Comparer des méthodes

	Théorie de la généralisabilité multiniveau	Modélisation multiniveau
Domaine	Psychométrie	Statistiques de recherche
Famille	Latent structure	Process / pipeline
Année d'origine	1990s–2000s	1992
Auteur d'origine	Brennan, R. L. and Shavelson, R. J. (extensions of Cronbach et al. G-theory to multilevel designs)	Anthony Bryk and Stephen Raudenbush
Type	Measurement / variance decomposition	Method
Source fondatrice	Briggs, D. C. & Wilson, M. (2003). An introduction to multidimensional measurement using Rasch models and generalizability theory. Journal of Applied Measurement, 4(1), 1–19.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Alias	multilevel G-theory, ML-GT, hierarchical generalizability theory, multilevel G-study	HLM, mixed-effects models, random effects models, MLM
Apparentées	4	3
Résumé	Multilevel generalizability theory extends classical G-theory to measurement designs where observations are nested within higher-level units — for example, items nested within raters, or students nested within classrooms. It decomposes score variance into components attributable to persons, facets, and their interactions across hierarchical levels, enabling precise estimation of measurement precision in complex, real-world assessment settings.	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.