Prediction from Latent Classes: A Demonstration of Different Approaches to Include Distal Outcomes in Mixture Models

TLDR

Including auxiliary variables such as antecedent and consequent variables in mixture models provides valuable insight into population heterogeneity, yet how to incorporate predictors and outcomes remains an active research area with emerging methods and unclear best practices. This paper reviews methods for estimating the effects of latent class membership on distal outcomes. The authors illustrate recommended methods in Mplus and Latent Gold to model latent class membership and its effects on distal outcomes. The study demonstrates that latent class membership predicts two distal outcomes in students’ mathematics attitudes.

Abstract

Including auxiliary variables such as antecedent and consequent variables in mixture models provides valuable insight in understanding the population heterogeneity embodied by a latent class variable. The model building process regarding how to include predictors/correlates and outcomes of the latent class variables into mixture models is an area of active research. As such, new methods of including these variables continue to emerge and best practices for the application of these methods in real data settings (including simple guidelines for choosing amongst them) are still not well established. This paper focuses on one type of auxiliary variable—distal outcomes—providing an overview of the methods currently available for estimating the effects of latent class membership on subsequent distal outcomes. We illustrate the recommended methods in the software packages Mplus and Latent Gold using a latent class model to capture population heterogeneity in students' mathematics attitudes, linking latent class membership to two distal outcomes.