Comparar métodos

	Análisis de Mediación Multinivel	Análisis de procesos condicionales (mediación moderada)	Modelado Lineal Jerárquico (HLM / Modelado Multinivel)	Análisis de Mediación
Campo	Estadística	Inferencia causal	Estadística	Estadística
Familia	Hypothesis test	Regression model	Hypothesis test	Hypothesis test
Año de origen	2003	2018	1986	1986
Autor original	Kenny, Korchmaros & Bolger	Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)	Raudenbush & Bryk (popularized); Goldstein (parallel development)	Baron & Kenny
Tipo	Multilevel structural model	Regression-based conditional process model	Parametric nested-data regression	Indirect effects / path test
Fuente seminal	Kenny, D. A., Korchmaros, J. D., & Bolger, N. (2003). Lower level mediation in multilevel models. Psychological Methods, 8(2), 115–128.	Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2nd ed.). The Guilford Press. ISBN: 978-1462534654	Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049	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	moderated mediation, moderated mediation analysis, PROCESS model, Hayes PROCESS conditional process model	HLM, MLM, multilevel modeling, multilevel analysis	indirect effects analysis, path-based mediation, PROCESS macro mediation, Aracılık Analizi (Mediation / PROCESS)
Relacionados	8	5	4	5
Resumen	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.	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.	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.	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.