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dc.contributor.authorNimon, Kim F.
Oswald, Frederick L.
dc.date.accessioned 2013-08-02T16:06:10Z
dc.date.available 2013-08-02T16:06:10Z
dc.date.issued 2013
dc.identifier.citation Nimon, Kim F. and Oswald, Frederick L.. "Understanding the Results of Multiple Linear Regression: Beyond Standardized Regression Coefficients." Organizational Research Methods, (2013) Sage: http://dx.doi.org/10.1177/1094428113493929.
dc.identifier.urihttps://hdl.handle.net/1911/71722
dc.description.abstract Multiple linear regression (MLR) remains a mainstay analysis in organizational research, yet intercorrelations between predictors (multicollinearity) undermine the interpretation of MLR weights in terms of predictor contributions to the criterion. Alternative indices include validity coefficients, structure coefficients, product measures, relative weights, all-possible-subsets regression, dominance weights, and commonality coefficients. This article reviews these indices, and uniquely, it offers freely available software that (a) computes and compares all of these indices with one another, (b) computes associated bootstrapped confidence intervals, and (c) does so for any number of predictors so long as the correlation matrix is positive definite. Other available software is limited in all of these respects. We invite researchers to use this software to increase their insights when applying MLR to a data set. Avenues for future research and application are discussed.
dc.language.iso eng
dc.publisher Sage
dc.rights Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
dc.title Understanding the Results of Multiple Linear Regression: Beyond Standardized Regression Coefficients
dc.type Journal article
dc.citation.journalTitle Organizational Research Methods
dc.subject.keywordmultiple regression
quantitative research
exploratory
research design
dc.embargo.terms none
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1177/1094428113493929
dc.type.publication publisher version


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