In recent years, methodological advances have provided researchers with improved techniques to model and study longitudinal data. The purpose of the proposed research is to apply general growth mixture modeling (GGMM), one of these new techniques. to two types of longitudinal substance use (SU) data - use of SU services and involvement with SU. Specific aims include the application of GGMM to classify individuals' receipt of services over time into latent classes of individuals with similar growth trajectories and then to relate these person-centered patterns of change to important contextual variables (e.g., gender. family cohesion). Similar analyses will be conducted on the growth trajectories for SU involvement. The study will use existing data of approximately 800 children, adolescents, and young adults with serious emotional disturbances, aged 818 years at the start of the study, who participated in the 7-year National Adolescent and Child Treatment Study as a cost-effective way to test the utility of GGMM for analyzing SU services and SU involvement. Results from this study should provide substantive knowledge to practitioners about which classes of participants benefit from SU services, as well as methodological recommendations to SU service researchers on the use of GGMM.