潜伏D-评分建模:对物品和人参数的估计
Dimiter M Dimitrov1,2, Dimitar V Atanasov3
1National Center for Assessment, Saudi Arabia.
Educational and psychological measurement
|November 6, 2023
概括
本研究引入了测试分析中D-评分方法的潜在框架. 它证明了对人和物品参数的准确估计,增强了经典测试理论的应用.
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
- 统计建模 统计建模
背景情况:
- 经典测试理论 (CTT) 提供了测试得分,等级和项目分析的基本方法.
- D-评分方法是最近的经典方法,为这些心理测量程序提供了替代框架.
- 现有的CTT方法可能缺乏物品响应理论 (IRT) 中发现的复杂建模功能.
研究的目的:
- 为D-评分方法提供一个潜在的,类似物品响应理论的框架.
- 在这种潜伏的D-评分框架内,开发和验证用于估计人与物体参数的方法.
- 根据潜伏的D-评分模型来导出关键心理测量函数 (信息,标准误差) 的分析表达式.
主要方法:
- 开发D评分方法的潜在框架,集成项目响应函数 (IRF) 模型.
- 使用D-尺度 (0至1) 上的边际最大概率 (MML) 估计来估计人与物品参数.
- 对项目信息函数 (IIF),测试信息函数 (TIF) 和估计标准误差 (SEE) 的分析公式的推导.
主要成果:
- 拟议的潜伏框架成功地将IRF建模与D评分方法集成在一起.
- 边际最大概率估计在模拟研究中显示出物品和人参数的良好恢复.
- 成功地获得了IIF,TIF和SEE的分析表达式,提供了有价值的心理测量见解.
结论:
- 潜伏的框架提供了一个强大的和理论上健全的D-评分方法的扩展.
- MML估计方法提供了准确的参数恢复,验证了拟议的模型.
- 这项工作增强了D-评分方法在测试开发和分析中的应用,将古典和现代心理测量方法相结合.
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