A suppressor variable is a variable that increases the predictive validity of another variable when included in a
regression equation
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one o ...
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[Krus, D. J., & Wilkinson, S. M. (1986). Demonstration of properties of a suppressor variable. ''Behavior Research Methods, Instruments, & Computers'', 18(1), 21-24.]
Suppression can occur when a single causal variable is related to an outcome variable through two separate mediator variables, and when one of those mediated effects is positive and one is negative. In such a case, each mediator variable suppresses or conceals the effect that is carried through the other mediator variable. For example, higher intelligence scores (a causal variable, A) may cause an increase in error detection (a mediator variable, B) which in turn may cause a decrease in errors made at work on an assembly line (an outcome variable, X); at the same time, intelligence could also cause an increase in boredom (C), which in turn may cause an increase in errors (X). Thus, in one causal path intelligence decreases errors, and in the other it increases them. When neither mediator is included in the analysis, intelligence appears to have no effect or a weak effect on errors. However, when boredom is controlled intelligence will appear to decrease errors, and when error detection is controlled intelligence will appear to increase errors. If intelligence could be increased while only boredom was held constant, errors would decrease; if intelligence could be increased while holding only error detection constant, errors would increase.
See also
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Confounding variable
In statistics, a confounder (also confounding variable, confounding factor, extraneous determinant or lurking variable) is a variable that influences both the dependent variable and independent variable, causing a spurious association. Con ...
References
Regression analysis
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