Comparer des méthodes

	Analyse des processus conditionnels (médiation modérée)	Modélisation Bayésienne par Équations Structurelles (BSEM)
Domaine	Inférence causale	Bayésien
Famille	Regression model	Bayesian methods
Année d'origine	2018	2012
Auteur d'origine	Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)	Bengt Muthén & Tihomir Asparouhov
Type	Regression-based conditional process model	Bayesian latent variable model
Source fondatrice	Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2nd ed.). The Guilford Press. ISBN: 978-1462534654	Muthén, B. & Asparouhov, T. (2012). Bayesian SEM: A More Flexible Representation of Substantive Theory. Psychological Methods, 17(3), 313–335.
Alias	moderated mediation, moderated mediation analysis, PROCESS model, Hayes PROCESS conditional process model	BSEM, Bayesian latent variable model, approximate zero constraints SEM, Bayesçi Yapısal Eşitlik Modeli
Apparentées	5	6
Résumé	Conditional process analysis is Andrew F. Hayes's regression-based PROCESS framework (2018) that combines mediation and moderation in a single model, testing how an indirect effect changes across levels of a moderator. It quantifies conditional indirect and conditional direct effects and tests them with bootstrap confidence intervals.	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.