Enhanced Versus Simply Positive: A New Condition-Based Regression Analysis to Disentangle Effects of Self-Enhancement From Effects of Positivity of Self-View
Enhanced Versus Simply Positive: A New Condition-Based Regression Analysis to Disentangle Effects of Self-Enhancement From Effects of Positivity of Self-View
复制标题
DOI：
10.1037/pspp0000134
复制
发表时间：
2018-02-01
影响因子：
7.6
通讯作者：
Back, Mitja D.
中科院分区：
文献类型：
--
作者：
Humberg, Sarah;Dufner, Michael;Back, Mitja D.
尽管有大量的文献和不断完善的分析技术，自我增强（SE）的后果的研究仍然是模糊的如何定义SE效果，实证结果是不一致的。在本文中，我们指出，这种混乱的一部分是由于缺乏概念和方法上的差异的影响，在多大程度上的人提高自己（SE），并在他们如何积极地看待自己（积极的自我观; PSV）。我们发现，通常用于分析SE效应的方法是有偏见的，因为他们不能区分PSV的影响和SE的影响。我们提供了一个新的基于条件的回归分析（CRA），明确识别SE的影响，通过测试直观和数学推导的条件下的系数在一个二元线性回归。使用数据从3个研究的智力SE（总N = 566），我们然后说明，CRA提供了新的结果相比，传统的方法。结果表明，许多先前确定的SE效应实际上是PSV单独的效应。因此，新的CRA方法提供了对SE后果的清晰和公正的理解。它可以应用于所有的概念化的SE，更一般地说，每一个上下文中，两个变量之间的差异对第三个变量的影响进行检查。
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.