Latent Class and Latent Transition Analysis

Abstract

Often quantities of interest in psychology cannot be observed directly. These unobservable quantities are known as latent variables. By using multiple items as indicators of the latent variable, we can obtain a more complete picture of the construct of interest and estimate measurement error. One approach to latent variable modeling is latent class analysis, a method appropriate for examining the relationship between discrete observed variables and a discrete latent variable. The present chapter will introduce latent class analysis, its extension to repeated measures, and recent developments further extending the latent class model. First, the concept of a latent class and the mathematical model are presented. This is followed by a discussion of parameter restrictions, model fit, and the measurement quality of categorical items. Second, latent class analysis is demonstrated through an examination of the prevalence of depression types in adolescents. Third, longitudinal extensions of the latent class model are presented. This section also contains an empirical example on adolescent depression types, where the previous analysis is extended to examine the stability and change in depression types over time. Finally, several recent developments that further extend the latent class model are introduced.