesian analyses of the two real studies. Finally, the chapter introduces some other extensions of the basic LCM.

Controlled Vocabulary Terms

Bayes estimator; categorical data; Categorical variables; latent class model; longitudinal study