Self-enhancement: Conceptualization and Assessment

Abstract

Self-enhancement bias is conventionally construed as an unwarranted social comparison in social psychology and a misperception of social reality in personality psychology. Researchers in both fields rely heavily on discrepancy scores to represent self-enhancement and fail to distinguish between a general tendency or bias to self-enhance and a self-enhancement error, or false perception of own excellence. We critically review prominent discrepancy measures and then propose a decision-theoretic alternative that [a] mitigates confounds between self-positivity and self-superiority, [b] separates error from bias, and [c] discourages reliance on measures that reify self-enhancement as a stable personality trait. To evaluate our hypotheses, we re-analyze data collected in our laboratory and perform a series of simulation studies. We share these materials with interested researchers.

Testing Significance Testing

“…After years of confirmatory findings, the researcher can predict that most respondents will regard themselves as above average when rating themselves and the average person on dimensions of personal importance (Krueger, Heck, & Asendorpf, 2017). The prior probability of the null hypothesis of no self-enhancement is low and the meta-probability of a low p value is high.…”

Why Condition-Based Regression Analysis (CRA) is Indeed a Valid Test of Self-Enhancement Effects: A Response to Krueger et al. ()

“…Consequently, the argumentation that the authors provided cannot justify their conclusion about CRA. In this rebuttal, we clarify CRA, explain why the critique in Krueger et al (2017) is unjustified, and explain why CRA does, in fact, solve the problems of prior two-step approaches that have been applied for investigating SE effects.…”

“…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.…”

“…Unlike previous approaches, CRA enables users to test SE effects without mistaking the main effects of the self-view for SE effects. 1 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 effects (see Humberg et al, 2018;Kurt & Paulhus, 2008;Kwan, John, Kenny, et al, 2004;Kwan, John, Robins, & Kuang, 2008;Moore & Healy, 2008;Paulhus, 2008;Robins & Paulhus, 2001, for similar overviews). The authors then revisited the critique of the two-step approach and concluded that it is not suitable for testing SE effects.…”

“…After their recap on the statistical problems of twostep approaches, Krueger et al (2017) noted that CRA has been suggested 2 as a way to solve these problems, but then they misrepresented the CRA approach and drew a conclusion that would be very unfortunate for the field: They concluded that CRA should be avoided because it suffers from the very same problems as discrepancy score correlations. Specifically, the authors first correctly introduced CRA by stating that two conditions must be tested to detect an SE effect but they then referred to only one of the conditions when evaluating the utility of CRA.…”