检测评级规模与部分信贷模型和通用部分信贷模型的故障
1The University of Alabama, Tuscaloosa, USA.
Educational and psychological measurement
|September 4, 2023
概括
使用部分信用模型 (PCM) 和概括部分信用模型 (GPCM) 的评级规模分析技术,有效识别评级规模问题. 这两种模型都帮助研究人员了解尺度的运作和检测潜在的问题.
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
- 量化心理学 量化心理学
背景情况:
- 顺序评级表在研究中被广泛使用.
- 有效使用需要尺度在心理测量上发挥作用.
- 有限的研究探讨了尺度"功能故障"的敏感性.
研究的目的:
- 系统地探索评级级别分析技术的敏感性.
- 调查两个流行的多种物体反应理论 (IRT) 模型:PCM和GPCM.
- 评估模型对评级尺度故障的敏感性.
主要方法:
- 使用了真实数据分析.
- 进行了模拟研究.
- 应用部分信用模型 (PCM) 和通用部分信用模型 (GPCM).
主要成果:
- 无论是PCM还是GPCM,都提供了对评级规模值排序和精度的宝贵见解.
- 这些模型有助于研究人员了解尺度运行.
- 在特定条件下观察到模型对故障敏感性的差异.
结论:
- 评级尺度分析技术,特别是PCM和GPCM,对于评估尺度心理特征是有用的.
- 这些方法有助于确定需要改进规模的领域.
- 研究人员可以利用IRT模型来检测和解决评级规模问题.
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