Suppressor Variable(s) Importance within a Regression Model: An Example of Salary Compression from Career Services

Of the variables chosen as predictors of salary, only degree had a negative effect on the dependent variable; that is, as level of education increased, salary tended to decrease for career services directors at both institutional types. This negative effect signified a high probability of the presence of salary compression. Further, correlation matrices verified that degree was acting as a suppressor variable within the regression models by denoting that it had a very low, close to zero correlation with salary and a correlation with another independent variable (e.g., age). The negative suppressor designation of degree was applicable by observing its positive correlation with salary and its negative beta weights within the regression equations.