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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.)

The current article notes that the standard application of relative importance analyses is not appropriate when examining the relative importance of interactive or other higher order effects (e.g., quadratic, cubic). Although there is a growing demand for strategies that could be used to decompose the predicted variance in regression models containing such effects, there has been no formal, systematic discussion of whether it is appropriate to use relative importance statistics in such decompositions, and if it is appropriate, how to go about doing so. The purpose of this article is to address this gap in the literature by describing three different yet related strategies for decomposing variance in higher-order multiple regression models—hierarchical F tests (a between-sets test), constrained relative importance analysis (a within-sets test), and residualized relative importance analysis (a between- and within-sets test). Using a previously published data set, we illustrate the different types of inferences these three strategies permit researchers to draw. We conclude with recommendations for researchers seeking to decompose the predicted variance in regression models testing higher order effects. [ABSTRACT FROM AUTHOR]

Aims This study sought to develop and begin validation of an indirect screener for identification of drug use during pregnancy, without reliance on direct disclosure. Design Women were recruited from their hospital rooms after giving birth. Participation involved (i) completing a computerized assessment battery containing three types of items: direct (asking directly about drug use), semi-indirect (asking only about drug use prior to pregnancy) and indirect (with no mention of drug use), and (ii) providing urine and hair samples. An optimal subset of indirect items was developed and cross-validated based on ability to predict urine/hair test results. Setting Obstetric unit of a university-affiliated hospital in Detroit. Participants Four hundred low-income, African American, post-partum women (300 in the developmental sample and 100 in the cross-validation sample); all available women were recruited without consideration of substance abuse risk or other characteristics. Measurements Women first completed the series of direct and indirect items using a Tablet PC; they were then asked for separate consent to obtain urine and hair samples that were tested for evidence of illicit drug use. Findings In the cross-validation sample, the brief screener consisting of six indirect items predicted toxicology results more accurately than direct questions about drug use (area under the ROC curve = 0.74, P < 0.001). Traditional direct screening questions were highly specific, but identified only a small minority of women who used drugs during the last trimester of pregnancy. Conclusions Indirect screening may increase the accuracy of mothers' self-reports of prenatal drug use. [ABSTRACT FROM AUTHOR]

The search for a meaningful index of the relative importance of predictors in multi- pie regression has been going on for years. This type of index is often desired when the explanatory aspects of regression analysis are of interest. The authors define relative importance as the proportionate contribution each predictor makes to R2, considering both the unique contribution of each predictor by itself and its incremental contribution when combined with the other predictors. The purposes of this article are to introduce the concept of relative importance to an audience of researchers in organizational behavior and industrial/organizational psychology and to update previous reviews of relative importance indices. To this end, the authors briefly review the history of research on predictor importance in multiple regression and evaluate alternative measures of relative importance. Dominance analysis and relative weights appear to be the most successful measures of relative importance currently available. The authors conclude by discussing how importance indices can be used in organizational research. [ABSTRACT FROM AUTHOR]