Objective: Pediatric psychologists are often interested in finding patterns in heterogeneous longitudinal data. Latent variable mixture modeling is an emerging statistical approach that models such heterogeneity by classifying individuals into unobserved groupings (latent classes) with similar (more homogenous) patterns. The purpose of the second of a 2-article set is to offer a nontechnical introduction to longitudinal latent variable mixture modeling.
Methods: 3 latent variable approaches to modeling longitudinal data are reviewed and distinguished.
Results: Step-by-step pediatric psychology examples of latent growth curve modeling, latent class growth analysis, and growth mixture modeling 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 longitudinal data patterns to determine the extent to which these patterns may relate to variables of interest.
Keywords: growth mixture modeling; latent class growth analysis; latent growth curve modeling; longitudinal data analysis; structural equation modeling.