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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.


Applied Statistics | Psycholinguistics and Neurolinguistics | Psychology


NOTICE IN COMPLIANCE WITH PUBLISHER POLICY: This is the Author’s Accepted Manuscript version of a work that was subsequently published in Journal of Memory and Language. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Memory and Language 72: 37-48 (April 2014). doi: 10.1016/j.jml.2013.12.003