Bayesian approach to piecewise growth mixture modeling: Issues and applications in school psychology.

Bayesian piecewise growth mixture models (PGMMs) are a powerful statistical tool based on the Bayesian framework for modeling nonlinear, phasic developmental trajectories of heterogeneous subpopulations over time. Although Bayesian PGMMs can benefit school psychology research, their empirical applications within the field remain limited. This article introduces Bayesian PGMMs, addresses three key methodological considerations (i.

Applying Negative Binomial Distribution in Diagnostic Classification Models for Analyzing Count Data.

Diagnostic classification models (DCMs) have been used to classify examinees into groups based on their possession status of a set of latent traits. In addition to traditional item-based scoring approaches, examinees may be scored based on their completion of a series of small and similar tasks. Those scores are usually considered as count variables.