在认知诊断中的一般非参数分类方法的一致性理论
Chengyu Cui1, Yanlong Liu2, Gongjun Xu1
1Department of Statistics, University of Michigan, Ann Arbor, MI, USA.
Psychometrika
|April 7, 2025
概括
认知诊断模型 (CDM) 的非参数方法现在更加可靠. 这项研究在实际条件下重新建立了它们的一致性理论,改善了它们在教育和心理学中的使用.
科学领域:
- 心理测量 心理测量 心理测量
- 教育测量教育的测量
- 数据科学数据科学数据科学
背景情况:
- 认知诊断模型 (CDM) 在教育,心理学和社会科学中得到广泛应用.
- 参数概率估计是安装CDM的一个常见方法.
- 非参数化方法在易于实施和稳定性方面具有优势,特别是在小样本大小的情况下.
研究的目的:
- 为一般非参数分类方法重新建立一致性理论.
- 为了放松现有的非参数估计方法经常要求的限制性条件.
- 为非参数式CDM提供更实用的理论基础.
主要方法:
- 非参数分类方法的理论分析.
- 制定一个通用的一致性框架.
- 检查非参数估计的条件.
主要成果:
- 重新建立了一般非参数分类方法的一致性理论.
- 确定了非参数估计的较弱和更实际的条件.
- 这些发现支持非参数CDM的更广泛的适用性.
结论:
- 修订后的理论框架提高了CDM非参数方法的可靠性.
- 这些发现有助于在各个领域实践应用强大的非参数方法.
- 这项工作有助于在认知诊断中的统计方法的进步.
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