|dc.contributor.author||Nimon, Kim F.
Oswald, Frederick L.
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.
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.
Understanding the Results of Multiple Linear Regression: Beyond Standardized Regression Coefficients
Organizational Research Methods