在多维IRT模型中估计一个简单的结构,使用结构规范化
Ryosuke Shimmura1, Joe Suzuki1
1Graduate School of Engineer Science, Osaka University, Toyonaka 560-0043, Japan.
Entropy (Basel, Switzerland)
|January 22, 2024
概括
我们开发了一种用于多维物品响应理论模型的新方法,该方法简化了潜在特征估计. 这种方法提高了可解释性和项目聚类,优于现有的L1惩罚方法,特别是在数据有限的情况下.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 教育测量的教育测量.
背景情况:
- 多维物品响应理论 (MIRT) 模型是复杂的.
- 现有的方法可能难以解释,并且可能在小样本大小的情况下扎.
- 需要更简单,更易于解释的MIRT估计技术.
研究的目的:
- 在MIRT中开发一种用于估计简单结构矩阵的新方法.
- 为了提高MIRT中潜在特征的解释性.
- 为了实现基于潜伏特征的测试项目的有效集群.
主要方法:
- 使用基于产品的弹性净 (prenet) 罚款,根据因素分析进行了调整.
- 使用随机EM算法,近似梯度方法和坐标下降的组合进行优化.
- 开发一种方法,使每个测试项对应于单个潜在特征.
主要成果:
- 拟议的方法通过将单个潜伏特征分配给测试项目,从而产生易于解释的结果.
- 根据相应的潜伏特征实现了对测试项的有效聚类.
- 数字实验与现有的L1惩罚方法相比显示出更高的性能,特别是在少量测试对象的情况下.
结论:
- 预网罚款为在MIRT中估计简单结构矩阵提供了一种有效的方法.
- 该方法提供了增强的解释性和项目聚类能力.
- 这种技术在样本规模有限的场景中特别有利,为现有的L1惩罚方法提供了强大的替代方案.
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