An evaluation of the use of covariates to assist in class enumeration in linear growth mixture modeling

Abstract

This study examined whether the inclusion of covariates that predict class membership improves class identification in a growth mixture modeling (GMM). We manipulated the degree of class separation, sample size, the magnitude of covariate effect on class membership, the covariance between the intercept and the slope, and fit two models with covariates and an unconditional model. We concluded that correct class identification in GMM requires large sample sizes and class separation, and that unconditional GMM performs better than GMM with covariates if the sample size and class separation are sufficiently large. With small sample sizes, GMM with covariates outperformed unconditional GMM, but the percentage of correct class enumeration was low across different fit criteria.