Detecting Unobserved Heterogeneity in Latent Growth Curve Models

Katerina M. Marcoulides, Laura Trichera

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Growth mixture models combine latent growth curve models and finite mixture models to examine the existence of latent classes that follow distinct developmental patterns. Analyses based on these models are becoming quite common in social and behavioral science research because of recent advances in computing, the availability of specialized statistical programs, and the ease of programming. In this article, we show how mixture models can be fit to examine the presence of multiple latent classes by algorithmically grouping or clustering individuals who follow the same estimated growth trajectory based on an evaluation of individual case residuals. The approach is illustrated using empirical longitudinal data along with an easy to use computerized implementation.

Original languageEnglish (US)
Pages (from-to)390-401
Number of pages12
JournalStructural Equation Modeling
Volume26
Issue number3
DOIs
StatePublished - May 4 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018, Copyright © 2019 Taylor & Francis Group, LLC.

Keywords

  • growth mixture modeling
  • individual case residuals
  • latent growth curve models
  • unobserved heterogeneity

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