The Bias of Individuals (in Crowds): Why Implicit Bias Is Probably a Noisily Measured Individual-Level Construct
The Bias of Individuals (in Crowds): Why Implicit Bias Is Probably a Noisily Measured Individual-Level Construct
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DOI：
10.1177/1745691620931492
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发表时间：
2020-08-03
影响因子：
12.6
通讯作者：
Evers, Ellen R. K.
中科院分区：
文献类型：
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作者：
Connor, Paul;Evers, Ellen R. K.
佩恩、沃莱蒂奇和伦德伯格的群体偏见模型提出，通过将内隐偏见概念化为情境的特征，而不是个体的特征，可以解决许多经验性难题。在本文中，我们反对这种模式，并提出，鉴于现有的证据，内隐偏见是最好的理解为一个个体水平的结构测量与实质性的错误。首先，使用真实的和模拟数据，我们展示了如何佩恩和同事们提出的难题可以解释为测量误差的结果，并通过聚合减少。其次，我们讨论了为什么作者反对这种解释的反驳是没有说服力的。最后，我们测试了一个假设，来自偏见的人群模型的影响，个别有针对性的“隐式偏见为基础的驱逐计划”在大学内，并显示该模型缺乏实证支持。最后，我们考虑的影响，概念化的内隐偏见作为一个嘈杂的测量个人层面的结构正在进行的内隐偏见的研究。所有数据和代码都可以在https://osf.io/tj8u6/上找到。
Payne, Vuletich, and Lundberg's bias-of-crowds model proposes that a number of empirical puzzles can be resolved by conceptualizing implicit bias as a feature of situations rather than a feature of individuals. In the present article we argue against this model and propose that, given the existing evidence, implicit bias is best understood as an individual-level construct measured with substantial error. First, using real and simulated data, we show how each of Payne and colleagues' proposed puzzles can be explained as being the result of measurement error and its reduction via aggregation. Second, we discuss why the authors' counterarguments against this explanation have been unconvincing. Finally, we test a hypothesis derived from the bias-of-crowds model about the effect of an individually targeted "implicit-bias-based expulsion program" within universities and show the model to lack empirical support. We conclude by considering the implications of conceptualizing implicit bias as a noisily measured individual-level construct for ongoing implicit-bias research. All data and code are available at https://osf.io/tj8u6/.