检测DIF与多个单一维的配对偏好模型:Lord的千方形和IPR-NCDIF方法
Lavanya S Kumar1, Naidan Tu2, Sean Joo3
1Department of Psychology, University of South Florida, Tampa, FL, USA.
Applied psychological measurement
|July 4, 2025
概括
差异物品功能 (DIF) 检测方法适用于多维强制选择 (MFC) 措施. 已建立的方法,如Lord's chi-square和项目参数复制 (IPR),在MFC测试中有效检测DIF,为非认知评估提供可靠的见解.
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
- 非认知评估 非认知评估
背景情况:
- 多维强制选择 (MFC) 测量越来越多地用于非认知评估.
- 在这些MFC模型中检测差异项目功能 (DIF) 的研究有限.
研究的目的:
- 扩展和评估两种已建立的DIF检测方法,用于MFC措施.
- 调查Lord's chi-square和物品参数复制 (IPR) 方法在多维单维对制偏好 (MUPP) 模型中的性能.
主要方法:
- 使用蒙特卡洛模拟来检查I型错误率和统计能力.
- 操纵的关键变量包括样本大小,影响,DIF来源 (歧视,门,位置) 和DIF大小.
主要成果:
- 无论是Lord's chi-square方法还是IPR方法,都表现出一致的统计能力,并在各种条件下有效控制了I型错误率.
- 当DIF来源于声明歧视时,Lord's chi-square显示出优异的表现,而IPR则在声明值DIF时表现更好.
- 这两种方法在DIF源自语句位置时,性能相对较好,功率更好.
结论:
- 已建立的DIF检测方法适合在MFC测试中与MUPP模型一起使用.
- 在Lord's chi-square和IPR之间做出选择可能取决于DIF的具体来源.
- 提供了在MFC措施中对DIF检测的实际应用和限制的建议.
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