Determining the Statistical Significance of Relative Weights

被引：221

作者：

Tonidandel, Scott ^{[1
]}

LeBreton, James A. ^{[2
]}

Johnson, Jeff W. ^{[3
]}

机构：

[1] Davidson Coll, Dept Psychol, Davidson, NC 28035 USA

[2] Purdue Univ, Dept Psychol Sci, W Lafayette, IN 47907 USA

[3] Personnel Decis Res Inst, Minneapolis, MN USA

来源：

PSYCHOLOGICAL METHODS | 2009年 / 14卷 / 04期

关键词：

relative weights; relative importance; predictor importance; multiple regression; bootstrapping; MULTIPLE-REGRESSION; PARAMORPHIC REPRESENTATION; ORGANIZATIONAL RESEARCH; STRUCTURE COEFFICIENTS; DOMINANCE ANALYSIS; CLINICAL JUDGMENT; BOOTSTRAP; VARIABLES; PREDICTORS; NUMBER;

D O I：

10.1037/a0017735

中图分类号：

B84 [心理学];

学科分类号：

04 ; 0402 ;

摘要：

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.

引用

页码：387 / 399

页数：13