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, the authors describe several special features of dominance analysis that allow researchers to study more specific and narrowly focused aspects of relative importance. These analyses offer new insights into the pattern of importance that are not provided by any of the global measures of importance.
Azen, R. (2000). Inference for predictor comparisons: Dominance analysis and the distribution of R2 differences. Unpublished doctoral dissertation, University of Illinois at Urbana-Champaign.
2.
Azen, R., & Budescu, D. V. (2003). The dominance analysis approach for comparing predictors in multiple regression. Psychological Methods, 8, 129-148.
3.
Azen, R., & Budescu, D. V. (2004). Comparing predictors in multivariate regression models: An extension of dominance analysis. Manuscript submitted for publication.
4.
Baltes, B. B., Parker, C. P., Young, L. M., Huff, J. W., & Altmann, R. (2004). The practical utility of importance measures in assessing the relative importance of work-related perceptions and organizational characteristics on work-related outcomes. Organizational Research Methods, 7, 326-340.
5.
Budescu, D. V. (1993). Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psychological Bulletin, 114, 542-551.
6.
Cramer, E. M., & Nicewander, A. (1979). Some symmetric, invariant, measures of multivariate association. Psychometrika, 44, 43-54.
7.
Darlington, R. B. (1968). Multiple regression in psychological research and practice. Psychological Bulletin, 69, 161-182.
8.
Efron, B. (1979). Bootstrap methods: Another look at the jackknife. Annals of Statistics, 9, 1218-128.
9.
French, S. (1988). Decision theory: An introduction to the mathematics of rationality. Chichester, UK: Ellis Horwood.
10.
Johnson, J. W. (2000). A heuristic method for estimating the relative weight of predictor variables in multiple regression. Multivariate Behavioral Research, 35, 1-19.
11.
Johnson, J. W. (2004). Factors affecting relative weights: The influence of sampling and measurement error. Organizational Research Methods, 7, 283-299.
12.
Johnson, J. W., & LeBreton, J. M. (2004). History and use of relative importance indices in organizational research. Organizational Research Methods, 7, 238-257.
13.
Johnson, R. A., & Wichern, D. W. (2002). Applied multivariate statistical analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.
14.
Kutner, M. H., Nachtsheim, C. J., & Neter, J. (2003). Applied linear regression models (4th ed.). Boston: McGraw-Hill.
15.
LeBreton, J. M., Binning, J. F., Adorno, A. J., & Melcher, K. M. (2004). Importance of personality and job-specific affect for predicting job attitudes and withdrawal behavior. Organizational Research Methods, 7, 300-325.
16.
van den Burg, W., & Lewis, C. (1988). Some properties of two measures of multivariate association. Psychometrika, 53, 109-122.
