Growth mixture modeling: Identifying and predicting unobserved subpopulations with longitudinal data

  • Wang M
  • Bodner T
  • 137

    Readers

    Mendeley users who have this article in their library.
  • 109

    Citations

    Citations of this article.

Abstract

An important limitation of conventional latent-growth modeling (LGM) is that it assumes that all individuals are drawn from one or more observed populations. However, in many applied-research situations, unobserved subpopulations may exist, and their different latent trajectories may be the focus of research to test theory or to resolve inconsistent prior research findings. Conventional LGM does not help to identify and predict these unobserved subpopulations. This article introduces the growth-mixture modeling (GMM) method for these purposes. Given that GMM handles longitudinal data (i.e., nesting of time observations within individuals) and identifies unobserved subpopulations (i.e., the nesting of individuals within latent classes), GMM can be construed as a multilevel modeling technique. The modeling procedure of GMM is illustrated on a simulated data set. Steps in the modeling process are highlighted and limitations, cautions, recommendations, and extensions of using GMM are discussed. Technical references for additional information are noted throughout.

Author-supplied keywords

  • Growth curves
  • Growth-mixture modeling
  • Longitudinal data analysis

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Mo Wang

  • Todd E. Bodner

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free