A Comparative Evaluation of a Conditional Median-Based Bayesian Growth Curve Modeling Approach with Missing Data

Longitudinal data are essential for studying within subject change and between subject differences in change. However, missing data, especially when the observed variables are nonnormal, remain a significant challenge in longitudinal analysis. Full information maximum likelihood estimation (FIML) and a two stage robust estimation (TSRE) are widely used to handle missing data, but their effectiveness may diminish with data skewness, high missingness rates, and nonignorable missingness. Recently, a robust median \textendash based Bayesian (RMB) approach for growth curve modeling (GCM) was proposed to handle nonnormal longitudinal data, yet its performance with missing data has not been fully investigated. This study fills that gap by using Monte Carlo simulations to evaluate RMB relative to FIML and TSRE. Overall, the RMB \textendash based GCM is shown to be a reliable option for managing both ignorable and nonignorable missing data across a variety of distributional scenarios. An empirical example illustrates the application of these methods.