两种方法测量计划的缺失数据与有目的地选择的样本
Menglin Xu1, Jessica A R Logan2
1The Ohio State University, Columbus, OH, USA.
Educational and psychological measurement
|November 4, 2024
概括
计划失踪数据设计现在可以基于学生表现的有目的失踪,而不仅仅是完全随机 (MCAR) 失踪机制. 这种方法保持了统计能力,同时将评估集中在目标样本上.
科学领域:
- 教育研究方法论的教育研究方法.
- 统计建模 统计建模
- 心理测量 心理测量 心理测量
背景情况:
- 计划失踪数据设计在教育研究中越来越多地使用.
- 传统方法依赖于完全随机缺失 (MCAR) 数据.
- 现有的设计可能无法充分利用有针对性的评估策略.
研究的目的:
- 根据学生的表现,评估计划失踪设计的可行性.
- 引入和测试一个有目的的失踪方法.
- 将其性能与MCAR机制进行比较.
主要方法:
- 进行了一项蒙特卡洛模拟研究.
- 目的性缺失方法是在两种方法的测量设计中实施的.
- 在各种条件下评估了参数恢复.
主要成果:
- 目的性缺失方法在恢复参数估计方面表现出与MCAR方法相似的准确性.
- 在多种模拟条件下,性能一致.
- 这种方法允许对目标样本集中评估工作.
结论:
- 有目的的缺失是计划缺失数据设计中的MCAR的可行替代方案.
- 这种方法可以保持统计能力,同时优化评估资源.
- 它为应用教育研究提供了一种灵活的方法.
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