In the social and health sciences, data are often structured hierarchically, with individuals nested within groups. Dyads constitute a special case of hierarchically structured data with variation at both the individual and dyadic level. Analyses of data from dyads pose several challenges due to the interdependence between members within dyads and issues related to small group sizes. Multilevel analytic techniques have been developed and applied to dyadic data in an attempt to resolve these issues. In this article, we describe a set of analyses for modeling individual- and dyad-level influences on binary outcomes using SAS statistical software; and we discuss the benefits and limitations of such an approach. For illustrative purposes, we apply these techniques to estimate individual-dyad-level predictors of viral hepatitis C infection among heterosexual couples in East Harlem, New York City.