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| Datorizēta adaptīvā testa diferenciālā vienumu funkcionēšana (CAT-DIF)× | Vienuma atbildes teorija (IRT)× | |
|---|---|---|
| Nozare | Psihometrija | Psihometrija |
| Saime | Latent structure | Latent structure |
| Izcelsmes gads≠ | 1990s–2000s | 1952–1968 |
| Autors≠ | Wainer, Zwick, and colleagues in the CAT and DIF literatures | Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models) |
| Tips≠ | Item bias detection in adaptive testing | Probabilistic measurement model |
| Pirmavots≠ | Zwick, R., Thayer, D. T., & Mazzeo, J. (1997). Describing and categorizing DIF in polytomous items. Journal of Educational Measurement, 34(4), 261–285. DOI ↗ | Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley. link ↗ |
| Citi nosaukumi | CAT DIF analysis, adaptive test DIF, DIF in computerized adaptive testing, CAT item bias detection | IRT, latent trait theory, item characteristic curve theory, modern test theory |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | CAT-DIF identifies items in a computerized adaptive test that behave differently across demographic or group subpopulations after controlling for overall ability. Because adaptive algorithms select items non-randomly based on each examinee's estimated proficiency, standard DIF detection methods require adjustment before they can be validly applied in this context. | 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. |
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