Sensitivity of Bayesian Model Fit Indices to the Prior Specification of Latent Growth Models

Longitudinal research often involves relatively small samples and missing values. Under these conditions, Bayesian estimation can still result in accurate parameter estimates for latent growth models (LGMs). However, researchers were limited in their options for assessing model fit. Several new (approximate) model fit indices have been introduced into the Bayesian structural equation modeling framework. Through a simulation study, we examined the performance of these indices for model fit and selection with a latent growth model (LGM). Specifically, this study was designed to assess the impact of different prior specifications on the fit indices across several sample sizes and missing data conditions. Findings suggested that priors that diverge from the population values can interfere with model fit and selection assessment, making correctly specified models appear misspecified. In addition, the approximate fit indices may be more suited for model selection rather than model fit assessment. Implications for applied researchers are discussed.

Automated summary

This study examines the use of Bayesian estimation for parameter estimation in longitudinal research and how to assess model fit and selection. The findings suggest that the choice of prior specification affects model fit and selection assessment, and approximate fit indices are more suitable for model selection.