Confronta i metodi

	Analisi di Mediazione Multilivello	Analisi di mediazione causale (effetti diretti e indiretti naturali)	Analisi di processo condizionale (mediazione moderata)	Analisi di mediazione
Campo	Statistica	Inferenza causale	Inferenza causale	Statistica
Famiglia	Hypothesis test	Regression model	Regression model	Hypothesis test
Anno di origine	2003	2010	2018	1986
Ideatore	Kenny, Korchmaros & Bolger	Pearl (2001); general framework by Imai, Keele & Tingley (2010)	Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)	Baron & Kenny
Tipo	Multilevel structural model	Counterfactual causal decomposition	Regression-based conditional process model	Indirect effects / path test
Fonte seminale	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	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.
Alias	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	indirect effects analysis, path-based mediation, PROCESS macro mediation, Aracılık Analizi (Mediation / PROCESS)
Correlati	8	5	5	5
Sintesi	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.	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.