Failure to Detect Moderating Effects: Is Multicollinearity the Problem?

We show that Morris, Sherman, and Mansfield's (1986) contention that multicollinearity causes ordinary least squares-moderated multiple regression (OLS-MMR) to underestimate the importance of moderator effects is incorrect for their own data. Multicollinearity was reduced to the point that it was negligible by transforming predictor variables and moderator variables to standard scores prior to computing cross-product terms. We show the resulting cross-product terms both mathematically and empirically to have near-zero correlations with standardized predictors and moderators. Yet, as Arnold and Evans (1979) showed, the results of OLS-MMR are unchanged by this linear transformation of scale. Morris et al.'s (1986) finding of significant moderator effects when using principal-components regression (PCR) is probably a result of some artifact of PCR.