Salīdzināt metodes

	Klasterizēts pilna faktoriālā eksperimenta dizains	Faktoriālais randomizētais kontrolētais pētījums
Nozare	Eksperimentu plānošana	Eksperimentu plānošana
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	Late 20th–early 21st century (formalized ~1998–2014)	1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization)
Autors	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)
Tips	Experimental design	Experimental trial design
Pirmavots	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.
Citi nosaukumi	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
Saistītās	6	6
Kopsavilkums	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.