How can the consequences of self-enhancement (SE) be tested empirically? Traditional two-step approaches for investigating SE effects have been criticized for providing systematically biased results. Recently, we suggested condition-based regression analysis (CRA) as an approach that enables users to test SE effects while overcoming the shortcomings of previous methods. Krueger et al. (2017) reiterated the problems of previous two-step approaches and criticized the extent to which CRA could solve these problems. However, their critique was based on a misrepresentation of our approach: Whereas a key element of CRA is the requirement that the coefficients of a multiple regression model must meet two conditions, Krueger et al.'s argumentation referred to the test of only a single condition. As a consequence, their reasoning does not allow any conclusions to be drawn about the validity of our approach. In this paper, we clarify these misunderstandings and explain why CRA is a valid approach for investigating the consequences of SE.Keywords: self-view; self-enhancement; discrepancy model; algebraic difference; residual scores Self-enhancement (SE) is often defined as the degree to which the self-view (e.g., about one's ability) exceeds some kind of criterion (e.g., one's objectively measured ability). The (mal)adaptive consequences of SE are one of the most hotly debated topics in social-personality psychology. Are people better (or worse) adjusted the more they overestimate (or the less they underestimate) their positive attributes (e.g., Bonanno, Field, Kovacevic, & Kaltman, 2002;Church et al., 2006;Colvin, Block, & Funder, 1995;Dufner, Gebauer, Sedikides, & Denissen, 2018;Gramzow, Willard, & Mendes, 2008;Paulhus, 1998;Robins & Beer, 2001;Sedikides & Gregg, 2008;Taylor & Brown, 1988;Taylor, Lerner, Sherman, Sage, & McDowell, 2003)?Previous empirical studies addressing such questions have typically applied an analytical approach involving two steps. A discrepancy score (e.g., an algebraic difference or residual) between individuals' self-view and their value on some criterion measure was computed in a first step, which was then correlated with an outcome variable in a second step. Researchers have repeatedly emphasized that this two-step approach is not appropriate for testing SE effects because it is systematically biased toward mistaking main effects of the self-view for effects of SE (e.g., Asendorpf & Ostendorf, 1998;Edwards & Parry, 1993;Griffin, Murray, & Gonzalez, 1999;Humberg et al., 2018;Krueger & Wright, 2011;Ullrich, 2009;Zuckerman & Knee, 1996). Recently, Humberg et al. (2018) introduced condition-based regression analysis (CRA) as an approach that can be applied to overcome the problems of prior two-step approaches. Unlike previous approaches, CRA enables users to test SE effects without mistaking the main effects of the self-view for SE effects. In a recent article, Krueger, Heck, and Asendorpf (2017) reviewed discrepancy scores that have typically been used in the first step of the analysis of SE effe...