Interpreting moderated multiple regression: A comment on Van Iddekinge, Aguinis, Mackey, and DeOrtentiis (2018)

When data contradict theory, data usually win. Yet, the conclusion of Van Iddekinge, Aguinis, Mackey, and DeOrtentiis (2018) that performance is an additive rather than multiplicative function of ability and motivation may not be valid, despite applying a meta-analytic lens to the issue. We argue that the conclusion was likely reached because of a common error in the interpretation of moderated multiple-regression results. A Monte Carlo study is presented to illustrate the issue, which is that moderated multiple regression is useful for detecting the presence of moderation but typically cannot be used to determine whether or to what degree the constructs have independent or nonjoint (i.e., additive) effects beyond the joint (i.e., multiplicative) effect. Moreover, we argue that the practice of interpreting the incremental contribution of the interaction term when added to the first-order terms as an effect size is inappropriate, unless the interaction is perfectly symmetrical (i.e., X-shaped), because of the partialing procedure that moderated multiple regression uses. We discuss the importance of correctly specifying models of performance as well as methods that might facilitate drawing valid conclusions about theories with hypothesized multiplicative functions. We conclude with a recommendation to fit the entire moderated multiple-regression model in a single rather than separate steps to avoid the interpretation error highlighted in this article. (PsycInfo Database Record (c) 2021 APA, all rights reserved).