Comparer des méthodes

	Modèle Linéaire Hiérarchique Robuste	Modélisation multiniveau
Domaine	Statistique	Statistiques de recherche
Famille	Regression model	Process / pipeline
Année d'origine	2004	1992
Auteur d'origine	Maas & Hox (2004); Goldstein et al. (2018)	Anthony Bryk and Stephen Raudenbush
Type	Robust multilevel regression	Method
Source fondatrice	Maas, C. J. M., & Hox, J. J. (2004). Robustness issues in multilevel regression analysis. Statistica Neerlandica, 58(2), 127–137.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Alias	robust HLM, robust multilevel model, robust mixed-effects linear model, robust nested regression	HLM, mixed-effects models, random effects models, MLM
Apparentées	5	3
Résumé	Robust Hierarchical Linear Model (Robust HLM) extends standard HLM by replacing or protecting its standard errors against violations of distributional assumptions — chiefly non-normal residuals, heteroscedasticity, and influential clusters. It retains the nested, two-level (or higher) structure while producing more trustworthy inference under real-world data conditions.	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.