Some Common Myths About Centering Predictor Variables in Moderated Multiple Regression and Polynomial Regression

Abstract

Additive transformations are often offered as a remedy for the common problem of collinearity in moderated regression and polynomial regression analysis. As the authors demonstrate in this article, mean-centering reduces nonessential collinearity but not essential collinearity. Therefore, in most cases, mean-centering of predictors does not accomplish its intended goal. In this article, the authors discuss and explain, through derivation of equations and empirical examples, that mean-centering changes lower order regression coefficients but not the highest order coefficients, does not change the fit of regression models, does not impact the power to detect moderating effects, and does not alter the reliability of product terms. The authors outline the positive effects of mean-centering, namely, the increased interpretability of the results and its importance for moderator analysis in structural equations and multilevel analysis. It is recommended that researchers center their predictor variables when their variables do not have meaningful zero-points within the range of the variables to assist in interpreting the results.