基本的局部独立模型的扩展到多个观察到的分类
Pasquale Anselmi1, Debora de Chiusole1, Egidio Robusto1
1Department of Philosophy, Sociology, Education and Applied Psychology (FISPPA), University of Padova, Padova, Italy.
The British journal of mathematical and statistical psychology
|September 22, 2025
概括
多重观察分类局部独立模型 (MOCLIM) 允许概率在不同的人群中变化,与基本局部独立模型 (BLIM) 不同. 这种新模型有效地识别了测试性能中的群体差异.
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
- 认知心理学 认知心理学
背景情况:
- 基本的局部独立模型 (BLIM) 假设知识状态的统一概率,疏忽的错误,和幸运的猜测跨越人口.
- 这种假设在现实场景中经常被违反,因此需要更灵活的模型.
研究的目的:
- 引入多重观察分类局部独立模型 (MOCLIM) 作为BLIM的扩展.
- 让知识状态的概率,不小心的错误和幸运的猜测在不同的人口中有所不同.
- 为参数估计和模型评估提供算法.
主要方法:
- 发展MOCLIM,结合熟练,不小心,和猜测类.
- 对MOCLIM参数的最大概率估计算法.
- 模拟研究以评估参数恢复和模型识别.
- 对乌类矩阵数据的实证应用.
主要成果:
- 模拟结果显示真实参数值的准确恢复.
- 使用合适度的模型比较有效地揭示了真正的模型.
- 经验应用表明MOCLIM能够区分预期和意想不到的群体差异.
结论:
- 当人口异质性存在时,MOCLIM提供了一个灵活的框架来建模项目响应数据.
- 提出的估计和模型选择方法是有效的.
- MOCLIM提供了关于认知评估中的群体差异的宝贵见解.
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