Сравнение методов

	Байесовский дифференциальный функционал элемента (Bayesian DIF)	Многогрупповой дифференциальный анализ элементов (MG-DIF)
Область	Психометрия	Психометрия
Семейство	Latent structure	Latent structure
Год появления	1990s–2000s	1980s-1990s
Автор метода	H. Swaminathan & H. J. Rogers (classical DIF); Bayesian extensions developed through Markov chain Monte Carlo IRT methods in the 1990s–2000s	Shealy & Stout (SIBTEST framework); Lord (IRT-based DIF)
Тип	Item bias detection / Bayesian inference	Measurement bias detection
Основополагающий источник	Swaminathan, H., & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27(4), 361–370.	Millsap, R. E. (2012). Statistical Approaches to Measurement Invariance. Routledge. ISBN: 978-1848728936
Другие названия	Bayesian DIF, Bayesian DIF analysis, Bayesian item bias detection, BDIF	MG-DIF, multi-group DIF, differential item functioning across groups, multiple-group DIF analysis
Связанные	5	6
Сводка	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.	Multi-group differential item functioning examines whether test or scale items function equivalently across three or more distinct groups — such as gender, ethnicity, or country — after matching respondents on the underlying trait being measured. Items that behave differently across groups threaten fair measurement and valid score comparisons.