Rethinking the Control of Nuisance Variables in Theory Testing

Abstract

In testing hypotheses, researchers frequently have used multiple regression analysis to control for nuisance variables (i.e., potential confounding variables that are correlated with hypothesized causal variables). In this paper, we highlight limitations of this control strategy, and we discuss fundamental issues that should be considered in deciding whether to use it. Ultimately, we suggest the use of multiple regression analysis sometimes may not improve causal understanding and may actually limit the generalizability of results. Instead of the common practice of controlling for nuisance variables, we suggest that looking at shared variance frequently is a more appropriate test of a theoretical hypothesis.