Uncovering predictor importance

Relative importance or relative weight analysis is a method to “partition explained variance among multiple predictors to better understand the role played by each predictor in a regression equation” (Tonidandel & LeBreton, 2011).

RWA Web Shiny Apps

  • Multiple Regression

  • Multivariate Regression (in progress)

  • Logistic Regression (in progress)

This site enables users to calculate estimates of relative importance across a variety of situations including multiple regression, multivariate multiple regression, and logistic regression. The link to the left will direct users to an interactive web form where, after providing some key pieces of information, the program will calculate estimates of importance using Johnson's (2000) relative weight analysis, confidence intervals around those weights, and information regarding the statistical significance of those weights.

In addition, users will be able to test whether two weights are significantly different from one another and whether weights are significantly different across groups.

Publications on Relative Importance

Tonidandel, S., & LeBreton, J. M. (2015). RWA web: A free, comprehensive, web-based, and user-friendly tool for relative weight analyses. Journal of Business and Psychology, 30(2), 207-216.

Tonidandel, S., & LeBreton, J. M. (2011). Relative importance analysis: A useful supplement to regression analysis. Journal of Business and Psychology, 26(1), 1-9.

Tonidandel, S., LeBreton, J. M., & Johnson, J. W. (2009). Determining the statistical significance of relative weights. Psychological methods, 14(4), 387.