半参数物品响应理论与O'SullivanSplines用于物品响应和响应时间
1National Taiwan Normal University, Taiwan.
Applied psychological measurement
|February 5, 2025
概括
一个新的半参数物品响应理论 (IRT) 模型使用O.
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:
- 项目响应理论 (IRT) 模型传统上使用对响应时间 (RT) 的参数方法.
- 参数模型可能由于行为变化而无法捕捉潜在特征和RT之间的复杂,非线性关系.
- 准确估计潜伏特征和项目参数对于教育测试至关重要.
研究的目的:
- 引入一种新型的半参数IRT模型,采用O'Sullivan线条.
- 为了灵活地建模平均RT形状,并探索潜伏特征和RT之间的非线性关系.
- 提高参数估计的准确性,减少教育评估中的测量错误.
主要方法:
- 使用O'Sullivan线条开发一个半参数IRT模型.
- 模拟研究将拟议模型与传统的参数模型进行比较.
- 从国际学生评估计划 (PISA) 数学测试中分析现实世界的数据.
主要成果:
- 与参数模型相比,拟议的半参数模型显著提高了参数估计的准确性.
- 当非线性RT模式存在时,参数模型表现出偏差和增加的测量误差.
- PISA数据集分析证实了非线性存在,并证明了新方法的优越模型适应性.
结论:
- 新的半参数IRT模型为分析响应时间数据提供了更灵活,更准确的方法.
- 这种方法提高了测试可靠性,并通过捕获非线性关系来减少测量误差.
- 该模型显示了作为教育测量的宝贵心理测量工具的潜力.
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