Growth mixture modeling: Analysis with non-Gaussian random eﬀects
This chapter gives an overview of non-Gaussian random-eﬀects modeling in the context of ﬁnite-mixture growth modeling developed in Muthe´n and Shedden (1999), Muthe´n (2001a, 2001b, 2004), and Muthe´n et al. (2002), and extended to cluster samples and clusterlevel mixtures in Asparouhov and Muthe´n (2008). Growth mixture modeling represents
unobserved heterogeneity between the subjects in their development using both random eﬀects (e.g., Laird and Ware, 1982) and ﬁnite mixtures (e.g., McLachlan and Peel, 2000). This allows diﬀerent sets of parameter values for mixture components corresponding to different unobserved subgroups of individuals, capturing latent trajectory classes with diﬀerent growth curve shapes. This chapter discusses examples motivating modeling with such trajectory classes. A general latent-variable modeling framework is presented together with its maximum likelihood estimation. Examples from criminology, mental health, and education are analyzed. The choice of a normal or a non-parametric distribution for the random eﬀects is discussed and investigated using a simulation study. The discussion will refer to growth mixture modeling techniques as implemented in the Mplus program (Muthe´n and Muthe´n, 1998-2007) and input scripts for the analyses are available at http://www.statmodel.com.