Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Multilevel Differential Item Functioning× | Vienuma atbildes teorija (IRT)× | |
|---|---|---|
| Nozare | Psihometrija | Psihometrija |
| Saime | Latent structure | Latent structure |
| Izcelsmes gads≠ | 2001 | 1952–1968 |
| Autors≠ | Kamata (2001) and subsequent multilevel IRT/DIF literature | Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models) |
| Tips≠ | Bias detection / multilevel measurement model | Probabilistic measurement model |
| Pirmavots≠ | French, B. F., & Finch, W. H. (2008). Multigroup confirmatory factor analysis: Locating the invariant referent sets. Structural Equation Modeling: A Multidisciplinary Journal, 15(1), 96–113. DOI ↗ | Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley. link ↗ |
| Citi nosaukumi | multilevel DIF, hierarchical DIF analysis, cross-level DIF, ML-DIF | IRT, latent trait theory, item characteristic curve theory, modern test theory |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Multilevel DIF analysis detects whether individual test or survey items function differently across groups when respondents are clustered within higher-level units — such as students nested in schools, employees in organizations, or patients in clinics. By accounting for hierarchical data structure, it separates genuine item bias from artificial DIF signals caused by ignoring clustering. | 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. |
| ScholarGateDatu kopa ↗ |
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