Abstract
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 among multiple predictors to better understand the role played by each predictor in a regression equation. Unfortunately, when predictors are correlated, typically relied upon metrics are flawed indicators of variable importance. To that end, we highlight the key benefits of two relative importance analyses, dominance analysis and relative weight analysis, over estimates produced by multiple regression analysis. We also describe numerous situations where relative importance weights should be used, while simultaneously cautioning readers about the limitations and misconceptions regarding the use of these weights. Finally, we present step-by-step recommendations for researchers interested in incorporating these analyses in their own work and point them to available web resources to assist them in producing these weights.
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Appendix
Appendix
Resources for performing dominance analysis or relative weight analysis can be obtained from the following source. Numerous SAS macros for performing a variety of types of dominance analysis can be found on Razia Azen’s macros page located at https://pantherfile.uwm.edu/azen/www/damacro.html. For those that do not use SAS, an Excel file is available on James LeBreton’s computer programs page (http://www1.psych.purdue.edu/~jlebreto/relative.htm) that aggregates results across multiple individual regression runs to produce the general dominance weights for as many as six predictors. That same website also contains SPSS macros that can produce relative weights from a data set or a correlation matrix and for calculating relative weights for multivariate multiple regression. SAS programs that perform many of the same function and also compute relative weights for logistic regression and test for statistical significance can be found on Scott Tonidandel’s computer programs page (http://www1.davidson.edu/academic/psychology/Tonidandel/TonidandelProgramsMain.htm).
Finally, Jeff Johnson can be contacted via email at Jeff.Johnson@pdri.com, to obtain SPSS syntax for testing the significance of differences between relative weights. The first author has similar programs that can be executed in SAS.
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Tonidandel, S., LeBreton, J.M. Relative Importance Analysis: A Useful Supplement to Regression Analysis. J Bus Psychol 26, 1–9 (2011). https://doi.org/10.1007/s10869-010-9204-3
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DOI: https://doi.org/10.1007/s10869-010-9204-3