The true role that suppressor effects play in condition-based regression analysis: None. A reply to Fiedler (2021).

Condition-based regression analysis (CRA) is a statistical method for testing self-enhancement effects. That is, CRA indicates whether, in a set of empirical data, people with higher values on the directed discrepancy self-view S minus reality criterion R (i.e., S-R) tend to have higher values on some outcome variable (e.g., happiness). In a critical comment, Fiedler (2021) claims that CRA yields inaccurate conclusions in data with a suppressor effect. Here, we show that Fiedler's critique is unwarranted. All data that are simulated in his comment show a positive association between S-R and H, which is accurately detected by CRA. By construction, CRA indicates an association between S-R and H only when it is present in the data. In contrast to Fiedler's claim, it also yields valid conclusions when the outcome variable is related only to the self-view or when there is a suppressor effect. Our clarifications provide guidance for evaluating Fiedler's comment, clear up with the common heuristic that suppressor effects are always problematic, and assist readers in fully understanding CRA. (PsycInfo Database Record (c) 2022 APA, all rights reserved).