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Updated: Sep 9, 2025

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A Two-interval Forced-choice Task for Multisensory Comparisons
Published on: November 9, 2018
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多维强制选择问卷的等级-2PL模型的项目和测试特征曲线
Jianbin Fu1, Xuan Tan1, Patrick C Kyllonen1
1Educational Testing Service.
概括
一种新的方法使用Rank-2PL模型为多维强制选择问卷创建一维预期项和测试特征曲线. 这些特征曲线有助于在物品响应理论分析中识别不合适的特征得分.
科学领域:
- 心理测量
- 项目响应理论 (IRT)
- 统计模型
背景情况:
- 多维强制选择问卷在心理和教育评估中被广泛使用.
- 分析这些复杂的数据结构需要先进的项目响应理论 (IRT) 模型.
- 现有的方法可能无法完全捕捉强制选择格式的特征测量细微差别.
研究的目的:
- 提出一种用于生成一维预期项目特征曲线 (ICC) 和测试特征曲线 (TCC) 的新方法.
- 将这一过程应用于使用Rank-2PL IRT模型的多维强制选择问卷.
- 证明ICC和TCC在识别特征得分不匹配的项目和测试水平中的有用性.
主要方法:
- 开发基于Rank-2PL IRT模型的过程,用于分析两个或三个语句的强制选择项目.
- 在多维框架内生成单个特征的单维预期ICC和TCC.
- 应用和可视化ICC和TCC图表使用现实世界对和三重形态的数据.
主要成果:
- 拟议的过程成功地为每个特征生成一维的ICC和TCC.
- 从真实数据中可视化ICC和TCC图表,证明了它们在识别不合适特征得分方面的有效性.
- 通过将负面陈述转换为正面陈述以提高可解释性,建议对TCC图片进行扩展.
结论:
- 开发的方法为在IRT框架内分析多维强制选择数据提供了有价值的工具.
- 生成的ICC和TCC对于诊断物品和测试水平不合适是有效的,提高了测量准确度.
- 对TCC图片的拟议修改为更精细的数据诊断提供了潜力.
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