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	Bayesian Differential Item Functioning	Teoria odpowiedzi na pozycje (IRT)
Dziedzina	Psychometria	Psychometria
Rodzina	Latent structure	Latent structure
Rok powstania	1990s–2000s	1952–1968
Twórca	H. Swaminathan & H. J. Rogers (classical DIF); Bayesian extensions developed through Markov chain Monte Carlo IRT methods in the 1990s–2000s	Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models)
Typ	Item bias detection / Bayesian inference	Probabilistic measurement model
Źródło pierwotne	Swaminathan, H., & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27(4), 361–370.	Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley.
Inne nazwy	Bayesian DIF, Bayesian DIF analysis, Bayesian item bias detection, BDIF	IRT, latent trait theory, item characteristic curve theory, modern test theory
Pokrewne	5	5
Podsumowanie	Bayesian differential item functioning analysis detects whether a test item behaves differently across demographic or cultural groups — such as males vs. females — after accounting for the underlying ability or trait being measured. It applies Bayesian IRT estimation to obtain posterior distributions of item parameters separately per group, then evaluates group differences with posterior credibility intervals or Bayes factors rather than classical p-values.	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.