Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| CAT-DIF× | Uchambuzi wa Kipengee cha Kujiendesha kwa Kompyuta× | |
|---|---|---|
| Nyanja | Saikometriki | Saikometriki |
| Familia | Latent structure | Latent structure |
| Mwaka wa asili≠ | 1990s–2000s | 1970s–1990s |
| Mwanzilishi≠ | Wainer, Zwick, and colleagues in the CAT and DIF literatures | Lord, Weiss, and colleagues in psychometric research on adaptive testing |
| Aina≠ | Item bias detection in adaptive testing | Item calibration and evaluation |
| Chanzo asilia≠ | Zwick, R., Thayer, D. T., & Mazzeo, J. (1997). Describing and categorizing DIF in polytomous items. Journal of Educational Measurement, 34(4), 261–285. DOI ↗ | van der Linden, W. J. & Glas, C. A. W. (Eds.) (2000). Computerized Adaptive Testing: Theory and Practice. Kluwer Academic Publishers. ISBN: 978-0792365556 |
| Majina mbadala | CAT DIF analysis, adaptive test DIF, DIF in computerized adaptive testing, CAT item bias detection | CAT item analysis, adaptive item calibration, IRT-based CAT item evaluation, adaptive item parameter estimation |
| Zinazohusiana | 6 | 6 |
| Muhtasari≠ | 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. | Computerized adaptive test item analysis evaluates and calibrates items intended for use in adaptive testing environments. Unlike fixed-form analysis, it accounts for the non-random item exposure inherent in adaptive administration, using item response theory to estimate item parameters, information functions, and exposure rates across the ability continuum. |
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