贝叶斯模型的准确性适合在选择多维物件响应理论模型中的指数
1Loyola University Chicago, IL, USA.
Educational and psychological measurement
|June 20, 2024
概括
对物件响应理论 (IRT) 数据的贝叶斯模型比较可以有利于复杂的嵌套模型. 使用适当的指数,如帕雷托平滑重要性抽样,可以避免偏差,确保准确的维度评估.
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:
- 项目响应理论 (IRT) 模型对于分析评级级数据和确定维度至关重要.
- 基于预测性能的模型比较可能会有偏见,有利于嵌套维度IRT模型而不是非嵌套的IRT模型.
- 这种偏差的程度,特别是在贝叶斯估计和特定比较指数方面,仍然不清楚.
研究的目的:
- 在对嵌套和非嵌套维度IRT模型进行比较时,调查贝叶斯预测性能指数中的偏差.
- 评估四个贝叶斯指数在不同维度结构下区分这些模型类型的准确性.
- 澄清嵌套维度模型可能受到不公平优惠的条件.
主要方法:
- 为了评估贝叶斯预测性绩效指数,进行了一项模拟研究.
- 研究了四个指数:偏差信息标准 (DIC),帕雷托平滑重要性抽样 (PSIS-LOO),瓦塔纳贝-阿卡伊克信息标准 (WAIC) 和日志预测边际概率 (LPML).
- 该研究模拟了代表特定维度结构的数据,以测试模型差异化准确性.
主要成果:
- 偏差信息标准 (DIC) 显示出极端偏差,偏好嵌套维度模型,即使是不正确的.
- 帕雷托平滑重要性抽样 (PSIS-LOO) 在测试的指数中显示出最少的偏差.
- 瓦塔纳贝-阿卡伊克信息标准 (WAIC) 和日志预测边际概率 (LPML) 也表现相对较好,偏差比DIC.
结论:
- 如果使用适当的贝叶斯预测指数,当数据代表特定的维度结构时,嵌套维度IRT模型本质上不受青.
- 选择模型比较指数对于在IRT中准确的维度评估至关重要.
- 在这些情况下,建议采用帕雷托平滑重要性抽样 (PSIS-LOO) 进行可靠的模型比较.
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