Over the past two decades, individual growth models have become one of the most common methods to analyze change. This chapter introduces three of the most common of these models and highlights common issues in the analysis of change. A common way to examine change involves the use multivariate repeated measures (MRM) designs, of which the most common analysis is repeated measures analysis of variance (ANOVA). Structural equation models (SEM) or multilevel models (MLM) (also referred to as hierarchical linear models (HLM) or mixed models) allow for the estimation of growth-curve models to estimate individuals' change across time. The chapter provides a very brief overview of MLM, SEM, and Growth Mixture Modeling (GMM), as three methods to examine systematic change over time. GMM describe unobserved heterogeneity of participants and categorize participants based on growth trajectories. Measurement issues are especially salient when analyzing change.
