방법 비교

	베이지안 구조 방정식 모형 (Bayesian Structural Equation Modeling, BSEM)	베이지안 계층 모델	확인적 요인분석(CFA)
분야	베이지안	베이지안	통계학
계열	Bayesian methods	Bayesian methods	Latent structure
기원 연도	2012	2006	1969
창시자	Bengt Muthén & Tihomir Asparouhov	Gelman & Hill (2006); Bayesian multilevel tradition	Karl Jöreskog
유형	Bayesian latent variable model	hierarchical probabilistic model	Confirmatory latent variable model
원전	Muthén, B. & Asparouhov, T. (2012). Bayesian SEM: A More Flexible Representation of Substantive Theory. Psychological Methods, 17(3), 313–335.	Gelman, A. & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.	Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363
별칭	BSEM, Bayesian latent variable model, approximate zero constraints SEM, Bayesçi Yapısal Eşitlik Modeli	multilevel Bayes, Bayesian multilevel model, Bayesian HLM, partial pooling model	Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model
관련	6	4	4
요약	Bayesian SEM, introduced by Muthén and Asparouhov in 2012, extends classical structural equation modeling by placing prior distributions on factor loadings, path coefficients, and covariances. Instead of returning a single maximum-likelihood estimate, it uses Markov chain Monte Carlo to produce a full posterior distribution for every parameter, enabling principled uncertainty quantification in models with latent variables.	Bayesian hierarchical modelling, popularised by Gelman and Hill (2006), is a Bayesian approach to nested data structures — such as students within schools within districts — that estimates separate parameters at each level while allowing those levels to share statistical strength through a mechanism called partial pooling. Where a classical hierarchical linear model treats group means as fixed unknown quantities, the Bayesian version places hyperprior distributions on those group means so that information flows freely across levels, producing more reliable group-level estimates whenever any individual group has few observations.	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.