DIF项目余额和效果大小与树结合的影响
Nana Amma Berko Asamoah1, Ronna C Turner1, Wen-Juo Lo1
1University of Arkansas, Fayetteville, AR, USA.
Educational and psychological measurement
|September 29, 2025
概括
拉什树方法有效地检测了差异物品功能 (DIF) 与平衡的组和大效果大小. 它的准确性随着不平衡的组和增加的污染而下降,特别是在小样本中.
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
- 统计建模 统计建模
背景情况:
- 评估中的公平性至关重要,需要强有力的方法来检测差异性项目功能 (DIF).
- 拉什树方法为DIF检测提供了一个灵活的,基于模型的方法,不需要预先指定组.
- 在现实条件下对其表现的研究有限,特别是在不平衡的DIF的情况下.
研究的目的:
- 为了评估Rash树方法的DIF检测性能.
- 为了调查DIF平衡的影响,大小,样本大小,测试长度和污染.
- 为了比较DIF检测的统计显著性与效果大小启发式.
主要方法:
- 模拟数据被用来评估拉什树方法.
- 操纵的关键因素包括DIF余额,大小,样本大小,测试长度和污染.
- 教育测试服务效果大小启发式被纳入作为绩效标准.
主要成果:
- 拉什树方法在平衡的DIF条件下显示了更高的真实DIF检测率,并且大幅度.
- 精度降低了不平衡的DIF和更高的污染水平.
- 使用效果大小减少了可以忽略不计的DIF的检测,而较小的样本产生了最低的检测率.
结论:
- 拉什树方法对DIF平衡和大小敏感.
- DIF集团的不平衡和污染显著影响检测准确度.
- 在实际评估环境中提供了优化DIF检测的建议,强调小样本的谨慎性.
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