贝叶斯适应拉索用于检测多维物件响应理论模型中的项目-特征关系和差异性项目功能
Na Shan1, Ping-Feng Xu1,2
1Northeast Normal University.
Psychometrika
|February 25, 2026
概括
本研究介绍了一种统一的框架,用于在多维物件响应理论 (MIRT) 模型中同时检测项目-特征关系和差异性项目功能 (DIF). 贝叶斯适应拉索方法有效地识别了这些关键的心理特征.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 教育测量教育的测量
背景情况:
- 在多维测试中,精确测量潜伏特征是必不可少的.
- 差异性项目运行 (DIF) 分析对于确保不同组的公平性和有效性至关重要.
- 现有的文献经常单独讨论项目-特征关系和DIF,限制了全面的理解.
研究的目的:
- 在多维项目响应理论 (MIRT) 模型中开发一个统一的框架,同时识别项目-特征关系和DIF.
- 将DIF效应集成到MIRT模型中,将问题视为潜在/观察变量及其相互作用的变量选择任务.
- 为分析复杂的心理测量数据提供强大的统计方法.
主要方法:
- 开发一个贝叶斯适应拉索程序,用于同时选择变量.
- 将DIF效应直接纳入MIRT框架.
- 使用模拟研究来评估拟议方法的性能.
主要成果:
- 提出的贝叶斯适应拉索方法有效估计参数,恢复项目-特征关系,并检测DIF效应.
- 模拟研究证明了该方法在识别项目-特征关联和DIF时的准确性和可靠性.
- 统一的框架成功地解决了这些心理特性的同时检测.
结论:
- 开发的统一框架为分析项目-特征关系和DIF同时在MIRT.提供了一个强大的方法.
- 贝叶斯适应拉索程序为心理测量学家和研究人员提供了一种高效和准确的工具.
- 这种方法提高了多维评估数据的有效性和可解释性,正如Eysenck人格问卷应用程序所证明的那样.
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