For years, organizational scholars have sought effective ways to evaluate the importance of
predictors included in a regression analysis. Recent techniques, such as general …

The search for a meaningful index of the relative importance of predictors in multiple
regression has been going on for years. This type of index is often desired when the …

In this primer we present a hands-on introduction to relative importance analysis as a way of
exploring the relative importance of predictors in regression analysis. This method is …

This article advocates for the wider use of relative importance indices as a supplement to
multiple regression analyses. The goal of such analyses is to partition explained variance …

In many disciplines, including various management science fields, researchers have shown
interest in assigning relative importance weights to a set of explanatory variables in …

Techniques such as dominance analysis and relative weight analysis have been proposed
recently to evaluate more accurately predictor importance in ordinary least squares (OLS) …

This article reports the results of a Monte Carlo simulation comparing four different indices of
relative importance (squared correlation, squared beta, product measure, epsilon) to a …

This article provides a reanalysis of JW Johnson's (2000)“relative weights” method for
assessing variable importance in multiple regression. The primary conclusion of the …

Ambiguity surrounds any importance measure in cases in which predictor variables are
correlated. However, a new measure is proposed that has attractive properties, such as …

The relative weight of predictor variables in multiple regression is difficult to determine
because of non-zero predictor intercorrelations. Relative weight (also called relative …