Child, Youth, and Family Studies, Department of

 

Document Type

Article

Date of this Version

2013

Citation

Published in Journal of Pediatric Psychology, Advance Access (2013); doi: 10.1093/jpepsy/jst085

Comments

Copyright © 2013 Kristoffer S. Berlin, Gilbert R. Parra, and Natalie A. Williams. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. Used by permission.

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 two 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 1998–99 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.

Includes supplemental file.

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