在2PL IRT模型中的个人特定参数异质性
Alexandra Lane Perez1, Eric Loken1
1Educational Psychology, University of Connecticut.
Multivariate behavioral research
|June 23, 2023
概括
这项研究探讨了项目响应理论 (IRT) 中的人特测量模型,发现项目参数的个体差异可能导致低估的歧视和因素得分的可靠性降低. 这些发现突出了测试应用中异质性的潜在来源.
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:
- 标准因子模型可以掩盖因子负载的个体差异.
- 个体特定的测量模型为了解个体反应提供了更细致的方法.
研究的目的:
- 在物品响应理论 (IRT) 中调查个人特定的测量模型.
- 评估个人特异性歧视和难度参数对模型匹配和参数估计的影响.
主要方法:
- 引入了按个人级别的项目随机变化,以创建个人特定的歧视和难度参数.
- 应用了2参数后勤 (2PL) IRT模型的标准拟合算法.
- 使用常见的诊断工具来评估个人和物品级别的不适合.
主要成果:
- 使用标准诊断工具检测到人或物品级别不适合的温和证据.
- 项目困难通常被很好地估计,但项目歧视被明显低估.
- 因数得分显示出低于预期的可靠性,这是由于潜在的异质性.
结论:
- 个人特定的IRT模型代表了诸如多层或混合模型等更复杂结构的限制案例.
- 测试应用程序中未确认的异质性来源可能会影响参数估计和得分可靠性.
- 该研究强调了考虑测量模型中的个体差异的重要性.
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