Model-based clustering methods, commonly referred to as finite mixture modeling, have been applied to a wide variety of cross-sectional and longitudinal data to account for heterogeneity in population characteristics. In this article, we elucidate 2 such approaches: growth mixture modeling and latent profile analysis. Both techniques are illustrated using motivation data from 2 studies. General strategies for fitting these classes of mixture models are discussed, as are extensions to other applications.