An Introduction to Latent Variable Mixture Modeling (Part 1): Overview and Cross-Sectional Latent Class and Latent Profile Analyses

TLDR

Latent variable mixture modeling is an emerging person‑centered approach that classifies individuals into unobserved groups with similar patterns, a technique of growing interest to pediatric psychologists studying heterogeneous cross‑sectional data. This article provides a nontechnical introduction to cross‑sectional mixture modeling for pediatric psychologists. The authors review an overview of latent variable mixture modeling and distinguish two cross‑sectional examples. Step‑by‑step examples using the Early Childhood Longitudinal Study–Kindergarten data illustrate how latent class and latent profile analyses can identify groups of children with similar patterns and assess their relationships to variables of interest.

Abstract

Objective Pediatric psychologists are often interested in finding patterns in heterogeneous cross-sectional data. Latent variable mixture modeling is an emerging person-centered statistical approach that models heterogeneity by classifying individuals into unobserved groupings (latent classes) with similar (more homogenous) patterns. The purpose of this article is to offer a nontechnical introduction to cross-sectional mixture modeling. Method An overview of latent variable mixture modeling is provided and 2 cross-sectional examples are reviewed and distinguished. Results Step-by-step pediatric psychology examples of latent class and latent profile analyses are provided using the Early Childhood Longitudinal Study–Kindergarten Class of 1998–1999 data file. Conclusions Latent variable mixture modeling is a technique that is useful to pediatric psychologists who wish to find groupings of individuals who share similar data patterns to determine the extent to which these patterns may relate to variables of interest.