使用自然语言处理来提高预测能力并减少人员选择决策中的子组差异.
Emily D Campion1, Michael A Campion2, James Johnson3
1Department of Management and Entrepreneurship, University of Iowa.
The Journal of applied psychology
|October 19, 2023
概括
申请人叙述的自然语言处理 (NLP) 改善了工作选择预测,并减少了种族差异. 这种方法揭示了传统测试和数字数据遗漏的与工作相关的见解.
科学领域:
- 组织心理学 组织心理学
- 自然语言处理自然语言处理.
- 人力资源 人力资源 人力资源
背景情况:
- 传统的选择方法依赖于心理能力测试和数值数据.
- 这些方法可能无法捕捉所有相关的与工作相关的构造.
- 现有的方法可以显示种族子组的差异.
研究的目的:
- 展示NLP使用叙事应用数据增强预测的能力.
- 为了减少选择分数中的种族子组差异.
- 通过NLP识别未被捕捉的与工作相关的构造.
主要方法:
- 将NLP应用于美国空军军官培训学校申请人的叙事应用数据 (N=1,828).
- 预测高级官员董事会做出的选择决定.
- 将NLP得分与心理能力测试和数值数据进行比较.
主要成果:
- NLP得分预测了与人类评分器 (0.60相关性) 相比的董事会得分.
- NLP增加了超越传统指标的增量预测有效性.
- 在选择分数中,NLP减少了种族分组之间的差异.
结论:
- 对叙事数据的NLP分析显示了改善选择有效性的前景.
- 这种方法有助于解决有效性-不利影响困境.
- NLP提供了一种方法来揭示更深层次的申请人见解.
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