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The five articles in this special issue of Organizational Research Methods focus on various measures of global importance that rank and scale all p predictors in a multiple regression equation and compare their relative merits using a variety of real and simulated data sets. In this commentary,...
Relative weights are used to interpret a regression equation when the researcher is interested in the relative importance of the predictor variables. Like other statistics, relative weights are influenced by sampling and measurement error. To assess the influence of sampling error, a...
This study explores the relative importance of trait-based personality constructs and a state-based job-specific affect construct for predicting job attitudes and withdrawal behaviors of incumbent customer service call center representatives ( N = 150). Results based on three traditional indices...
The utility of dominance analysis and other importance indices is the subject of much debate in determining the relative importance of predictors in multiple regression. The goal in conducting this research was to bring an applied perspective to this issue by comparing the conclusions one would...
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 explanatory aspects of regression analysis are of interest. The authors define relative importance as the proportionate...
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 relatively new method called dominance analysis. Conceptually and empirically, dominance analysis represents an...
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