Understanding Variation in Longitudinal Data Using Latent Growth Mixture Modeling

Abstract

Abstract Objective This article guides researchers through the process of specifying, troubleshooting, evaluating, and interpreting latent growth mixture models. Methods Latent growth mixture models are conducted with small example dataset of N = 117 pediatric patients using Mplus software. Results The example and data show how to select a solution, here a 3-class solution. We also present information on two methods for incorporating covariates into these models. Conclusions Many studies in pediatric psychology seek to understand how an outcome changes over time. Mixed models or latent growth models estimate a single average trajectory estimate and an overall estimate of the individual variability, but this may mask other patterns of change shared by some participants. Unexplored variation in longitudinal data means that researchers can miss critical information about the trajectories of subgroups of individuals that could have important clinical implications about how one assess, treats, and manages subsets of individuals. Latent growth mixture modeling is a method for uncovering subgroups (or “classes”) of individuals with shared trajectories that differ from the average trajectory.