Understanding the Results of Multiple Linear Regression: Beyond Standardized Regression Coefficients
| dc.citation.journalTitle | Organizational Research Methods | en_US |
| dc.contributor.author | Nimon, Kim F. | en_US |
| dc.contributor.author | Oswald, Frederick L. | en_US |
| dc.date.accessioned | 2013-08-02T16:06:10Z | en_US |
| dc.date.available | 2013-08-02T16:06:10Z | en_US |
| dc.date.issued | 2013 | en_US |
| 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. | en_US |
| dc.embargo.terms | none | en_US |
| dc.identifier.citation | Nimon, Kim F. and Oswald, Frederick L.. "Understanding the Results of Multiple Linear Regression: Beyond Standardized Regression Coefficients." <i>Organizational Research Methods,</i> (2013) Sage: http://dx.doi.org/10.1177/1094428113493929. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.1177/1094428113493929 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/71722 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Sage | en_US |
| 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. | en_US |
| dc.subject.keyword | multiple regression | en_US |
| dc.subject.keyword | quantitative research | en_US |
| dc.subject.keyword | exploratory | en_US |
| dc.subject.keyword | research design | en_US |
| dc.title | Understanding the Results of Multiple Linear Regression: Beyond Standardized Regression Coefficients | en_US |
| dc.type | Journal article | en_US |
| dc.type.dcmi | Text | en_US |
| dc.type.publication | publisher version | en_US |