Auxiliary Variables in Mixture Modeling: Three-Step Approaches Using M<i>plus</i>

TLDR

The article reviews alternatives to single‑step mixture modeling. The authors evaluate a 3‑step approach for latent class predictors across latent class analysis, latent transition analysis, and growth mixture modeling, assess its robustness to assumption violations (e.g., direct effects), extend it to distal variables, and derive standard errors for the Lanza–Tan–Bray method. Lanza, Tan, and Bray (2013).

Abstract

This article discusses alternatives to single-step mixture modeling. A 3-step method for latent class predictor variables is studied in several different settings, including latent class analysis, latent transition analysis, and growth mixture modeling. It is explored under violations of its assumptions such as with direct effects from predictors to latent class indicators. The 3-step method is also considered for distal variables. The Lanza, Tan, and Bray (2013) method for distal variables is studied under several conditions including violations of its assumptions. Standard errors are also developed for the Lanza method because these were not given in Lanza et al. (2013).