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	Bayesowskie modelowanie równań strukturalnych (BSEM)	Konfirmacyjna Analiza Czynnikowa (CFA)	Model krzywej wzrostu utajonego (LGC)
Dziedzina	Statystyka bayesowska	Statystyka	Statystyka
Rodzina	Bayesian methods	Latent structure	Latent structure
Rok powstania	2012	1969	1990
Twórca	Bengt Muthén & Tihomir Asparouhov	Karl Jöreskog	Meredith & Tisak
Typ	Bayesian latent variable model	Confirmatory latent variable model	Latent variable / longitudinal growth model
Źródło pierwotne	Muthén, B. & Asparouhov, T. (2012). Bayesian SEM: A More Flexible Representation of Substantive Theory. Psychological Methods, 17(3), 313–335.	Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363	Meredith, W. & Tisak, J. (1990). Latent Curve Analysis. Psychometrika, 55(1), 107–122.
Inne nazwy	BSEM, Bayesian latent variable model, approximate zero constraints SEM, Bayesçi Yapısal Eşitlik Modeli	Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model	latent growth model, LGC, growth curve model, Gizil Büyüme Eğrisi Modeli
Pokrewne	6	4	5
Podsumowanie	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.	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.	The latent growth curve model is a structural equation modelling approach introduced by Meredith and Tisak (1990) for analysing change over time. It treats each individual's starting point (intercept) and rate of change (slope) as latent variables, simultaneously estimating the average trajectory across the sample and the extent to which individuals differ in their own trajectories.