在基于变量自编码器的项目响应理论中处理缺失的数据
Karel Veldkamp1, Raoul Grasman1, Dylan Molenaar1
1Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.
The British journal of mathematical and statistical psychology
|October 26, 2024
概括
变量自编码器 (VAE) 为高维物件响应理论 (IRT) 模型提供高效的估计. 新的VAE方法有效地处理缺失的数据,在模拟和现实世界的测试中表现优于传统方法.
科学领域:
- 心理测量 心理测量 心理测量
- 机器学习 机器学习
- 统计建模 统计建模
背景情况:
- 高维物件响应理论 (IRT) 模型对于教育和心理评估至关重要.
- 传统的IRT估计方法在大量数据集和缺失数据方面存在困难.
- 变量自编码器 (VAE) 对高效估计有希望,但缺乏固有的缺失数据处理.
研究的目的:
- 适应并提出基于VAE的方法,用于估计缺少数据的高维IRT模型.
- 将这些VAE方法的性能与传统的边际最大概率 (MML) 估计进行对比.
- 评估增加缺失数据水平对VAE方法性能的影响.
主要方法:
- 将三种现有的VAE归算技术适应IRT环境.
- 开发一种基于VAE的新方法来处理IRT中缺少的数据.
- 模拟研究具有不同的维度 (3D,10D) 和缺失的数据比例.
- 将VAE模型应用于真实世界代数测试数据集.
主要成果:
- 基于VAE的方法为IRT估计提供了MML的时间效率高的替代方案.
- VAE方法的性能可与MML相提并论,尤其是在仔细调整参数的情况下.
- 对于VAE方法,当缺失数据比例很大时,需要增加重要性加权的样本.
- 在代数测试数据集上证明了实用的实用性.
结论:
- 基于VAE的方法为估计高维IRT模型提供了可行和高效的解决方案,特别是在缺少数据的情况下.
- 选择VAE方法和样本数量对于具有广泛缺失的最佳性能至关重要.
- 对用于心理测量建模的VAE进行进一步研究是有必要的.
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