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ERIC Number: EJ865294
Record Type: Journal
Publication Date: 2009-Dec
Pages: 13
Abstractor: As Provided
ISSN: ISSN-1082-989X
Determining the Statistical Significance of Relative Weights
Tonidandel, Scott; LeBreton, James M.; Johnson, Jeff W.
Psychological Methods, v14 n4 p387-399 Dec 2009
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. (Contains 5 footnotes, 2 tables and 5 figures.)
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A