Dynamic latent class analysis

Abstract

This article describes the general time-intensive longitudinal latent class modeling framework implemented in Mplus. For each individual a latent class variable is measured at each time point and the latent class changes across time follow a Markov process (i.e., a hidden or latent Markov model), with subject-specific transition probabilities that are estimated as random effects. Such a model for single-subject data has been referred to as the regime-switching state-space model. The latent class variable can be measured by continuous or categorical indicators, under the local independence condition, or more generally by a class-specific structural equation model or a dynamic structural equation model. We discuss the Bayesian estimation based on Markov chain Monto Carlo, which allows modeling with arbitrary long time series data and many random effects. The modeling framework is illustrated with several simulation studies.