The Perils of Partialling

TLDR

Partialling independent variables in multiple regression carries notable risks. The article argues that the main overlooked danger of partialling is the ambiguity of a variable’s construct after shared variance is removed. The authors analyze data from 696 inmates using three psychopathy and aggression measures, noting that researchers must decide whether conclusions refer to the original or residualized constructs, and they propose best practices for handling residualized scales. The study found that raw and residualized scores differed markedly in item and scale relationships.

Abstract

Although a powerful technique, the partialling of independent variables from one another in the context of multiple regression analysis poses certain perils. The present article argues that the most important and underappreciated peril is the difficulty in knowing what construct an independent variable represents once the variance shared with other independent variables is removed. The present article presents illustrative analyses in a large sample of inmates (n=696) using three measures from the psychopathy and aggression fields. Results indicate that in terms of relations among items on a single scale and relations between scales, the raw and residualized scores bore little resemblance to one another. It is argued that researchers must decide to which construct-the one represented by the original scale or the one represented by the residualized scale-conclusions are meant to apply. Difficulties in applying the conclusions to the residualized scale are highlighted and best practices suggested.