Department of Psychology, Ben-Gurion University of the Negev.
Department of Psychology, Fordham University.
Psychol Sci. 2020 Apr;31(4):437-448. doi: 10.1177/0956797620903717. Epub 2020 Mar 23.
Choosing between candidates for a position can be tricky, especially when the selection test is affected by irrelevant characteristics (e.g., reading speed). One can correct for this irrelevant attribute by penalizing individuals who have unjustifiably benefited from it. Statistical models do so by including the irrelevant attribute as a suppressor variable, but can people do the same without the help of a model? In three experiments (total = 357), participants had to choose between two candidates, one of whom had higher levels of an irrelevant attribute and thus enjoyed an unfair advantage. Participants showed a substantial preference for the candidate with high levels of the irrelevant attribute, thus choosing the less suitable candidate. This bias was attenuated when the irrelevant attribute was a situational factor, probably by making the correction process more intuitive. Understanding the intuitive judgment of suppressor variables can help candidates from underprivileged groups boost their chances to succeed.
在选择职位候选人时,这可能很棘手,尤其是当选拔测试受到不相关特征的影响(例如,阅读速度)时。人们可以通过惩罚那些不合理地从中受益的人来纠正这种不相关的属性。统计模型通过将不相关的属性作为抑制变量来实现这一点,但如果没有模型的帮助,人们可以做到吗?在三项实验中(总共有 357 人参与),参与者必须在两名候选人之间做出选择,其中一名候选人具有更高水平的不相关属性,因此享有不公平的优势。参与者明显更喜欢具有较高不相关属性的候选人,从而选择了不太合适的候选人。当不相关属性是情境因素时,这种偏见会减弱,这可能是因为使纠正过程更加直观。了解抑制变量的直观判断可以帮助弱势群体的候选人增加成功的机会。