Comparer des méthodes

	Modèle Linéaire Hiérarchique (HLM)	Modélisation multiniveau
Domaine	Statistique	Statistiques de recherche
Famille	Regression model	Process / pipeline
Année d'origine	1992	1992
Auteur d'origine	Bryk & Raudenbush	Anthony Bryk and Stephen Raudenbush
Type	Multilevel linear regression	Method
Source fondatrice	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications. ISBN: 978-0761919049	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Alias	HLM, multilevel linear model, nested data model, random coefficient model	HLM, mixed-effects models, random effects models, MLM
Apparentées	4	3
Résumé	The Hierarchical Linear Model (HLM) is a multilevel regression method designed for data in which lower-level units (e.g., students, patients) are nested within higher-level groups (e.g., schools, hospitals). It simultaneously models within-group relationships and between-group variation, producing unbiased estimates and correct standard errors that ordinary regression cannot provide for nested data.	Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies.