Multivariate relative importance: extending relative weight analysis to multivariate criterion spaces.
For years, organizational scholars have sought effective ways to evaluate the importance of predictors included in a regression analysis. Recent techniques, such as general dominance weights and relative weights, have shown great promise for guiding evaluations of predictor importance. Nevertheless, questions remain regarding how one should investigate relative importance in the presence of a multidimensional criterion variable. The purpose of this article is to extend understanding of relative importance statistics to multivariate designs. The authors review the concept of relative importance and discuss a new procedure for calculating estimates of importance in the presence of multiple correlated criteria. Finally, a published correlation matrix is reanalyzed and a Monte Carlo simulation conducted to compare the new procedure with another technique for estimating importance. Unlike canonical solutions, which are often uninterpretable, the proposed multivariate relative weights provide an intuitive index regarding the relationship between predictors and criteria. Implications for organizational researchers are discussed.
LeBreton
James M
JM
Department of Psychological Sciences, Purdue University, West Lafayette, IN 47907-2081, USA. lebreton@psych.purdue.edu
Tonidandel
Scott
S
D006801:Humans; D008960:Models, Psychological; D009010:Monte Carlo Method; D015279:Organizational Culture; D012044:Regression Analysis