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| Multilevel Differential Item Functioning× | Diferenciālā vienumu funkcionēšana (DVF)× | |
|---|---|---|
| Nozare | Psihometrija | Psihometrija |
| Saime | Latent structure | Latent structure |
| Izcelsmes gads≠ | 2001 | 1970s–1993 |
| Autors≠ | Kamata (2001) and subsequent multilevel IRT/DIF literature | William H. Angoff and colleagues (ETS); systematized by Holland & Wainer |
| Tips≠ | Bias detection / multilevel measurement model | Item-level bias detection |
| 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 ↗ | Holland, P. W. & Wainer, H. (Eds.) (1993). Differential Item Functioning. Lawrence Erlbaum Associates. ISBN: 978-0805809589 |
| Citi nosaukumi | multilevel DIF, hierarchical DIF analysis, cross-level DIF, ML-DIF | DIF, item bias analysis, measurement non-equivalence, item-level measurement bias |
| 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. | Differential item functioning identifies test or survey items that behave differently for examinees from different groups — such as gender, ethnicity, or language background — after controlling for the underlying ability or trait being measured. DIF analysis is essential for fairness evaluation in educational testing and psychological scale development. |
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