Methoden vergleichen

	Kausale Mediationsanalyse (Natürliche Direkte und Indirekte Effekte)	Bedingte Prozessanalyse (moderierte Mediation)	Mediationsanalyse
Fachgebiet	Kausale Inferenz	Kausale Inferenz	Statistik
Familie	Regression model	Regression model	Hypothesis test
Entstehungsjahr	2010	2018	1986
Urheber	Pearl (2001); general framework by Imai, Keele & Tingley (2010)	Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)	Baron & Kenny
Typ	Counterfactual causal decomposition	Regression-based conditional process model	Indirect effects / path test
Wegweisende Quelle	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	Baron, R. M. & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research. Journal of Personality and Social Psychology, 51(6), 1173–1182.
Aliasnamen	natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation	moderated mediation, moderated mediation analysis, PROCESS model, Hayes PROCESS conditional process model	indirect effects analysis, path-based mediation, PROCESS macro mediation, Aracılık Analizi (Mediation / PROCESS)
Verwandt	5	5	5
Zusammenfassung	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.	Mediation analysis is a statistical procedure that tests whether the effect of an independent variable X on an outcome Y operates wholly or partly through a third variable M, called the mediator. Formalised by Baron and Kenny in 1986, it decomposes the total effect of X on Y into a direct path (c′) and an indirect path (a × b), quantifying how much of the relationship is carried by the mediating mechanism.