Determining the Statistical Significance of Relative Weights

Scott Tonidandel, James M. LeBreton, Jeff W. Johnson

Research output: Contribution to journalArticlepeer-review

206 Scopus citations


Relative weight analysis is a procedure for estimating the relative importance of correlated predictors in a regression equation. Because the sampling distribution of relative weights is unknown, researchers using relative weight analysis are unable to make judgments regarding the statistical significance of the relative weights. J. W. Johnson (2004) presented a bootstrapping methodology to compute standard errors for relative weights, but this procedure cannot be used to determine whether a relative weight is significantly different from zero. This article presents a bootstrapping procedure that allows one to determine the statistical significance of a relative weight. The authors conducted a Monte Carlo study to explore the Type I error, power, and bias associated with their proposed technique. They illustrate this approach here by applying the procedure to published data.

Original languageEnglish (US)
Pages (from-to)387-399
Number of pages13
JournalPsychological Methods
Issue number4
StatePublished - Dec 2009

All Science Journal Classification (ASJC) codes

  • Psychology (miscellaneous)


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