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| 다범주 문항차별기능 (Polytomous DIF)× | 문항 반응 이론 (IRT)× | |
|---|---|---|
| 분야 | 심리측정학 | 심리측정학 |
| 계열 | Latent structure | Latent structure |
| 기원 연도≠ | 1990s–2000s | 1952–1968 |
| 창시자≠ | Bruno D. Zumbo and colleagues (ordinal logistic regression framework); Robert D. Ankenmann, Hariharan Swaminathan and others (IRT-based extensions) | Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models) |
| 유형≠ | Measurement fairness / item bias detection | Probabilistic measurement model |
| 원전≠ | Zumbo, B. D. (1999). A handbook on the theory and methods of differential item functioning (DIF): Logistic regression modeling as a unitary framework for binary and Likert-type (ordinal) item scores. Directorate of Human Resources Research and Evaluation, Department of National Defense. link ↗ | Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley. link ↗ |
| 별칭 | Polytomous DIF, DIF for polytomous items, ordinal DIF analysis, graded-response DIF | IRT, latent trait theory, item characteristic curve theory, modern test theory |
| 관련≠ | 4 | 5 |
| 요약≠ | Polytomous differential item functioning detects whether a test or survey item with more than two ordered response categories (e.g., Likert-type scales, partial-credit items) functions differently across groups such as gender, ethnicity, or language background, after controlling for the latent trait being measured. It extends classical binary DIF methods to ordinal response formats. | Item response theory models the probability that a respondent answers an item correctly (or endorses it) as a function of the respondent's latent trait level and the item's own statistical properties — difficulty, discrimination, and guessing. Unlike classical test theory, IRT places persons and items on the same scale, yielding measurement that is sample-independent for items and test-independent for persons. |
| ScholarGate데이터셋 ↗ |
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