Model Selection Criteria for Latent Growth Models Using Bayesian Methods

Abstract

Research in applied areas, such as statistical, psychological, behavioral, and educational areas, often involves the selection of the best available model from among a large set of candidate models. Considering that there is no well-defined model selection criterion in a Bayesian context and that latent growth mixture models are becoming popular in many areas, the goal of this study is to investigate the performance of a series of model selection criteria in the framework of latent growth mixture models with missing data and outliers in a Bayesian context. This study conducted five simulation studies to cover different cases, including latent growth curve models with missing data, latent growth curve models with missing data and outliers, growth mixture models with missing data and outliers, extended growth mixture models with missing data and outliers, and latent growth models with different classes. Simulation results show that almost all the proposed criteria can effectively identify the true models. This study also illustrated the application of these model selection criteria in real data analysis. The results will help inform the selection of growth models by researchers seeking to provide states with accurate estimates of the growth of their students.