Salīdzināt metodes

	Bayesiskā faktoru analīze	Korelatīvās faktoru analīzes (KFA)	Eksploratīvā faktoru analīze (EFA)
Nozare	Bajesa metodes	Statistika	Statistika
Saime	Bayesian methods	Latent structure	Latent structure
Izcelsmes gads	2004	1969	—
Autors	Lopes & West (2004) for Bayesian model assessment in factor analysis	Karl Jöreskog	—
Tips	Bayesian latent variable model	Confirmatory latent variable model	Latent variable / dimension reduction
Pirmavots	Lopes, H. F. & West, M. (2004). Bayesian Model Assessment in Factor Analysis. Statistica Sinica, 14(1), 41–67.	Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363	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.
Citi nosaukumi	Bayesian EFA, Bayesian CFA, Bayesçi Faktör Analizi, probabilistic factor analysis	Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model	common factor analysis, açımlayıcı faktör analizi, factor analysis
Saistītās	7	4	4
Kopsavilkums	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.	Confirmatory factor analysis tests whether a researcher-specified factor structure fits the observed data. Formalised by Karl Jöreskog in 1969, it is the measurement-model step within structural equation modelling and is the standard tool for validating the factorial structure of scales and questionnaires before comparing groups or estimating latent relationships.	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.