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Faking Detection Improved: Adopting a Likert Item Response Process Tree Model
Organizational Research Methods ( IF 8.247 ) Pub Date : 2021-04-15 , DOI: 10.1177/10944281211002904
Tianjun Sun 1, 2 , Bo Zhang 3 , Mengyang Cao 1 , Fritz Drasgow 1, 4
Affiliation  

With the increasing popularity of noncognitive inventories in personnel selection, organizations typically wish to be able to tell when a job applicant purposefully manufactures a favorable impression. Past faking research has primarily focused on how to reduce faking via instrument design, warnings, and statistical corrections for faking. This article took a new approach by examining the effects of faking (experimentally manipulated and contextually driven) on response processes. We modified a recently introduced item response theory tree modeling procedure, the three-process model, to identify faking in two studies. Study 1 examined self-reported vocational interest assessment responses using an induced faking experimental design. Study 2 examined self-reported personality assessment responses when some people were in a high-stakes situation (i.e., selection). Across the two studies, individuals instructed or expected to fake were found to engage in more extreme responding. By identifying the underlying differences between fakers and honest respondents, the new approach improves our understanding of faking. Percentage cutoffs based on extreme responding produced a faker classification precision of 85% on average.



中文翻译:

伪造检测得到改进:采用李克特项目响应过程树模型

随着人员选择中非认知清单的日益普及,组织通常希望能够告诉求职者何时有意制造良好的印象。过去的伪造研究主要集中在如何通过仪器设计,警告和伪造的统计校正来减少伪造。本文通过研究伪造(实验操纵和上下文驱动)对响应过程的影响,采用了一种新方法。我们修改了最近引入的项目响应理论树建模程序(三过程模型),以在两项研究中识别伪造。研究1使用诱导的伪造实验设计检查了自我报告的职业兴趣评估反应。研究2研究了一些人处于高风险状态时自我报告的人格评估反应(i。e。,选择)。在这两项研究中,发现被指示或预期要伪造的人会做出更极端的反应。通过识别伪造者与诚实的被访者之间的根本差异,新方法提高了我们对伪造品的理解。基于极端响应的百分比截止值产生了平均85%的伪造分类精度。

更新日期:2021-04-15
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