对性能和工艺数据的一般化线性潜变量模型的快速估计,使用顺序,连续和计数观察到的变量
Maoxin Zhang1, Björn Andersson1,2, Shaobo Jin3
1Center for Educational Measurement, University of Oslo, Oslo, Norway.
The British journal of mathematical and statistical psychology
|February 12, 2024
概括
本研究引入了一种有效的方法,用于分析心理和教育测量的混合数据类型. 第二阶拉普拉斯近似提高了顺序,连续和计数数据的收和参数精度.
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
- 统计建模 统计建模
背景情况:
- 心理和教育评估产生各种数据类型 (例如,响应时间,计数).
- 建模这些混合数据类型,包括复杂的依赖关系,需要专门的统计方法.
- 一般化的线性潜变量模型 (GLLVMs) 可以处理混合数据,但在估计过程中面临计算挑战.
研究的目的:
- 开发一种高效的估计方法,在GLLVM中同时建模顺序,连续和计数数据.
- 扩展现有的拉普拉斯近似方法,用于混合数据类型的联合建模.
- 通过模拟和经验示例来评估拟议方法的性能.
主要方法:
- 使用一级或二级拉普拉斯近似来联合建模序列,连续和计数数据的估计方法的推导.
- 应用该方法来分析来自计算机评估的混合数据.
- 进行模拟研究以评估估计效率,收率和参数恢复.
主要成果:
- 与第一阶近似相比,第二阶拉普拉斯近似显示出更高的收率和参数精度.
- 拟议的方法为复杂的GLLVM提供了快速而准确的参数估计.
- 纳入刺激水平依赖性的模型显著改善了经验数据的合适性.
结论:
- 开发的拉普拉斯近似方法为心理学和教育研究中混合数据类型的联合建模提供了有效的解决方案.
- 建议使用二次拉普拉斯近似,因为其精度和计算效率的平衡.
- 考虑刺激中的可变依赖性可以提高模型性能和数据解释.
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