Sammenlign metoder

	Multiniveau-medieringsanalyse	Kausal formidlingsanalyse (naturlige direkte og indirekte effekter)	Mediationsanalyse
Fagområde	Statistik	Kausal inferens	Statistik
Familie	Hypothesis test	Regression model	Hypothesis test
Oprindelsesår	2003	2010	1986
Ophavsperson	Kenny, Korchmaros & Bolger	Pearl (2001); general framework by Imai, Keele & Tingley (2010)	Baron & Kenny
Type	Multilevel structural model	Counterfactual causal decomposition	Indirect effects / path test
Oprindelig kilde	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.	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.
Aliasser	multilevel mediation, hierarchical mediation, cross-level mediation, 1-1-1 mediation	natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation	indirect effects analysis, path-based mediation, PROCESS macro mediation, Aracılık Analizi (Mediation / PROCESS)
Relaterede	8	5	5
Resumé	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.	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.