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强制选择测试的多维IRT:文献综述

Lei Nie¹, Peiyi Xu², Di Hu³

¹School of Public Administration, East China Normal University, China.

|March 7, 2024

概括

多维强制选择 (MFC) 测试在减少响应偏差方面提供了优势,但却产生了 ipsative 数据. 新的多维物件响应理论 (MIRT) 模型有助于从MFC测试中收集规范性数据,从而提高其实用性.

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
心理评估心理评估

背景情况:

多维强制选择 (MFC) 测试被广泛用于非认知性评估,以减轻利克特尺度内在的响应偏差.
然而,MFC测试产生了ipsative数据,这给个人比较和规范数据开发带来了挑战.
多维物件响应理论 (MIRT) 的最新进展为分析强制选择数据提供了有希望的解决方案.

研究的目的:

为多维强制选择项目响应理论 (MFC-IRT) 引入一个新的建模框架,整合响应格式,测量模型和决策理论.
在MFC-IRT模型中比较参数估计技术.
检查MFC-IRT在参数不变性测试,计算机自适应测试 (CAT) 和有效性调查中的实证应用.

主要方法:

开发用于MFC-IRT分析的概念框架.
在框架内选择和应用四个示例IRT模型.
对MFC-IRT模型的参数估计技术进行比较分析.
在参数不变性,CAT和有效性研究中对MFC-IRT进行实证检查.

主要成果:

该研究为理解和应用MFC-IRT模型提供了一个结构化的框架.
它提供了与MFC数据相关的参数估计方法的全面比较.
经验分析表明MFC-IRT在推进心理测量应用方面的潜力.

结论:

拟议的MFC-IRT框架通过允许规范性数据收集和强有力的心理测量分析来提高强制选择测试的实用性.
未来的研究应该专注于改进MFC-IRT模型,参数不变性测试,强制选择CAT和有效性研究.
这些进展对于提高非认知评估的准确性和适用性至关重要.

关键词:

构造性数据构造性数据一个叫做MUPP MUPP的.多维强制选择测试多维强制选择测试参数估计的参数估计.这就是TIRTT.

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