Comparar métodos

	Diferencial de Itens Bayesiano (Bayesian DIF)	Análise Fatorial Confirmatória (AFC)
Área	Psicometria	Psicometria
Família	Latent structure	Latent structure
Ano de origem	1990s–2000s	1969
Autor original	H. Swaminathan & H. J. Rogers (classical DIF); Bayesian extensions developed through Markov chain Monte Carlo IRT methods in the 1990s–2000s	Karl Gustav Jöreskog
Tipo	Item bias detection / Bayesian inference	Hypothesis-testing latent variable model
Fonte seminal	Swaminathan, H., & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27(4), 361–370.	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.
Outros nomes	Bayesian DIF, Bayesian DIF analysis, Bayesian item bias detection, BDIF	CFA, confirmatory FA, measurement model, restricted factor analysis
Relacionados	5	4
Resumo	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.	Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing.