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隐藏的波桑计数模型用于从技术增强评估中获得的行动计数数据.
Gregory Arbet1, Hyeon-Ah Kang1
1University of Texas at Austin, Austin, Texas, USA.
The British journal of mathematical and statistical psychology
|February 4, 2026
概括
本研究引入了一种灵活的统计模型,用于分析来自各种项目类型的计算机评估的行动计数. 精细的模型准确地捕捉了项目类型的影响,改进了用于创新的评估格式的数据分析.
科学领域:
- 心理测量和教育测量方法
- 统计建模 统计建模
- 计算统计学 计算统计学
背景情况:
- 计算机化评估越来越多地使用创新的项目格式,如拖放和基于场景的问题.
- 现有的统计模型可能无法充分捕捉这些不同的项目类型对用户行动数量的系统影响.
- 需要灵活的建模方法来考虑项目特征及其对响应数据的影响.
研究的目的:
- 呈现统计模型的精细化方案,在计算机化评估中明确考虑项目特征和项目类型影响.
- 将该方案整合到现有的计数模型中,特别是拉什和康威-马克斯韦-波森模型.
- 验证建议的推断程序的准确性和实际相关性.
主要方法:
- 开发了一个改进方案,使用多方面的参数化来表征共同和独特的项目特征.
- 在Rasch和Conway-Maxwell-Poisson计数模型中实施该方案.
- 利用Stan进行模型推断,并使用模拟数据进行数值实验以验证.
主要成果:
- 拟议的推断方案在模拟中证明了基础模型参数的充分恢复.
- 经验应用证实了当数据中存在明显的项目类型效应时,改进的实际相关性.
- 该研究验证了改进模型的估计准确性.
结论:
- 拟议的模型改进有效地捕捉了在计算机化评估数据中的项目类型影响.
- 这种方法为分析来自创新的项目格式的计数数据提供了实用解决方案.
- 结果提供了关于应用Poisson模型用于心理测量中计数数据分析的见解.
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