Nonlinear Structured Growth Mixture Models in Mplus and OpenMx

Kevin J. Grimm, Nilam Ram, Ryne Estabrook

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

Growth mixture models (GMMs; B. O. Muthén & Muthén, 2000; B. O. Muthén & Shedden, 1999) are a combination of latent curve models (LCMs) and finite mixture models to examine the existence of latent classes that follow distinct developmental patterns. GMMs are often fit with linear, latent basis, multiphase, or polynomial change models because of their common use, flexibility in modeling many types of change patterns, the availability of statistical programs to fit such models, and the ease of programming. In this article, we present additional ways of modeling nonlinear change patterns with GMMs. Specifically, we show how LCMs that follow specific nonlinear functions can be extended to examine the presence of multiple latent classes using the Mplus and OpenMx computer programs. These models are fit to longitudinal reading data from the Early Childhood Longitudinal Study-Kindergarten Cohort to illustrate their use.

Original languageEnglish (US)
Pages (from-to)887-909
Number of pages23
JournalMultivariate Behavioral Research
Volume45
Issue number6
DOIs
StatePublished - Nov 1 2010

ASJC Scopus subject areas

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)

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