Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовский анализ заданий× | Теория отклика на задания (IRT)× | |
|---|---|---|
| Область | Психометрия | Психометрия |
| Семейство | Latent structure | Latent structure |
| Год появления≠ | 1990s–2000s | 1952–1968 |
| Автор метода≠ | Originated in Bayesian psychometrics literature, developed extensively by Jean-Paul Fox and colleagues | Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models) |
| Тип≠ | Bayesian inference / item-level diagnostics | Probabilistic measurement model |
| Основополагающий источник≠ | Fox, J.-P. (2010). Bayesian Item Response Modeling: Theory and Applications. Springer. DOI ↗ | Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley. link ↗ |
| Другие названия | BIA, Bayesian classical item analysis, Bayesian item statistics, Bayesian item-level diagnostics | IRT, latent trait theory, item characteristic curve theory, modern test theory |
| Связанные≠ | 4 | 5 |
| Сводка≠ | Bayesian item analysis applies Bayesian inference to estimate item-level statistics — difficulty, discrimination, and distractor effectiveness — by combining observed response data with prior knowledge. It produces full posterior distributions over item parameters rather than single point estimates, providing richer uncertainty information especially with small samples. | 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. |
| ScholarGateНабор данных ↗ |
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