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| 베이즈 차별 문항 기능 (베이즈 DIF)× | 차별 문항 기능(Differential Item Functioning, DIF)× | |
|---|---|---|
| 분야 | 심리측정학 | 심리측정학 |
| 계열 | Latent structure | Latent structure |
| 기원 연도≠ | 1990s–2000s | 1970s–1993 |
| 창시자≠ | H. Swaminathan & H. J. Rogers (classical DIF); Bayesian extensions developed through Markov chain Monte Carlo IRT methods in the 1990s–2000s | William H. Angoff and colleagues (ETS); systematized by Holland & Wainer |
| 유형≠ | Item bias detection / Bayesian inference | Item-level bias detection |
| 원전≠ | Swaminathan, H., & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27(4), 361–370. DOI ↗ | Holland, P. W. & Wainer, H. (Eds.) (1993). Differential Item Functioning. Lawrence Erlbaum Associates. ISBN: 978-0805809589 |
| 별칭 | Bayesian DIF, Bayesian DIF analysis, Bayesian item bias detection, BDIF | DIF, item bias analysis, measurement non-equivalence, item-level measurement bias |
| 관련 | 5 | 5 |
| 요약≠ | Bayesian differential item functioning analysis detects whether a test item behaves differently across demographic or cultural groups — such as males vs. females — after accounting for the underlying ability or trait being measured. It applies Bayesian IRT estimation to obtain posterior distributions of item parameters separately per group, then evaluates group differences with posterior credibility intervals or Bayes factors rather than classical p-values. | 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. |
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