The Use of Structural Equation Models in Interpreting Regression Equations Including Suppressor and Enhancer Variables

Abstract

It is shown that the usual interpretation of "sup pressor" effects in a multiple regression equation assumes that the correlations among variables have been generated by a particular structural (causal) model, namely, Conger's (1974) two-factor model. A distinction is drawn between the technical definition of "suppression," which is more fittingly labelled enhancement, and suppression as the appropriate interpretation of a regression equation exhibiting enhancement when that equation has been gen erated by the two-factor model. It is demonstrated that a number of models can generate enhancement but cannot sensibly be interpreted in terms of the measuring, removing, or suppressing of irrelevant or invalid variance. How a regression equation is interpreted thus depends critically on the structural model deemed appropriate.