title: Nonlinear Structured Growth Mixture Models in Mplusand OpenMx.
abstract: Growth mixture models (GMMs; Muthén & Muthén, 2000; 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 paper, 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 Mplusand OpenMx computer programs. These models are fit to longitudinal reading data from the Early Childhood Longitudinal Study-Kindergarten Cohort to illustrate their use.
authors.affiliation: University of California, Davis.
authors.forename: Kevin J
authors.initials: KJ
authors.lastname: Grimm
authors.affiliation: Pennsylvania State University ; Max Plank Institute of Human Development.
authors.forename: Nilam
authors.initials: N
authors.lastname: Ram
authors.affiliation: University of Virginia ; Virginia Commonwealth University.
authors.forename: Ryne
authors.initials: R
authors.lastname: Estabrook
keywords: Change; Growth; Heterogeneity; Mixture; Structural Equation Modeling