Porovnat metody

	Vícúrovňová analýza mediace	Kauzalní mediátorová analýza (přirozené přímé a nepřímé účinky)	Podmíněná analýza procesů (moderovaná mediace)
Obor	Statistika	Kauzální inference	Kauzální inference
Rodina	Hypothesis test	Regression model	Regression model
Rok vzniku	2003	2010	2018
Tvůrce	Kenny, Korchmaros & Bolger	Pearl (2001); general framework by Imai, Keele & Tingley (2010)	Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)
Typ	Multilevel structural model	Counterfactual causal decomposition	Regression-based conditional process model
Původní zdroj	Kenny, D. A., Korchmaros, J. D., & Bolger, N. (2003). Lower level mediation in multilevel models. Psychological Methods, 8(2), 115–128.	Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420.	Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2nd ed.). The Guilford Press. ISBN: 978-1462534654
Další názvy	multilevel mediation, hierarchical mediation, cross-level mediation, 1-1-1 mediation	natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation	moderated mediation, moderated mediation analysis, PROCESS model, Hayes PROCESS conditional process model
Příbuzné	8	5	5
Shrnutí	Multilevel mediation analysis is a parametric structural method that estimates indirect (mediated) effects within hierarchically nested data, such as students within schools or employees within organisations. Formalised for lower-level mediation in multilevel models by Kenny, Korchmaros and Bolger (2003), it simultaneously handles individual-level (1-1-1) and group-level (2-2-1 or 2-1-1) mediation pathways in a single coherent framework.	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.	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.