Enumeration Index Performance in Generalized Growth Mixture Models: A Monte Carlo Test of Muthén’s (2003) Hypothesis

James Peugh · Xitao Fan

38 citations0/0 files storedStructural Equation Modeling A Multidisciplinary Journal

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

Abstract

Relatively few empirical studies have examined the performance of enumeration indices in correctly identifying longitudinal population heterogeneity when present, and these studies have produced mixed results. In light of these findings, Muthén (2003) suggested that the inclusion of time-invariant (antecedent) and time-varying (concurrent) covariates, as well as allowing the extracted growth mixture classes to predict a distal outcome (consequent covariate), could improve the performance of enumeration indices. This Monte Carlo simulation study examined the performance of a large set of enumeration indices within these generalized growth mixture model (GGMM) conditions, as suggested by Muthén, by manipulating 4 design conditions: (a) sample size, (b) separation distance between adjacent latent growth trajectories, (c) growth trajectory membership proportions, and (d) the amount of mixture model variance explained by the covariates. The findings suggest: (a) at smaller (N = 500) sample sizes, enumeration indices were much more likely to select an incorrect model than the correct one, (b) at larger (N = 3,000) sample sizes, correct model identification occurred only in the largest separation distance and trajectory membership equality conditions, and (c) the inclusion of covariates had a negligible effect at smaller sample sizes, but covariate inclusion had a more favorable effect on data generation model identification in the largest sample size, largest trajectory separation distance, and equal trajectory membership proportions conditions. Suggestions for researchers and future empirical study possibilities are discussed.

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References

2007Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study 1987Factor Analysis and AIC 2004Latent Variable Analysis: Growth Mixture Modeling and Related Techniques for Longitudinal Data 1990Latent Curve Analysis 2005Investigating population heterogeneity with factor mixture models.2003Distributional Assumptions of Growth Mixture Models: Implications for Overextraction of Latent Trajectory Classes.2007Performance of Factor Mixture Models as a Function of Model Size, Covariate Effects, and Class-Specific Parameters 2003Statistical and Substantive Checking in Growth Mixture Modeling: Comment on Bauer and Curran (2003).

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2019Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers 2017An evaluation of the use of covariates to assist in class enumeration in linear growth mixture modeling 2017The Effect of Model Misspecification on Growth Mixture Model Class Enumeration 2016Extracting Spurious Latent Classes in Growth Mixture Modeling With Nonnormal Errors

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