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| Bayesian Itemanalyse× | Differenzielles Item-Funktionieren (DIF)× | |
|---|---|---|
| Fachgebiet | Psychometrie | Psychometrie |
| Familie | Latent structure | Latent structure |
| Entstehungsjahr≠ | 1990s–2000s | 1970s–1993 |
| Urheber≠ | Originated in Bayesian psychometrics literature, developed extensively by Jean-Paul Fox and colleagues | William H. Angoff and colleagues (ETS); systematized by Holland & Wainer |
| Typ≠ | Bayesian inference / item-level diagnostics | Item-level bias detection |
| Wegweisende Quelle≠ | Fox, J.-P. (2010). Bayesian Item Response Modeling: Theory and Applications. Springer. DOI ↗ | Holland, P. W. & Wainer, H. (Eds.) (1993). Differential Item Functioning. Lawrence Erlbaum Associates. ISBN: 978-0805809589 |
| Aliasnamen | BIA, Bayesian classical item analysis, Bayesian item statistics, Bayesian item-level diagnostics | DIF, item bias analysis, measurement non-equivalence, item-level measurement bias |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | 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. | 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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