一般化豪斯曼测试用于检测二参数IRT模型隐性变量分布中的非正常性
Lucia Guastadisegni1, Silvia Cagnone1, Irini Moustaki2
1University of Bologna, Bologna, Italy.
The British journal of mathematical and statistical psychology
|December 26, 2024
概括
一个新的概括的豪斯曼测试有效地检测二进制数据模型的潜在变量分布中的非正常性. 这种统计方法比现有的潜在特征分析测试提供了更好的性能.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 项目响应理论.
背景情况:
- 一维隐性特征模型被广泛用于二进制数据分析.
- 评估潜变量的正常性假设对于模型有效性至关重要.
- 现有的测试潜变量分布适合性的方法有局限性.
研究的目的:
- 介绍和评估一种新的一般化豪斯曼测试,用于检测隐性变量分布中的非正常性.
- 为了比较一般化豪斯曼测试的性能与潜在变量分布适合性和整体适合性好处的现有统计数据.
- 将拟议的测试和信息标准应用于现实世界的数据集.
主要方法:
- 使用对对最大概率 (PML) 和半非参数最大概率 (ML) 估计器.
- 进行模拟研究,以评估一般化豪斯曼测试的功率和性能.
- 将拟议的测试与现有的适合性统计数据进行比较,并采用模型选择的信息标准.
主要成果:
- 在大多数模拟条件下,与其他测试统计数据相比,一般化豪斯曼测试显示出更高的性能.
- 该测试有效地识别了潜变量分布中的正常偏差.
- 在特定条件下,信息标准产生了一些矛盾的结果,需要进一步解释.
结论:
- 概括的豪斯曼测试是一个有希望和有效的工具,用于评估隐性变量分布在单维隐性特征模型中的正常性.
- 建议进行进一步的研究,以澄清与拟议测试相关的信息标准的应用.
- 一般化豪斯曼测试的实际实用性通过其应用于三个实证数据集来证明.
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