Perils of Partialing: Can Scholars Predict Residualized Variables' Nomological Nets?

Objective: Partialing is a statistical procedure in which the variance shared among two or more constructs is removed, allowing researchers to examine the unique properties of the residualized, partialed, or unique portions of each construct. Although this technique is common, its use has been criticized due to the difficulty faced in interpreting residualized variables, especially when the original constructs were highly correlated. The aim of this study is to test the degree to which psychological researchers from the fields of clinical, social, and personality psychology are able to estimate the nomological network of partialed variables accurately when provided with information on the zero-order relations between the variables and with general personality traits.

Methods: Variables with intercorrelations of varying magnitudes (i.e., anxiety, depression, antisocial personality disorder, and borderline personality disorder) will be used to test whether experts can estimate partialed variables' nomological networks vis-à-vis basic trait profiles. Experts' estimates will be compared to obtained partialed trait profiles via macro (overall profile similarity) and more micro (individual trait comparisons) approaches.