Сравнение методов

	Байесовский факторный анализ	Эксплораторный факторный анализ (ЭФА)
Область	Байесовские методы	Статистика
Семейство	Bayesian methods	Latent structure
Год появления	2004	—
Автор метода	Lopes & West (2004) for Bayesian model assessment in factor analysis	—
Тип	Bayesian latent variable model	Latent variable / dimension reduction
Основополагающий источник	Lopes, H. F. & West, M. (2004). Bayesian Model Assessment in Factor Analysis. Statistica Sinica, 14(1), 41–67.	Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.
Другие названия	Bayesian EFA, Bayesian CFA, Bayesçi Faktör Analizi, probabilistic factor analysis	common factor analysis, açımlayıcı faktör analizi, factor analysis
Связанные	7	4
Сводка	Bayesian Factor Analysis is a probabilistic latent-variable method that places prior distributions on the factor loading matrix and the residual variances, then infers a full posterior over these parameters from the observed data. Developed prominently in the Bayesian framework by Lopes and West (2004), it extends classical exploratory and confirmatory factor analysis by quantifying uncertainty in every estimated loading rather than reporting single point estimates.	Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance.