Growth Mixture Modeling and Related Techniques for Longitudinal Data

This chapter gives an overview of recent advances in latent variable analysis. Emphasis is placed on the strength of modeling obtained by using a flexible combination of continuous and categorical latent variables. To focus the discussion and make it manageable in scope, analysis of longitudinal data using growth models will be considered. Continuous latent variables are common in growth modeling in the form of random effects that capture individual variation in development over time. The use of categorical latent variables in growth modeling is, in contrast, perhaps less familiar, and new techniques have recently emerged. The aim of this chapter is to show the usefulness of growth model extensions using categorical latent variables. The discussion also has implications for latent variable analysis of cross-sectional data. The chapter begins with two major parts corresponding to continuous outcomes versus categorical outcomes. Within each part, conventional modeling using continuous latent variables will be described