suppressorrelated

Suppressorrelated is a coined term used to describe a statistical situation in which a suppressor variable affects the observed correlation between two variables, often revealing a stronger or clearer association when included in a model. The phrase is not a standard label in textbooks, but it is used informally to spotlight how suppression can alter apparent relationships in data.

In regression analysis, a suppressor variable is one that increases the predictive validity of another variable by accounting for variance that is irrelevant to the criterion. When the suppressor is correlated with both the predictor and the outcome, the simple correlation between the predictor and outcome can be weak or misleading, while the coefficient for the predictor in a multiple regression may rise or change sign once the suppressor is included. This dynamic is a classic example of suppression, where the suppressor helps isolate the true association by removing extraneous variance.

Detection and interpretation involve comparing models with and without the suspected suppressor, and examining changes in coefficients, R-squared, and semi-partial correlations. Researchers check the correlations among variables and assess whether including the suppressor meaningfully improves model fit, while also considering potential multicollinearity and the theoretical rationale for including the variable.

Limitations and cautions include the lack of a standardized definition for suppressor correlated in some fields and the risk of overinterpreting changes in coefficients. Transparent reporting and replication are important to distinguish genuine suppression effects from statistical artifacts.

Related concepts include suppression effects, mediation, and confounding. When encountered, researchers should ground conclusions in theory and report all model changes clearly to avoid misinterpretation.