Nonlinear Structured Growth Mixture Models in Mplusand OpenMx

Kevin J. Grimm · Nilám Ram · Ryne Estabrook

59 citations0/0 files storedMultivariate Behavioral Research

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Psychometric Methodologies and Testing Statistical Methods and Bayesian Inference Bayesian Methods and Mixture Models

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.

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References

2007Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study 2000Integrating Person‐Centered and Variable‐Centered Analyses: Growth Mixture Modeling With Latent Trajectory Classes 1990Latent Curve Analysis 2011OpenMx: An Open Source Extended Structural Equation Modeling Framework 2009Methods and Measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups 2003Distributional Assumptions of Growth Mixture Models: Implications for Overextraction of Latent Trajectory Classes.2012Handbook of developmental research methods 1979Longitudinal Research in the Study of Behavior and Development

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2020Process and context: Longitudinal effects of the interactions between parental involvement, parental warmth, and SES on academic achievement 2016Performance of growth mixture models in the presence of time-varying covariates 2020Self-determination trajectories at work: A growth mixture analysis

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