It is shown that the usual interpretation of "suppressor"
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 generated
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.
McFatter, Robert M. (1979). The use of structural equation models in interpreting regression equations including suppressor and enhancer variables. Applied Psychological Measurement, 3, 123-135. doi:10.1177/014662167900300113
McFatter, Robert M..
The use of structural equation models in interpreting regression equations including suppressor and enhancer variables.
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