An introduction to latent variable mixture modeling (Part 2): Longitudinal latent class growth analysis and growth mixture models
Abstract
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. © The Author 2013. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved.
Publication Title
Journal of Pediatric Psychology
Recommended Citation
Berlin, K., Parra, G., & Williams, N. (2014). An introduction to latent variable mixture modeling (Part 2): Longitudinal latent class growth analysis and growth mixture models. Journal of Pediatric Psychology, 39 (2), 188-203. https://doi.org/10.1093/jpepsy/jst085
