Understanding the Results of Multiple Linear Regression: Beyond Standardized Regression Coefficients

摘要

Multiple linear regression (MLR) remains a mainstay analysis in organizational research, yet intercorrelationsbetween predictors (multicollinearity) undermine the interpretation of MLR weights interms of predictor contributions to the criterion. Alternative indices include validity coefficients,structure coefficients, product measures, relative weights, all-possible-subsets regression, dominanceweights, and commonality coefficients. This article reviews these indices, and uniquely, itoffers 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 predictorsso long as the correlation matrix is positive definite. Other available software is limited in allof these respects. We invite researchers to use this software to increase their insights when applyingMLR to a data set. Avenues for future research and application are discussed.