基于分数的测试与固定效应的人参数在项目响应理论:检测模型错误规范包括差异性项目功能
Rudolf Debelak1,2, Charles C Driver1
1University of Zurich, Zurich, Switzerland.
Applied psychological measurement
|February 13, 2026
概括
一个新的基于分数的测试有效地检测物品响应理论 (IRT) 模型中的模型错误规范,即使使用大型数据集. 这种方法简化了差异性项目功能 (DIF) 选,为复杂的评估提供了更快,更强大的替代方案.
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:
- 项目响应理论 (IRT) 模型被广泛用于教育评估.
- 检测模型错误规范,例如差异项目功能 (DIF),对于有效的测试解释至关重要.
- 传统的DIF检测方法可能是计算密集型,需要复杂的模型设置,特别是对于大规模数据集.
研究的目的:
- 引入一个基于分数的快速统计测试,用于检测IRT模型中的模型错误规范.
- 为在人参数被视为固定的效应时提供DIF查的有效方法,适用于非常大的数据集.
- 开发一个计算效率高,更简单的替代现有的DIF选方法.
主要方法:
- 开发了一个基于分数的近似测试,用于IRT模型的错误规范.
- 该测试与受约束的联合最大概率 (CJML) 和联合最大后期 (JMAP) 估计器兼容.
- 整合了一个基于spline的残留步骤来控制I型错误率,当共变量与能力相关时.
主要成果:
- 模拟显示了名义错误率和用于检测DIF的高功率,具有足够的响应数据 (每个受试者15-20个响应).
- 该方法被证明是可扩展和实用的,在一个大型数据集 (1602个项目,57,684名学生) 的应用中,确定了13%的项目与性别相关的DIF.
- 拟议的测试显示了与传统的DIF检测方法的高相关性.
结论:
- 开发的基于分数的测试为大规模评估提供了强大的,计算轻量的诊断.
- 当经典的随机效应方法是不可行的,或者当能力组结构和DIF效应形状是未知的或复杂的时,它特别有价值.
- 这种方法提高了心理测量分析中DIF查的效率和简单性.
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