Confronta i metodi

	Esperimento Fattoriale Completo Randomizzato a Cluster	Modellazione multilivello
Campo	Disegno sperimentale	Statistica per la ricerca
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	Late 20th–early 21st century (formalized ~1998–2014)	1992
Ideatore	Synthesis of cluster randomization (Murray, 1998) and factorial design traditions (Fisher, 1935; Collins et al., 2014)	Anthony Bryk and Stephen Raudenbush
Tipo	Experimental design	Method
Fonte seminale	Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials. Oxford University Press. ISBN: 978-0195120264	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Alias	cluster RCT full factorial, group-randomized full factorial design, CRT full factorial, cluster full factorial trial	HLM, mixed-effects models, random effects models, MLM
Correlati	6	3
Sintesi	A cluster-randomized full factorial experiment assigns intact groups (clusters) rather than individuals to every possible combination of two or more experimental factors. All factor-level combinations are tested simultaneously, enabling estimation of both main effects and all interaction effects, while preserving the integrity of naturally occurring social or organizational units such as schools, clinics, or communities.	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.