节的项目响应理论建模与cauchit链接:重新审视四参数物流模型的逻辑原理
Hyejin Shim1,2, Wes Bonifay3,4, Wolfgang Wiedermann1,2
1University of Missouri, 5 C Hill Hall, Columbia, MO, 65211, USA.
Behavior research methods
|May 19, 2025
概括
新的Cauchit项目响应理论 (IRT) 模型为四参数物流模型 (4PLM) 提供了一个更简单的替代方案. 它在较小的样本大小下表现良好,使IRT研究更容易获得.
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
- 统计 统计 统计 统计
背景情况:
- 四参数物流模型 (4PLM) 在物品响应理论 (IRT) 中被广泛使用,但其在估计非对称的复杂性带来了挑战.
- 在4PLM中估计上下非对称效应需要大样本大小和特定项目的特征,限制其在某些研究场景中的应用.
研究的目的:
- 引入Cauchit IRT模型作为4PLM的节且有效的替代方案.
- 评估与4PLM相比,Cauchit模型的性能,特别是在参数估计和样本大小要求方面.
主要方法:
- 考契IRT模型的开发,使用基于考契分布的链接函数.
- 进行全面的模拟研究,以在各种条件下将考奇特模型与4PLM进行比较.
- 对现实世界的数据进行分析,以验证模拟研究的发现.
主要成果:
- 考奇特模型以其对称错误分布和明显的尾巴为特征,有效地用单个项目参数捕捉了4PLM的关键特征.
- 与需要大样本 (N>5000) 的4PLM相比,cauchit模型在显著较小的样本大小 (N=100) 中表现出强大的性能.
- 考奇特模型的对称误差分布和明显的尾巴简化了参数估计,特别是在非对称效应.
结论:
- 考奇特IRT模型为4PLM提供了一个引人注目的,节的替代方案,简化了复杂的估计问题.
- 考奇特模型在小样本大小的适应性和有效性提高了它在各种IRT研究环境中的实用性.
- 该模型为项目响应理论研究提供了一种简化解决方案,特别是在处理有限数据时.
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