Analyzing problem-behavior trajectories can be difficult. The data are generally categorical and often quite skewed, violating distributional assumptions of standard normal-theory statistical models. In this paper, we present several currently-available modeling options, all of which make appropriate distributional assumptions for the observed categorical data. Three are based on the generalized linear model: a hierarchical generalized linear model (HGLM), a growth mixture model (GMM), and a latent class growth analysis (LCGA). We also describe a longitudinal latent class analysis (LLCA), which requires fewer assumptions than the first three. Finally, we illustrate all of the models using actual longitudinal adolescent alcohol-use data. We guide the reader through the model-selection process, comparing the results in terms of convergence properties, fit and residuals, parsimony, and interpretability. Advances in computing and statistical software have made the tools for these types of analyses readily accessible to most researchers. Using appropriate models for categorical data will lead to more accurate and reliable results, and their application in real data settings could contribute to substantive advancements in the field of development and the science of prevention.Preventing mental health disorders and problem behaviors, such as delinquency, risky sexual behaviors, and substance use, in childhood and adolescence is critically important to the wellbeing of young people and, ultimately, to our society. Problem behaviors often occur in tandem with one another (Donovan & Jessor, 1985) and are associated with concurrent difficulties, such as family dysfunction, academic failure, and poor peer relationships during childhood and adolescence (e.g., Hawkins, Catalano, & Miller, 1992;Wiesner & Windle, 2004). Later, as these behaviors continue into emerging adulthood, avenues toward a successful life course may be shut off. This can lead to adulthood failures in areas such as work and education, as well as to physical and emotional disorders, all of which are costly to the individual and society as a whole (Hill, White, Chung, Hawkins, & Catalano, 2000;Marmorstein & Iacono, 2005;Wiesner & Silbereisen, 2003). Understanding the etiology of childhood and adolescent problem Correspondence regarding this manuscript should be addressed to: Betsy Feldman; University of California, Berkeley; Graduate School of Education; 3659 Tolman Hall, #1670; Berkeley, CA 94720-1760. Email: bfeldman@ucdavis.edu. Publisher's Disclaimer: The following manuscript is the final accepted manuscript. It has not been subjected to the final copyediting, fact-checking, and proofreading required for formal publication. It is not the definitive, publisher-authenticated version. The American Psychological Association and its Council of Editors disclaim any responsibility or liabilities for errors or omissions of this manuscript version, any version derived from this manuscript by NIH, or other third parties. The published version is available ...