TY - JOUR
T1 - Nonlinear Structured Growth Mixture Models in Mplus and OpenMx
AU - Grimm, Kevin J.
AU - Ram, Nilam
AU - Estabrook, Ryne
N1 - Funding Information:
Kevin J. Grimm was supported by National Science Foundation Reece Program Grant DRL-0815787 and National Center for Research on Early Childhood Education, Institute of Education Sciences, U.S. Department of Education Grant R305A06021 awarded to the University of Virginia. Nilam Ram was supported by the National Institute on Aging (RC1-AG035645, R21-AG032379, R21-AG033109) and the Penn State Social Science Research Institute. Ryne Estabrook was supported by National Institute of Aging Training Grant T32 AG20500-08 while attending the University of Virginia and National Institute on Drug Abuse R25 DA026119-03 awarded to Virginia Commonwealth University.
PY - 2010/11
Y1 - 2010/11
N2 - 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.
AB - 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.
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U2 - 10.1080/00273171.2010.531230
DO - 10.1080/00273171.2010.531230
M3 - Article
AN - SCOPUS:78650506918
SN - 0027-3171
VL - 45
SP - 887
EP - 909
JO - Multivariate Behavioral Research
JF - Multivariate Behavioral Research
IS - 6
ER -