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基于响应模式的伪造检测方法的比较

Weiwen Nie¹, Ivan Hernandez², Louis Tay³

¹Hogan Assessments.

The Journal of applied psychology

|January 21, 2025

概括

机器学习方法最好用于检测人格测试中的假冒. 虽然所有方法都可能错误地分类诚实的高得分者,但检测伪造者的好处超过了这种风险.

科学领域:

心理评估心理评估心理评估
人格心理学人格心理学
心理测量心理测量心理测量

背景情况:

基于响应模式 (RPB) 的方法是检测人格测试假冒的关键.
对于尺度长度和样本大小等因素如何影响RPB方法性能的理解有限.
协差指数,特异性项目响应和机器学习是主要的RPB方法.

研究的目的:

系统地比较三种RPB伪造检测方法的性能.
调查实际因素对RPB方法有效性的影响.
为最佳实施伪造检测技术提供指导.

主要方法:

使用经验数据进行了三次重新抽样研究.
系统地改变条件,如尺度长度,培训样本大小和伪造者的比例.
比较的协同变量指数,特殊的项目响应和机器学习方法.

主要成果:

机器学习方法在大多数模拟条件下表现出卓越的性能.
所有RPB方法都显示了伪造概率和真实人格分数之间的中等到强烈的正相关性.
识别了高特征分数的诚实受访者的错误分类潜力.

结论:

机器学习是伪造检测最有效的RPB方法.
尽管存在潜在的错误分类,但识别伪造者的好处超过了风险.
为实施机器学习方法提供了实际指导和代码.

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