title: 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.
authors.affiliation: National Institute of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA.
authors.forename: Jinxiang
authors.initials: J
authors.lastname: Hu
authors.affiliation: College of Education, University of Florida, 1215 Norman Hall, Gainesville, FL, 32611, USA. Walter.Leite@coe.ufl.edu.
authors.forename: Walter L
authors.initials: WL
authors.lastname: Leite
authors.affiliation: Nanjing Normal University, Jiangsu, China.
authors.forename: Miao
authors.initials: M
authors.lastname: Gao
mesh_terms: D006801:Humans; D016014:Linear Models; D018401:Sample Size
keywords: Class enumeration; Growth mixture modeling; Information indices; Likelihood ratio tests; Predictors of class membership