Compara mètodes

	Anàlisi de mediació multinivell	Anàlisi de mediació causal (efectes directes i indirectes naturals)	Modelització Lineal Jeràrquica (HLM / Modelització Multinivell)
Camp	Estadística	Inferència causal	Estadística
Família	Hypothesis test	Regression model	Hypothesis test
Any d'origen	2003	2010	1986
Autor original	Kenny, Korchmaros & Bolger	Pearl (2001); general framework by Imai, Keele & Tingley (2010)	Raudenbush & Bryk (popularized); Goldstein (parallel development)
Tipus	Multilevel structural model	Counterfactual causal decomposition	Parametric nested-data regression
Font seminal	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.	Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049
Àlies	multilevel mediation, hierarchical mediation, cross-level mediation, 1-1-1 mediation	natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation	HLM, MLM, multilevel modeling, multilevel analysis
Relacionats	8	5	4
Resum	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.	Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels.