A Bayesian Approach for Analyzing Longitudinal Structural Equation Models

Abstract

This article considers a Bayesian approach for analyzing a longitudinal 2-level nonlinear structural equation model with covariates, and mixed continuous and ordered categorical variables. The first-level model is formulated for measures taken at each time point nested within individuals for investigating their characteristics that are dynamically changed over time. The second level is defined for individuals for assessing their characteristics that are invariant with time. The proposed longitudinal structural equation model also accommodates missing data. A Bayesian approach is developed for estimation of parameters and model comparison. The newly developed methodologies are applied to a longitudinal study concerning cocaine use.

Keywords:

• Bayesian approach
• Latent variables
• Longitudinal study on cocaine use
• L_v measure
• MCMC algorithm
• Ordered categorical variables