Abstract
In the two-predictor situation it is shown that traditional and negative suppressors increase the predictive value of a standard predictor beyond that suggested by the predictor's zero order validity. This effect of suppression is used to provide a revised definition of suppression and completely accounts for traditional and negative suppression. The revised definition, in conjunction with a two factor model, is shown to lead to a previously undetected type of suppression (reciprocal suppression) which occurs when predictors with positive zero order validities are negatively correlated with one another. In terms of the definition and parameters of the model, limits are determined in which the types of suppression can occur. Furthermore, it is shown how suppressors can be identified in multiple regression equations and a procedure is given for interpreting whether the variables are contributing directly (by predicting relevant variance in the criterion) or indirectly (by removing irrelevant variance in another predictor) or both.
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