RWA Web: A Free, Comprehensive, Web-Based, and User-Friendly Tool for Relative Weight Analyses

Scott Tonidandel, James M. LeBreton

Research output: Contribution to journalEditorialpeer-review

179 Scopus citations

Abstract

Over the last 15 years, a number of methodological developments have enabled researchers to draw more accurate inferences concerning the relative contribution (i.e., relative importance) among multiple (often correlated) predictor variables in a regression analysis. One such development has been relative weight analysis (RWA). Researchers can use a RWA to decompose the total variance predicted in a regression model (R2) into weights that accurately reflect the proportional contribution of the various predictor variables. Prior to RWA, researchers were forced to rely on traditional statistics (e.g., correlations; standardized regression weights), which are known to yield faulty or misleading information concerning variable importance (especially when predictor variables are correlated with one another, which is often the case in organizational research). Although there has been a surge of interest in RWA over the last 10 years, integration of this statistical tool into organizational research has been hampered by the lack of a user-friendly statistical package for implementing RWA. Indeed, most popular statistical packages (e.g., SPSS, SAS) have yet to include RWA protocols into their regression modules. The purpose of this paper is to present a new, free, comprehensive, web-based, user-friendly resource, RWA-Web, which may be used by anyone having simple access to the internet. Our paper is structured as a tutorial on using RWA-Web to examine relative importance in the classic multiple regression model, the multivariate multiple regression model, and the logistic regression model. We also illustrate how RWA-Web may be used to conduct null hypothesis significance tests using advanced bootstrapping procedures.

Original languageEnglish (US)
Pages (from-to)207-216
Number of pages10
JournalJournal of Business and Psychology
Volume30
Issue number2
DOIs
StatePublished - Jun 1 2015

All Science Journal Classification (ASJC) codes

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

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