Commonality Analysis: Demonstration of an SPSS Solution for Regression Analysis

Multiple regression is a widely used technique to study complexinterrelationships among people, information, and technology. Inthe face of multicollinearity, researchers encounter challengeswhen interpreting multiple linear regression results. Althoughstandardized function and structure coefficients provide insightinto the latent variable ( ) produced, they fall short whenresearchers want to fully report regression effects. Regressioncommonality analysis provides a level of interpretation ofregression effects that cannot be revealed by only examiningfunction and structure coefficients. Importantly, commonalityanalysis provides a full accounting of regression effects whichidentifies the loci and effects of suppression and multicollinearity.Conducting regression commonality analysis without the aid ofsoftware is laborious and may be untenable, depending on thenumber of predictor variables. A software solution in SPSS ispresented for the multiple regression case and demonstrated foruse in evaluating predictor importance.