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| Generalisierbarkeitstheorie für computergestützte adaptive Tests× | Item Response Theory (IRT)× | |
|---|---|---|
| Fachgebiet | Psychometrie | Psychometrie |
| Familie | Latent structure | Latent structure |
| Entstehungsjahr≠ | 1972 (G-theory); CAT application 1990s–2000s | 1952–1968 |
| Urheber≠ | Lee J. Cronbach (G-theory); applied to CAT by Brennan and others | Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models) |
| Typ≠ | Reliability / generalizability analysis | Probabilistic measurement model |
| Wegweisende Quelle≠ | Brennan, R. L. (2001). Generalizability Theory. Springer. ISBN: 978-0387952826 | Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley. link ↗ |
| Aliasnamen | CAT G-theory, adaptive test generalizability, G-theory in CAT, computerized adaptive generalizability analysis | IRT, latent trait theory, item characteristic curve theory, modern test theory |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | Generalizability theory (G-theory) applied to computerized adaptive testing (CAT) evaluates the dependability of adaptive test scores by decomposing score variance across measurement facets such as persons, items, and occasions. Unlike classical test theory, G-theory quantifies multiple simultaneous sources of measurement error, offering a richer reliability picture for adaptively administered assessments. | 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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