授权专家判断:在高维和数据稀缺评估中制定标准的数据驱动决策框架.
Tianpeng Zheng1,2, Zhehan Jiang2,3, Zhichen Guo2
1School of Public Health, Peking University, Beijing, China.
Educational and psychological measurement
|January 6, 2026
概括
在具有挑战性的小样本,高维数据中的标准设置需要先进的方法. 信息理论和集群方法提供解决方案,最佳选择取决于考生熟练程度的分布.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 教育测量的教育测量.
背景情况:
- 标准设置在小样本,高维数据中面临挑战,在这些数据中,项目数量超过了受试者数量.
- 传统的参数模型,如项目响应理论,在这些情况下可能是不稳定的,或者由于不可靠的参数估计而失败.
研究的目的:
- 研究和评估信息理论和无监督集群方法,以在具有挑战性的数据条件下制定标准.
- 建立一个基于证据的框架来选择适当的数据驱动的标准制定方法.
主要方法:
- 采用蒙特卡洛模拟来系统评估15种数据驱动方法.
- 模拟因素包括样本大小,物品对受试者的比例,混合物比例,物品质量和能力分离.
- 使用相对错误,分类准确度,灵敏度,特异性和Youden指数来评估性能.
主要成果:
- 没有任何一种方法被证明是普遍优越的;最佳方法的选择取决于试验混合物的比例.
- 量子信息比率 (QIR) 方法在一个占主导地位的非竞争群体的场景中显示出高特异性.
- 像卡林斯基-哈拉巴斯指数 (CHI) 和二次误差和 (SSE) 这样的聚类方法在选择性背景下与平衡组有效.
- 贝叶斯核密度估计 (BKDE) 在各种条件下显示出强大而平衡的性能.
结论:
- 该研究为从业者提供了一个决策框架,以便在传统方法不可行时选择可辩护的,数据驱动的标准制定方法.
- 这些发现强调了在选择标准制定方法时考虑受试者特征和数据结构的重要性.
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