विधियों की तुलना करें

	बाइसेशियन आइटम विश्लेषण	आइटम रिस्पांस थ्योरी (IRT)
क्षेत्र	मनोमिति	मनोमिति
परिवार	Latent structure	Latent structure
उद्भव वर्ष	1990s–2000s	1952–1968
प्रवर्तक	Originated in Bayesian psychometrics literature, developed extensively by Jean-Paul Fox and colleagues	Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models)
प्रकार	Bayesian inference / item-level diagnostics	Probabilistic measurement model
मौलिक स्रोत	Fox, J.-P. (2010). Bayesian Item Response Modeling: Theory and Applications. Springer.	Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley.
उपनाम	BIA, Bayesian classical item analysis, Bayesian item statistics, Bayesian item-level diagnostics	IRT, latent trait theory, item characteristic curve theory, modern test theory
संबंधित	4	5
सारांश	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.	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.