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在小样本大小的背景下,通过链接随机森林来等同NEAT:机器学习方法

Zhehan Jiang¹, Yuting Han¹, Lingling Xu¹

¹Peking University Health Science Center, Beijing, China.

Educational and psychological measurement

|September 4, 2023

概括

链接随机森林 (CRF) 归算方法提高了与测试 (NEAT) 设计不等价组的分数等同准确性,特别是在短时间测试和小样本大小的测试中. 这些机器学习技术提供了更可靠的估计在等同任务中缺少的数据.

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
机器学习机器学习

背景情况:

具有测试 (NEAT) 设计的非等效组通常用于教育测量的测试等效.
在NEAT设计中管理缺失的数据对于准确的分数等级至关重要.
传统的等式化方法可能会面临小样本大小和短测试长度的挑战.

研究的目的:

引入和评估基于机器学习的归算技术,链接随机森林 (CRF),用于在NEAT设计中等同任务.
提出七种基于CRF的归算等值方法,使用不同的数据增强策略.
在各种模拟条件下,将基于CRF的方法与传统等效方法的性能进行比较.

主要方法:

进行了一项模拟研究,以检查七种拟议的基于CRF的归算等效方法的等效性能.
研究的因素包括测试长度,样本大小,点物品比率,组等价性和点类型.
将CRF方法的性能与五种传统的等效方法 (塔克,莱文,等效,圆弧,拉什并行校准) 相比较.

主要成果:

基于CRF的方法,特别是整合塔克方法结果的方法 (例如IMP_total_Tucker,IMP_pair_Tucker),表现出优异的性能.
这些通过ML增强的方法为等同中缺少的数据提供了更可靠和可靠的估计.
与其他方法相比,使用基于CRF的方法更一致地实现了准确的等分得分,特别是在具有挑战性的条件下 (简短的测试,小样本).

结论:

机器学习技术,特别是CRF,在NEAT设计中为测试等级提供了显著的优势.
基于CRF的归算方法在解决缺失数据方面非常有效,从而提高了等同准确性.
建议的CRF方法,特别是与塔克方法相结合时,建议用于涉及简短测试和有限样本大小的实际应用.

关键词:

链接随机的森林.在等同化方面,它是相当的.基于机器学习的归算技术.小小的样本,小小的样本.

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