Comparer des méthodes

	Analyse des processus conditionnels (médiation modérée)	Modélisation Linéaire Hiérarchique (HLM / Modélisation Multiniveaux)
Domaine	Inférence causale	Statistique
Famille	Regression model	Hypothesis test
Année d'origine	2018	1986
Auteur d'origine	Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)	Raudenbush & Bryk (popularized); Goldstein (parallel development)
Type	Regression-based conditional process model	Parametric nested-data regression
Source fondatrice	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
Alias	moderated mediation, moderated mediation analysis, PROCESS model, Hayes PROCESS conditional process model	HLM, MLM, multilevel modeling, multilevel analysis
Apparentées	5	4
Résumé	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.