Conducting Confirmatory Latent Class Analysis Using M<i>plus</i>

TLDR

Latent class analysis is a popular exploratory tool for uncovering latent groups from categorical responses, but its exploratory nature can be limiting when prior knowledge about variables and populations exists. The article aims to explain the basic principles of confirmatory latent class analysis and provide the Mplus code needed to implement it. Confirmatory latent class analysis models and tests specific hypotheses about response patterns by imposing parameter constraints, and Mplus offers an accessible platform to implement these constrained models.

Abstract

Latent class analysis (LCA) is an increasingly popular tool that researchers can use to identify latent groups in the population underlying a sample of responses to categorical observed variables. LCA is most commonly used in an exploratory fashion whereby no parameters are specified a priori. Although this exploratory approach is reasonable when very little prior research has been conducted in the area under study, it can be very limiting when much is already known about the variables and population. Confirmatory latent class analysis (CLCA) provides researchers with a tool for modeling and testing specific hypotheses about response patterns in the observed variables. CLCA is based on placing specific constraints on the parameters to reflect these hypotheses. The popular and easy-to-use latent variable modeling software package Mplus can be used to conduct a variety of CLCA types using these parameter constraints. This article focuses on the basic principles underlying the use of CLCA, and the Mplus programming code necessary for carrying it out.