Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchambuzi wa Kuegemeza kwa Upimaji unaoendeshwa na Kompyuta× | Nadharia ya Itikio la Kipengee (IRT)× | |
|---|---|---|
| Nyanja | Saikometriki | Saikometriki |
| Familia | Latent structure | Latent structure |
| Mwaka wa asili≠ | 1970s–1980s | 1952–1968 |
| Mwanzilishi≠ | David J. Weiss and IRT psychometricians | Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models) |
| Aina≠ | Reliability estimation under adaptive testing | Probabilistic measurement model |
| Chanzo asilia≠ | Weiss, D. J. (1984). Application of computerized adaptive testing to educational problems. Journal of Educational Measurement, 21(4), 361–375. DOI ↗ | Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley. link ↗ |
| Majina mbadala | CAT reliability, adaptive test reliability, IRT-based reliability estimation, marginal reliability in CAT | IRT, latent trait theory, item characteristic curve theory, modern test theory |
| Zinazohusiana≠ | 4 | 5 |
| Muhtasari≠ | CAT reliability analysis quantifies measurement precision in computerized adaptive tests where each examinee receives a unique, individually tailored subset of items. Rather than a single classical coefficient, it uses item response theory to express precision as conditional standard error of measurement at each ability level, and marginal reliability as a global summary across the ability distribution. | 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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