So sánh phương pháp

	Phân tích tiến trình có điều kiện (Trung gian có điều hòa)	Mô hình phương trình cấu trúc Bayes (BSEM)
Lĩnh vực	Suy luận nhân quả	Bayes
Họ	Regression model	Bayesian methods
Năm ra đời	2018	2012
Người khởi xướng	Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)	Bengt Muthén & Tihomir Asparouhov
Loại	Regression-based conditional process model	Bayesian latent variable model
Công trình gốc	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.
Tên gọi khác	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
Liên quan	5	6
Tóm tắt	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.