This article advocates for the wider use of rela- tive importance indices as a supplement to multiple regres- sion 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 vari- able importance. To that end,wehighlight the key benefits of two relative importance analyses, dominance analysis and relative weight analysis, over estimates produced by multi- ple regression analysis. We also describe numerous situa- tions where relative importance weights should be used, while simultaneously cautioning readers about the limita- tions 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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