Vertaile menetelmiä

	Bayesilainen regressio	Vahvistava faktorianalyysi (CFA)	Eksploratiivinen faktorianalyysi (EFA)
Tieteenala	Bayesilainen tilastotiede	Tilastotiede	Tilastotiede
Menetelmäperhe	Bayesian methods	Latent structure	Latent structure
Syntyvuosi	—	1969	—
Kehittäjä	—	Karl Jöreskog	—
Tyyppi	Bayesian linear model	Confirmatory latent variable model	Latent variable / dimension reduction
Alkuperäislähde	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	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.
Rinnakkaisnimet	bayesian linear regression, probabilistic regression, bayesian regresyon	Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model	common factor analysis, açımlayıcı faktör analizi, factor analysis
Liittyvät	2	4	4
Tiivistelmä	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.	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.