Unrestricted Mixture Models for Class Identification in Growth Mixture Modeling
Min Liu, Gregory Robert Hancock · Educational and Psychological Measurement · 2014
Growth mixture modeling has gained much attention in applied and methodological social science research recently, but the selection of the number of latent classes for such models remains a challenging issue, especially when the assumption of proper model specification is violated. The current simulation study compared the performance of a linear growth mixture model (GMM) for determining the correct number of latent classes against a completely unrestricted multivariate normal mixture model. Results revealed that model convergence is a serious problem that has been underestimated by previous GMM studies. Based on two ways of dealing with model nonconvergence, the performance of the two types of mixture models and a number of model fit indices in class identification are examined and discussed. This article provides suggestions to practitioners who want to use GMM for their research.