"如果申请人伪造他们的答案呢? ":在高风险评估中模拟伪造和响应风格,使用多维名义响应模型
Timo Seitz1, Maik Spengler2, Thorsten Meiser1
1University of Mannheim, Germany.
Educational and psychological measurement
|January 27, 2025
概括
本研究引入了一种扩展的多维名义响应模型 (MNRM),用于检测和纠正高风险评估中伪造的人格测试的情况. 该模型通过考虑响应偏差来提高测量特征的准确性.
科学领域:
- 心理测量 心理测量
- 心理测量 心理测量 心理测量
- 人格评估 个性评估
背景情况:
- 自我报告人格测试容易被伪造,特别是在高风险的情况下,如求职申请.
- 现有的模型可能无法完全捕捉响应偏差的项目特定性质.
- 准确的人格评估对于公平有效的决策至关重要.
研究的目的:
- 将多维名义响应模型 (MNRM) 扩展到明确模拟和解释人格评估中的伪造行为.
- 在模拟研究和现实世界高风险测试场景中评估扩展的MNRM的性能.
- 证明模型在提高人格特征得分的准确性和有效性方面的实用性.
主要方法:
- 开发了多维名义响应模型 (MNRM) 的扩展,其中包含了伪造维度.
- 进行模拟研究以评估参数恢复和模型选择性能.
- 将扩展的MNRM应用于高风险环境中的3046名求职者和低风险环境中的现有职位的数据.
- 使用了从试点研究中得出的特定项目评分权重来模拟模拟效应.
主要成果:
- 扩展的MNRM在模拟中显示了良好的参数恢复.
- 与没有伪造的模型相比,模拟伪造显著改善了模型适合性和差异有效性.
- 假装维度显示了与共变量有意义的关系.
- 该模型有效地捕捉了假冒,并在高风险和低风险条件下调整了特征得分.
结论:
- 扩展的MNRM提供了一种可靠的方法来检测和纠正人格评估中的伪造.
- 对伪造的记账增强了人格指标的心理测量属性和有效性.
- 这种方法对提高高风险评估环境的完整性有重大影响.
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