Comparer des méthodes

	Expérience de laboratoire randomisée par clusters	Modélisation multiniveau
Domaine	Plans d'expériences	Statistiques de recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s (formalized; cluster randomization principles developed in 1970s-1980s)	1992
Auteur d'origine	David M. Murray (group-randomized trial methodology); built on classical cluster sampling in experimental design	Anthony Bryk and Stephen Raudenbush
Type	Controlled laboratory experiment with cluster-level randomization	Method
Source fondatrice	Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials. Oxford University Press. ISBN: 978-0195120363	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Alias	cluster-randomized lab experiment, group-randomized laboratory study, cluster RCT laboratory variant, clustered lab trial	HLM, mixed-effects models, random effects models, MLM
Apparentées	6	3
Résumé	A cluster randomized laboratory experiment assigns intact groups — such as lab sections, cohorts, or naturally formed teams — rather than individual participants, to experimental conditions. All participants within a cluster receive the same treatment. The design is used when individual randomization would cause contamination between conditions, while retaining the controlled environment of a laboratory setting.	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.