What residualizing predictors in regression analyses does (and what it does not do)

• Regression analyses are replacing traditional ANOVAs in some areas of research.
• Some researchers orthogonalize predictors by residualizing.
• Several effects of residualizing are demonstrated and discussed.
• Judging by many authors’ rationales, some of these effects are unexpected.
• Some of the effects of residualizing are quite undesirable.

Psycholinguists are making increasing use of regression analyses and mixed-effects modeling. In an attempt to deal with concerns about collinearity, a number of researchers orthogonalize predictor variables by residualizing (i.e., by regressing one predictor onto another, and using the residuals as a stand-in for the original predictor). In the current study, the effects of residualizing predictor variables are demonstrated and discussed using ordinary least-squares regression and mixed-effects models. Some of these effects are almost certainly not what the researcher intended and are probably highly undesirable. Most importantly, what residualizing does not do is change the result for the residualized variable, which many researchers probably will find surprising. Further, some analyses with residualized variables cannot be meaningfully interpreted. Hence, residualizing is not a useful remedy for collinearity.