缺失数据对参数估计的影响:计算机自适应测试中的三个例子
Xiaowen Liu1,2,3, Eric Loken4
1Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China.
Educational and psychological measurement
|January 9, 2025
概括
计算机自适应测试 (CAT) 数据可以被分析以重新估计参数,即使缺少信息. 确保使用所有数据至关重要,以避免在适应性测试和教学工具中出现偏见.
科学领域:
- 教育测量和心理测量学
- 计算机化的适应性测试 (CAT)
- 统计建模 统计建模
背景情况:
- 计算机自适应测试 (CAT) 量身定制项目难度以适应考生个体能力水平.
- 与传统的测试格式相比,术后的CAT数据往往显示出大量缺失的信息.
- 在CAT中,有限的响应范围可以减少项目和总分数的相关性,从而带来分析挑战.
研究的目的:
- 调查从术后CAT数据重新估计人体和物品参数的可行性.
- 检查不同测试设计对CAT数据分析的影响.
- 识别可能导致CAT内参数估计偏差的条件.
主要方法:
- 来自三个不同的测试设计数据的模拟:常见项目,随机选择的项目和CAT.
- 对术后数据的分析,以重新估计人体和物品的参数.
- 调查多维CAT,以评估是否需要包括所有测试阶段反应.
主要成果:
- 从术后的CAT数据中重新估计人体和物体参数被发现是可行的.
- 在多维CAT中,包括测试阶段的所有响应对于满足缺失数据假设 (MAR) 至关重要.
- 某些CAT设计可能导致"反转",对项目歧视产生负面影响,并导致显著的参数估计错误.
结论:
- 研究人员可以从术后CAT数据中可靠地重新估计参数,只要满足缺失数据假设.
- 利用测试阶段的所有可用数据至关重要,特别是在多维CAT中,以防止偏差.
- 这些发现适用于CAT研究和自适应式教学工具,强调需要仔细处理数据.
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