方法对比

	贝叶斯项目函数差异 (Bayesian DIF)	多组别项目功能差异 (MG-DIF)
领域	心理测量学	心理测量学
方法族	Latent structure	Latent structure
起源年份	1990s–2000s	1980s-1990s
提出者	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)
类型	Item bias detection / Bayesian inference	Measurement bias detection
开创性文献	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
别名	Bayesian DIF, Bayesian DIF analysis, Bayesian item bias detection, BDIF	MG-DIF, multi-group DIF, differential item functioning across groups, multiple-group DIF analysis
相关	5	6
摘要	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.