Department of Psychology, Westfälische Wilhelms-University Münster.
Department of Psychology, University of Leipzig.
J Pers Soc Psychol. 2018 Feb;114(2):303-322. doi: 10.1037/pspp0000134. Epub 2017 Mar 23.
Despite a large body of literature and ongoing refinements of analytical techniques, research on the consequences of self-enhancement (SE) is still vague about how to define SE effects, and empirical results are inconsistent. In this paper, we point out that part of this confusion is due to a lack of conceptual and methodological differentiation between effects of individual differences in how much people enhance themselves (SE) and in how positively they view themselves (positivity of self-view; PSV). We show that methods commonly used to analyze SE effects are biased because they cannot differentiate between the effects of PSV and the effects of SE. We provide a new condition-based regression analysis (CRA) that unequivocally identifies effects of SE by testing intuitive and mathematically derived conditions on the coefficients in a bivariate linear regression. Using data from 3 studies on intellectual SE (total N = 566), we then illustrate that the CRA provides novel results as compared with traditional methods. Results suggest that many previously identified SE effects are in fact effects of PSV alone. The new CRA approach thus provides a clear and unbiased understanding of the consequences of SE. It can be applied to all conceptualizations of SE and, more generally, to every context in which the effects of the discrepancy between 2 variables on a third variable are examined. (PsycINFO Database Record
尽管有大量的文献和不断改进的分析技术，但关于自我提升（SE）后果的研究仍然不清楚如何定义 SE 效应，并且实证结果也不一致。在本文中，我们指出，这种混淆的部分原因是缺乏对个体差异对自我提升程度（SE）和自我看法积极程度（自我看法的积极性；PSV）的影响之间的概念和方法上的区分。我们表明，常用于分析 SE 效应的方法存在偏差，因为它们不能区分 PSV 效应和 SE 效应。我们提供了一种新的基于条件的回归分析（CRA），通过对二元线性回归中系数的直观和数学推导条件进行测试，明确地确定了 SE 的影响。使用来自 3 项关于智力 SE 的研究的数据（总 N=566），我们随后说明了 CRA 与传统方法相比提供了新的结果。结果表明，许多先前确定的 SE 效应实际上只是 PSV 的效应。因此，新的 CRA 方法为 SE 的后果提供了清晰和无偏的理解。它可以应用于 SE 的所有概念化，并且更普遍地应用于检验两个变量之间的差异对第三个变量的影响的所有情况下。
