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.
All Science Journal Classification (ASJC) codes
- Business and International Management
- Business, Management and Accounting(all)
- Applied Psychology