An introduction to latent variable mixture modeling (part 1): overview and cross-sectional latent class and latent profile analyses

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.

Keywords: cross-sectional data analysis; latent class; latent profile; person-centered; statistical analysis; structural equation modeling.