General growth mixture modeling (GGMM) was briefly reviewed and its merits for the researchers who aim to conduct a longitudinal study discussed. The GGMM refers to modeling with categorical latent variables that represent subpopulations where population membership is not known but is inferred from the data (Muthén & Muthén, 2004). Latent class growth analysis (LCGA) and growth mixture modeling (GMM), which are submodels of GGMM, were applied to a single data set in this paper to examine development of antisocial behavior in children. Finally, the data analytical package sem developed by Fox (2006) was introduced, and an example for latent growth curve modeling with time invariant predictors described.