Class Selection in Growth Mixture Models: Comparing Information Criteria to Nonparametric and Parametric Bayesian Approaches

Selecting the number of latent classes is a critical yet challenging aspect of latent growth mixture modeling (LGMM), with implications for model validity and substantive interpretation. Researchers commonly rely on information criteria to compare models with different numbers of classes, but these methods can be inconsistent, especially when class separation is poor or class sizes are unequal. This study evaluates two alternative Bayesian approaches: (1) the Dirichlet process mixture (DPM) model, a nonparametric method, and (2) the mixture of finite mixtures (MFM) model, a parametric method. Both impose a prior on the number of classes and estimate that number from the data. While the DPM model is theoretically appealing, previous research has found it tends to over-extract small classes. The MFM model, in contrast, offers a more reliable alternative by explicitly modeling the number of classes as a finite random variable. We compare these techniques to traditional information criteria (AIC, BIC, AICc, and aBIC) across varying conditions of sample size, class structure, separation, and indicator reliability. Simulation results highlight key performance differences, and we provide practical guidance for researchers selecting among class number determination methods. Illustrative R code is provided as online supplemental material.