Compară metode

	Experiment factorial complet randomizat pe clustere	Studiu controlat randomizat factorial
Domeniu	Design experimental	Design experimental
Familie	Process / pipeline	Process / pipeline
Anul apariției	Late 20th–early 21st century (formalized ~1998–2014)	1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization)
Autorul original	Synthesis of cluster randomization (Murray, 1998) and factorial design traditions (Fisher, 1935; Collins et al., 2014)	R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014)
Tip	Experimental design	Experimental trial design
Sursa seminală	Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials. Oxford University Press. ISBN: 978-0195120264	Collins, L. M., Dziak, J. J., Kugler, K. C., & Trail, J. B. (2014). Factorial experiments: Efficient tools for evaluation of intervention components. American Journal of Preventive Medicine, 47(4), 498–504.
Denumiri alternative	cluster RCT full factorial, group-randomized full factorial design, CRT full factorial, cluster full factorial trial	Factorial RCT, factorial trial, multi-factor RCT, factorial experiment with randomization
Înrudite	6	6
Rezumat	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.	A factorial randomized controlled trial (factorial RCT) is an experimental design in which participants are randomly assigned to every possible combination of two or more independent factors (treatments or intervention components) simultaneously. This allows researchers to estimate the main effect of each factor and their interactions within a single, efficient trial, rather than running separate experiments for each factor.