When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality analysis to more completely interpret their regression effects and thereby inform theory. Using an empirical example from published literature, readers will see how regression commonality analysis can uncover important theoretical relationships that might be left undetected by only examining beta weights.
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