Assessments of substance use behaviors often include categorical variables that are frequently related to other measures using logistic regression or chi-square analysis. When the categorical variable is latent (e.g., extracted from a latent class analysis), data analysis often entails a three-step approach wherein classification of observations is used to create an observed nominal variable from the latent one for use in a subsequent analysis. However, recent simulation studies have found that this classical three-step process championed by the pioneers of latent class analysis produces underestimates of the associations between latent classes and other variables. Two preferable but underused alternatives for examining such associations, each of which is most appropriate under certain conditions—are (a) corrected three-step analysis and (b) one-step analysis. The purpose of this article is to dissuade researchers from conducting classical three-step analysis and to promote the use of the two newer approaches that are described and compared. In addition, the applications of these newer models—for use when the independent, the dependent, or both categorical variables are latent—are illustrated through substantive analyses relating classes of substance abusers to classes of intimate partner aggressors.