相关实验视频
Updated: Jan 18, 2026

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A Two-interval Forced-choice Task for Multisensory Comparisons
Published on: November 9, 2018
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强制选择问卷的Rank-2PL模型的信息功能
Jianbin Fu1, Xuan Tan1, Patrick C Kyllonen1
1Educational Testing Service.
概括
本研究介绍了Rank二参数后勤模型 (Rank-2PLM) 用于分析强制选择问卷. 它详细介绍了项目和测试信息功能,为改善问卷设计和特征得分估计提供了实际见解.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
背景情况:
- 强迫选择问卷在心理和教育评估中被广泛使用.
- 准确测量潜伏特征需要强大的项目和测试信息功能.
- 现有的模型可能无法完全捕捉对和三重物品格式的复杂性.
研究的目的:
- 为强制选择项目应用的Rank二参数后勤模型 (Rank-2PLM) 呈现项目和测试信息功能.
- 为双胞胎引入多个单一维的双胞胎偏好 (MUPP-2PLM) 模型和三胞胎的三胞胎-2PLM模型.
- 提供诊断工具,用于在强制选择环境中评估项目和测试性能.
主要方法:
- 在Rank-2PLM框架内描述费舍尔的信息和定向信息.
- 对最大概率 (ML),最大后期 (MAP) 和预期后期 (EAP) 属性得分估计的测试信息的区分.
- 建议并绘制不同级别的预期项目/测试信息索引.
主要成果:
- 该研究表明了项目/测试信息,区分参数,标准错误和可靠性之间的关系.
- 预期的项目/测试信息索引被提出并绘制为诊断目的.
- 由于大量的响应模式,对EAP分数的预期测试信息的计算可能具有挑战性.
结论:
- 排名-2PLM为理解强制选择问卷中的项目和测试信息提供了一个框架.
- 建议的信息索引和图表为开发和分析问卷提供了实际指导.
- 这些发现有助于更准确的特征得分估计和提高评估可靠性.
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