当评级者概括时:用混合物检查光环效应的来源 拉什方面模型
1Hong Kong Examinations and Assessment Authority, 68 Gillies Avenue South, Kowloon City, Kowloon, Hong Kong. kyjin@hkeaa.edu.hk.
Behavior research methods
|April 21, 2025
概括
一种常见的评级偏差,即光环效应,现在可以使用一种新的混合Rasch方面模型 (MRFM-H) 来检测. 该模型区分了一般印象和标准歧视问题,提高了评估准确性.
科学领域:
- 心理测量 心理测量 心理测量
- 认知心理学 认知心理学
- 统计建模 统计建模
背景情况:
- 光环效应是评估中的普遍认知偏见,导致评级错误.
- 现有的测量模型缺乏识别源和检测光环效应的能力.
- 了解光环效应背后的心理机制对于准确的评估至关重要.
研究的目的:
- 提出一个关于光环效应的Rasch方面模型 (MRFM-H) 的一般混合.
- 根据不同的心理机制,开发两个特定的模型 (MRFM-H ((GI) 和MRFM-H ((ID)):一般印象和不充分的标准歧视.
- 使用贝叶斯推理,模拟和现实世界的数据来实现和验证这些模型.
主要方法:
- 为光环效应 (MRFM-H) 开发了一种一般混合拉什方面模型.
- 衍生出两个特定的模型:一般印象的MRFM-H(GI) 和标准歧视的MRFM-H(ID).
- 采用贝叶斯推理来实现模型,并通过模拟研究和真实数据分析进行验证.
主要成果:
- 对于诱导光环的人来说,模型分类的准确性取决于评级者和标准的数量.
- 足够的分类准确度 (90%) 要求每位评级者-人组合至少需要25个评级.
- 忽视光环效应会偏向标准估计,但对个人和评价者的估计的影响很小.
- 贝叶斯匹配统计 (WAIC,WBIC) 有效地确定了正确的数据生成模型.
结论:
- 拟议的MRFM-H模型提供了一个强大的框架,用于检测和理解评估中的光环效应.
- 这些模型可以区分一般印象和标准歧视偏见.
- 这些模型在各种评估环境中具有实际实用性,以提高评级的准确性和有效性.
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