Correlated predictors in regression models are a fact of life in applied social science research. The extent to which they are correlated will influence the estimates and statistics associated with the other variables they are modeled along with. These effects, for example, may include enhanced regression coefficients for the other variables--a situation that may suggest the presence of a suppressor variable. This paper examines the history, definitions, and design implications and interpretations when variables are tested as suppressors versus when variables are found that act as suppressors. Longitudinal course evaluation data from a single study illustrate three different approaches to studying potential suppressors and the different results and interpretations they lead to.