Determining the Statistical Significance of Relative Weights

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 signif...

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Bibliographic Details
Published in:Psychological methods 2009-12, Vol.14 (4), p.387-399
Main Authors: Tonidandel, Scott, LeBreton, James M, Johnson, Jeff W
Format: Article
Language:English
Subjects:
Bias
Biological and medical sciences
Bootstrap methods
Data Interpretation, Statistical
Error of Measurement
Fundamental and applied biological sciences. Psychology
Human
Humans
Judgments
Methodology
Models, Psychological
Monte Carlo Methods
Multiple Regression
Multiple Regression Analysis
Power
Predictor Variables
Psychology - methods
Psychology - statistics & numerical data
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Psychometrics. Statistics. Methodology
Regression Analysis
Sampling
Standard errors
Statistical Inference
Statistical Power
Statistical Regression
Statistical Significance
Statistical Weighting
Statistics. Mathematics
Type I Errors
Citations: Items that cite this one
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Description
Summary: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.
ISSN:1082-989X
1939-1463
DOI:10.1037/a0017735