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通过快速猜测识别方法产生的错误分类及其在各种条件下的适用性
Santeri Holopainen1, Jari Metsämuuronen1, Mikko-Jussi Laakso1
1University of Turku, Finland.
Educational and psychological measurement
|February 26, 2026
概括
响应时间值方法 (RTTM) 在评估中有效识别快速猜测行为 (RG). 混合物lognormal (MLN) 方法被证明是最稳定的,为RG检测提供了精确的值.
科学领域:
- 教育测量教育的测量
- 心理测量 心理测量 心理测量
- 数据科学数据科学数据科学
背景情况:
- 响应时间值方法 (RTTM) 对于在低风险评估中检测快速猜测行为 (RG) 至关重要.
- 挑战包括错误分类由于重叠的响应时间和对最佳方法的分歧.
研究的目的:
- 在不同的条件下评估五个RTTM的性能.
- 确定在模拟研究中检测RG的最稳健的方法.
主要方法:
- 从单元 (没有RG) 和双元混合模型 (有RG) 中模拟的物品响应和响应时间.
- 多样化的分布,物品和人参数.
- 评估了五个RTTM:混合物lognormal (MLN),累积比例 (CUMP) 和混合物响应时间量子 (MRTQ),以及其他.
主要成果:
- MLN在识别RG方面表现出了卓越的稳定性,估计了最接近最佳错误分类点的精确值.
- CUMP不那么强大,但当成功时是准确的;当RG缺席时,这是设定值的唯一方法.
- 大多数方法都是保守的,除了MRTQ.
结论:
- 建议使用MLN作为RG识别的可靠方法,特别是当双模式是微妙的时.
- 提出了一个实用的六步RG识别过程,将混合模型 (MLN或MRTQ) 与CUMP集成在一起.
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