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异常响应对模型适应的影响 假设不同的底层响应过程
Jennifer Reimers1, Ronna C Turner1, Jorge N Tendeiro2
1Educational Statistics and Research Methods, University of Arkansas, Fayetteville, AR, USA.
Applied psychological measurement
|October 9, 2023
概括
异常响应显著影响了心理测量模型. 理想点模型 (如GGUM) 可能更适合累积数据,而不是累积模型 (如GPCM) 适合理想点数据.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 教育测量教育的测量
背景情况:
- 异常的响应 (例如,随机或有模式的答案) 可以扭曲尺度的心理测量特性.
- 在统计分析中,模型的不适合性可能来自数据的错误规范或异常响应模式.
研究的目的:
- 为了比较四种异常响应类型对模型匹配的影响.
- 在累积和理想点模型背景下评估这些效应.
- 使用分级部分信贷 (GPCM) 和通用分级展开 (GGUM) 模型来检查模型的合适性.
主要方法:
- 模拟数据具有四种异常响应类型的不同水平.
- 使用累积 (GPCM) 和理想点 (GGUM) 模型进行分析.
- 使用信息标准 (AIC,BIC) 和维度来评估模型的合适性.
主要成果:
- 异常数据严重影响模型适合累积和理想点数据.
- 长串响应最强烈地影响了这两种数据类型的维度.
- 随机响应对基于信息标准的模型匹配产生最负面影响.
结论:
- 与GPCM相比,理想点模型 (GGUM) 可能为累积数据提供更好的适应性.
- 累积模型 (GPCM) 可能不适合理想点数据.
- 了解异常反应对于准确的心理测量分析至关重要.
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