估计使用贝叶斯方法的多维通用分级展开模型与共变量.
Naidan Tu1, Bo Zhang2, Lawrence Angrave3
1Department of Psychology, University of South Florida, Tampa, FL 33620, USA.
Journal of Intelligence
|August 25, 2023
概括
新的bmggum R包准确地估计了多维通用分级展开模型 (MGGUM). 多维分析和共变量改善了对非认知构造的参数估计.
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
- 组织心理学 组织心理学
背景情况:
- 非认知结构经常使用总和分数进行评估,假设存在主导反应过程.
- 展开的响应过程,更好地代表了通用分级展开模型 (GGUM),越来越多地被认为是对非认知项目的评估.
- 现有的GGUM实现仅限于单维案例,对多维非认知构造构成了挑战.
研究的目的:
- 评估bmggum R包中的贝叶斯算法的准确性,用于估计多维通用分级展开模型 (MGGUM).
- 评估多维估计和共变量纳入对MGGUM参数准确性的影响.
- 在MGGUM框架内调查贝叶斯模型选择指数 (WAIC和LOO) 的表现.
主要方法:
- 为了检查bmggum R包的性能,进行了两项模拟研究.
- 使用bmggum包来估计MGGUM参数,并将共变量纳入其中.
- 评估了贝叶斯模型选择指数,广泛适用的信息标准 (WAIC) 和Leave-One-Out (LOO) 交叉验证.
- 经验数据被分析以证明bmggum,并将其与GGUM2004,GGUM和mirt软件进行比较.
主要成果:
- bmggum包显示了MGGUM参数的准确估计.
- 多维估计和包含相关的共变量大大提高了参数估计的准确性.
- 在MGGUM的背景下,WAIC和LOO都被认为是有效的模型选择.
- 与现有的GGUM软件相比,bmggum显示了可比或更好的性能.
结论:
- bmggum R包为使用MGGUM估计多维非认知构造提供了一个可靠的工具.
- 结合多维性和共变量可以提高非认知测量的参数估计的精度.
- 在应用MGGUM时,WAIC和LOO是合适的模型选择指标.
- 对于分析复杂的非认知数据的研究人员来说,bmggum是一个宝贵的进步.
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