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| Monitasoinen yleistettävyysteoria× | Vastausfunktioiden teoria (IRT)× | |
|---|---|---|
| Tieteenala | Psykometriikka | Psykometriikka |
| Menetelmäperhe | Latent structure | Latent structure |
| Syntyvuosi≠ | 1990s–2000s | 1952–1968 |
| Kehittäjä≠ | Brennan, R. L. and Shavelson, R. J. (extensions of Cronbach et al. G-theory to multilevel designs) | Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models) |
| Tyyppi≠ | Measurement / variance decomposition | Probabilistic measurement model |
| Alkuperäislähde≠ | Briggs, D. C. & Wilson, M. (2003). An introduction to multidimensional measurement using Rasch models and generalizability theory. Journal of Applied Measurement, 4(1), 1–19. link ↗ | Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley. link ↗ |
| Rinnakkaisnimet | multilevel G-theory, ML-GT, hierarchical generalizability theory, multilevel G-study | IRT, latent trait theory, item characteristic curve theory, modern test theory |
| Liittyvät≠ | 4 | 5 |
| Tiivistelmä≠ | Multilevel generalizability theory extends classical G-theory to measurement designs where observations are nested within higher-level units — for example, items nested within raters, or students nested within classrooms. It decomposes score variance into components attributable to persons, facets, and their interactions across hierarchical levels, enabling precise estimation of measurement precision in complex, real-world assessment settings. | 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. |
| ScholarGateAineisto ↗ |
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