State politics scholars often confront data situations where the explanatory variables in a model are highly related to each other. Such multicollinearity (“MC”) makes it difficult to identify the independent effect that each of these variables has on the outcome of interest. In an effort to circumvent MC, researchers sometimes drop collinear variables from the regression model. Using simulated data, we demonstrate the implications that MC has for statistical estimation and the potential for introducing bias that the omitting-variables approach generates. We also discuss MC in the context of multiplicative interaction models, using research on the influence of the initiative on policy responsiveness as an applied example. We conclude with advice for researchers faced with MC in their datasets.