The existence of several accessible sources has led to a proliferation of mediation models in the applied research literature. Most of these sources assume endogenous variables (e.g., M, and Y) have normally distributed residuals, precluding models of binary and/or count data. Although a growing body of literature has expanded mediation models to include more diverse data types, the nonlinearity of these models presents a substantial hurdle to their implementation and interpretation. The present study extends the existing literature (e.g., Hayes & Preacher, 2010; Stolzenberg, 1980) to propose conditional indirect effects as a useful tool for understanding mediation models that include paths estimated using the Generalized Linear Model (e.g., logistic regression, Poisson regression). We briefly review the relevant literature, culminating in a discussion of conditional indirect effects and their importance when examining nonlinear associations. We present a simple extension of the equations presented by Hayes and Preacher (2010) and provide an applied example of the technique.