ABSTRACT
Heck, 2013). Mixture models generally refer to quantitative models with a categorical latent variable that represents mixtures of subpopulations where population membership is not known but, rather, is inferred from the data (Muthén, 2001). Some types of mixture modeling treat possible within-class variation as fixed (i.e., the same model is presented within each class but outcome means may differ across classes), while other types support examining variation among individuals within each identified latent class. The purpose of this type of analysis is to estimate the number and size of the latent classes in the mixture, estimate the response probabilities for each indicator given the latent class, and assign membership in the latent classes to individuals in the population (Duncan et al., 2006). Although the general mixture model can be extended to include continuous latent variables used to classify individuals, in this chapter we will focus only on analyses involving categorical latent variables [for additional details, see Muthén (2002)].
