In this paper, we focus on PLS-SEM’s ability to handle models with observable binary outcomes. We examine the different ways in which a binary outcome may appear in a model and distinguish those situations in which a binary outcome is indeed problematic versus those in which one can easily incorporate it into a PLS-SEM analysis. Explicating such details enables IS researchers to distinguish different situations rather than avoid PLS-SEM altogether whenever a binary indicator presents itself. In certain situations, one can adapt PLS-SEM to analyze structural models with a binary observable variable as the endogenous construct. Specifically, one runs the PLS-SEM first stage as is. Subsequently, one uses the output for the binary variable and latent variable antecedents from this analysis in a separate logistic regression or discriminant analysis to estimate path coefficients for just that part of the structural model. We also describe a method—regularized generalized canonical correlation analysis (RGCCA)—from statistics, which is similar to PLS-SEM but unequivocally allows binary outcomes.