Social work researchers often use variable-centered approaches such as regression and factor analysis. However, these methods do not capture important aspects of relationships that are often imbedded in the heterogeneity of samples. Latent class analysis (LCA) is one of several person-centered approaches that can capture heterogeneity within and between groups. This method is illustrated in the present study, in which LCA is used to explicate differences in symptomatology in a nonclinical, national representative sample of youths. Data ("N" = 14,738) from the National Longitudinal Study of Adolescent Health were analyzed using externalizing and internalizing behavioral constructs and then validated against a number of sociodemographic characteristics and behavior outcomes typically associated with type and severity of symptomatology. Findings revealed important differences within the externalizing symptomatology construct and class differences across racial and ethnic groups, gender, age categories, and several behavior outcomes. Research and clinical implications on the importance of modeling heterogeneity using a person-centered approach are discussed.