Comparer des méthodes

	Fonctionnement Différentiel des Items Bayésien (DIF Bayésien)	Test d'invariance de mesure
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	1990s–2000s	2000
Auteur d'origine	H. Swaminathan & H. J. Rogers (classical DIF); Bayesian extensions developed through Markov chain Monte Carlo IRT methods in the 1990s–2000s	Vandenberg & Lance
Type	Item bias detection / Bayesian inference	Multi-group confirmatory factor analysis procedure
Source fondatrice	Swaminathan, H., & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27(4), 361–370.	Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature. Organizational Research Methods, 3(1), 4–70.
Alias	Bayesian DIF, Bayesian DIF analysis, Bayesian item bias detection, BDIF	Factorial Invariance, Measurement Equivalence, Configural-Metric-Scalar Testing, Ölçüm Değişmezliği
Apparentées	5	3
Résumé	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.	Measurement invariance testing is a sequence of nested confirmatory factor analysis (CFA) models that examines whether a psychological scale measures the same latent construct in the same way across distinct groups or time points. Systematized and popularized by Vandenberg and Lance (2000), the procedure tests a hierarchy of constraints — from identical factor patterns to identical item intercepts — so that researchers can justify meaningful group comparisons on latent means.