Impact of prior specifications on performance of Bayesian factor mixture modeling

Factor mixture modeling (FMM) has been increasingly adopted in social, behavioral, and health sciences to identify population heterogeneity by incorporating both continuous latent variables (i.e., latent factors) and categorical latent variables (i.e., latent classes). FMM is known to face a variety of methodological challenges given its model complexity, and this study evaluates the potential of Bayesian estimation, particularly prior specifications, in addressing two challenges of FMM: classification accuracy and parameter recovery. We considered possible scenarios in applied research where subjective beliefs regarding class separation were incorporated into prior specifications such that subjective class separation might be greater or smaller than the true class separation in the population. Results of comprehensive Monte Carlo simulations showed adequate model performance using a moderately informative prior with subjective class separation greater than the true class separation. Practical implications for researchers are provided.