On the Misconception of Multicollinearity in Detection of Moderating Effects: Multicollinearity Is Not Always Detrimental

TLDR

Moderated multiple regression is widely used to analyze interaction effects between two continuous predictors, and attention has focused on the supposed multicollinearity between predictors and their cross‑product term. This article clarifies the misconception that multicollinearity is always harmful in MMR and explores its counterintuitive benefits for detecting moderator relationships. The authors present comprehensive treatments and numerical investigations for the simplest interaction model and a more complex three‑predictor setting. The results show that multicollinearity can actually aid detection of moderator effects, providing insights that help avoid misleading interpretations and improve understanding of intercorrelation impacts in MMR analyses.

Abstract

Due to its extensive applicability and computational ease, moderated multiple regression (MMR) has been widely employed to analyze interaction effects between 2 continuous predictor variables. Accordingly, considerable attention has been drawn toward the supposed multicollinearity problem between predictor variables and their cross-product term. This article attempts to clarify the misconception of multicollinearity in MMR studies. The counterintuitive yet beneficial effects of multicollinearity on the ability to detect moderator relationships are explored. Comprehensive treatments and numerical investigations are presented for the simplest interaction model and more complex three-predictor setting. The results provide critical insight that both helps avoid misleading interpretations and yields better understanding for the impact of intercorrelation among predictor variables in MMR analyses.