贝叶斯对多个评级者的非参数模型:一个一般的统计框架
Giuseppe Mignemi1, Ioanna Manolopoulou2
1https://ror.org/05crjpb27Bocconi Institute for Data Science and Analytics, Bocconi University, Milan, Italy.
Psychometrika
|August 11, 2025
概括
本研究引入了一种灵活的贝叶斯非参数模型来分析评级数据,通过考虑评级者变化和受试者异质性来提高准确性. 新的框架提高了对类内相关系数 (ICC) 的估计,以更好地评估评级质量.
科学领域:
- 统计 统计 统计 统计
- 贝叶斯的非参数学.
- 心理测量 心理测量 心理测量
背景情况:
- 评级程序在教育,临床环境和紧急服务中至关重要,但评级者的变化带来了挑战.
- 估计类内相关系数 (ICC) 对于评估评级质量至关重要,但它可能会受到子组,背景和主题异质性的影响.
- 现有的参数多层模型做出了强有力的分布假设,限制了处理异质性的灵活性.
研究的目的:
- 为分析评级数据提出一个更灵活的贝叶斯非参数 (BNP) 模型.
- 为了自然地考虑评级者和受试者之间的异质性,提高估计准确性.
- 为连续性和粗性评级数据开发一个一般的BNP异构的框架.
主要方法:
- 在贝叶斯的非参数框架内使用等级的离散非参数先验.
- 开发一个一般的BNP异种模型来分析评级数据.
- 采用先的破棒表示来导出ICC索引.
主要成果:
- 拟议的模型适用于评级者和受试者之间的集群,自然处理异质性.
- 提高了对类内相关系数 (ICC) 估计的准确性.
- 独立识别受试者与评级者之间潜在的相似之处.
结论:
- 国民银行框架为评级分析提供了参数模型的灵活替代方案.
- 该方法增强了评级质量的评估,可以应用于精准教育中的个性化干预.
- 该研究提供了理论结果,计算策略,并通过模拟和现实世界的数据展示了应用.
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