Abstract
Suppression effect is one of the most elusive and difficult to understand dynamics in multiple regression analysis. Suppressor variable(s) and their dynamics in multiple regression analyses are important in reporting accurate research outcomes. However, quite a number of researchers are unfamiliar with the possible advantages and importance of these variables. Suppressor variables tend to appear useless as separate predictors, but have the potential to change the predictive ability of other variables and completely influence research outcomes. This research describes the role suppressor variables play in a multiple regression analysis and provides practical examples that further explain how suppressor effects can alter research outcomes. Finally, we employed mathematical set notation to demonstrate the concepts of suppressor effects.
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