使用多标签分类神经网络来检测与小样本大小的交叉DIF
概括
一个新的神经网络InterDIFNet有效地检测在小样本中的交叉差异物件功能 (DIF). 它在多个集团中识别复杂的DIF方面优于现有方法,提高了评估公平性.
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
- 人工智能的人工智能
背景情况:
- 传统的差异性项目运行 (DIF) 方法通常需要大样本大小.
- 边际DIF方法可能无法捕捉交叉身份的复杂效应.
研究的目的:
- 介绍InterDIFNet,一个用于检测交叉DIF的新型神经网络.
- 解决现有方法在小样本大小和复杂群体相互作用中的局限性.
主要方法:
- 开发了InterDIFNet,一个多标签分类神经网络.
- 采用了针对功率和1型错误控制的优化值程序.
- 进行了蒙特卡罗模拟,将InterDIFNet与截断拉索惩罚 (TLP) 和其他交叉DIF方法进行比较.
主要成果:
- 当使用TLP特征进行训练时,InterDIFNet的统计能力比TLP更高.
- 保持了可比的1型错误控制,特别是在三个或更多的交叉组.
- 经验应用证实了InterDIFNet在实际评估数据中的实际实用性.
结论:
- InterDIFNet提供了一个可扩展的,数据驱动的解决方案,用于识别交叉DIF.
- 该方法对于具有较小样本规模的教育和心理评估特别有效.
- 通过考虑交叉的身份,提供了对测试公平性的更细微的方法.
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