The current study investigated a methodological question of whether traditional, additive, quantitative data can be used to address intersectional issues, and illustrated such an approach with a sample of 301 HIV-positive, Latino gay men in the United States. Participants were surveyed using A-CASI. Hierarchical logistic set regression investigated the role of sets of variables reflecting demographic characteristics, gender nonconformity, and gay and ethnic discrimination in relation to depression and gay collective identity. Results showed the discrimination set was related to depression and to gay collective identity, as was gender nonconformity. Follow-up logistic regression showed that both types of discrimination were associated with greater depression, but gender nonconformity was not. Gay discrimination and gender nonconformity were positively associated with gay collective identity, whereas ethnic discrimination was negatively associated. Results are discussed in terms of the use of traditional quantitative data as a potential means of understanding intersectional issues, as well as of contributing to knowledge about individuals facing multiple structural inequalities.