方法对比
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| 稳健的微分项目功能 (Robust DIF)× | 差异项目功能 (DIF)× | |
|---|---|---|
| 领域 | 心理测量学 | 心理测量学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 1990s–2000s | 1970s–1993 |
| 提出者≠ | Building on DIF work by Cleary & Hilton (1968) and Mantel-Haenszel by Holland & Thayer (1988); robust extensions developed through 1990s–2000s | William H. Angoff and colleagues (ETS); systematized by Holland & Wainer |
| 类型≠ | Item bias / fairness analysis | Item-level bias detection |
| 开创性文献≠ | Magis, D., Beland, S., Tuerlinckx, F., & De Boeck, P. (2011). A general framework and an R package for the detection of dichotomous differential item functioning. Behavior Research Methods, 43(3), 847–862. DOI ↗ | Holland, P. W. & Wainer, H. (Eds.) (1993). Differential Item Functioning. Lawrence Erlbaum Associates. ISBN: 978-0805809589 |
| 别名 | Robust DIF, outlier-resistant DIF detection, robust item bias analysis, DIF with robust estimation | DIF, item bias analysis, measurement non-equivalence, item-level measurement bias |
| 相关≠ | 6 | 5 |
| 摘要≠ | Robust differential item functioning analysis detects items that behave differently across demographic groups after matching respondents on the underlying trait, while protecting the procedure against distortion by outliers, model misfit, or contaminated anchor items. It is applied in educational testing, clinical assessment, and survey research to ensure that a scale measures the same construct equally fairly for all groups. | Differential item functioning identifies test or survey items that behave differently for examinees from different groups — such as gender, ethnicity, or language background — after controlling for the underlying ability or trait being measured. DIF analysis is essential for fairness evaluation in educational testing and psychological scale development. |
| ScholarGate数据集 ↗ |
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