Порівняння методів

	Умовний аналіз процесів (модерована медіація)	Байєсівське моделювання структурними рівняннями (BSEM)	Причинний аналіз медіації (природні прямий та непрямий ефекти)	Регресія звичайно найменших квадратів (ЗНК)	Регресійний розривний дизайн (RDD)
Галузь	Причинно-наслідковий висновок	Баєсові методи	Причинно-наслідковий висновок	Економетрика	Причинно-наслідковий висновок
Родина	Regression model	Bayesian methods	Regression model	Regression model	Regression model
Рік появи	2018	2012	2010	2019	2008
Автор методу	Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)	Bengt Muthén & Tihomir Asparouhov	Pearl (2001); general framework by Imai, Keele & Tingley (2010)	Wooldridge (textbook treatment); classical least squares	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Тип	Regression-based conditional process model	Bayesian latent variable model	Counterfactual causal decomposition	Linear regression	Quasi-experimental causal design
Основоположне джерело	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.	Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
Інші назви	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	natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Пов'язані	5	6	5	5	5
Підсумок	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.	Causal mediation analysis is a counterfactual framework that splits a treatment's total effect into a Natural Direct Effect (NDE) and a Natural Indirect Effect (NIE) that runs through a mediator. The modern general approach was formalised by Pearl (2001) and Imai, Keele and Tingley (2010), giving the decomposition a precise causal interpretation.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.