Relative Importance Analysis: A Useful Supplement to Regression Analysis

Scott Tonidandel, James Marshall Lebreton

Research output: Contribution to journalArticlepeer-review

400 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1-9
Number of pages9
JournalJournal of Business and Psychology
Volume26
Issue number1
DOIs
StatePublished - Mar 1 2011

All Science Journal Classification (ASJC) codes

  • Business and International Management
  • Business, Management and Accounting(all)
  • Applied Psychology
  • Psychology(all)

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