Сравнение методов

	Кластерный рандомизированный дизайн Соломона с четырьмя группами	Факториальное рандомизированное контролируемое исследование
Область	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Process / pipeline
Год появления	1949 (Solomon design); cluster extension formalized in 1990s	1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization)
Автор метода	Richard L. Solomon (four-group logic, 1949); cluster randomization methods developed by Murray and colleagues in the 1990s	R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014)
Тип	Experimental design	Experimental trial design
Основополагающий источник	Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46(2), 137–150.	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.
Другие названия	CR-S4GD, cluster-randomized four-group design, group-randomized Solomon design, Solomon four-group cluster trial	Factorial RCT, factorial trial, multi-factor RCT, factorial experiment with randomization
Связанные	6	6
Сводка	The cluster randomized Solomon four-group design combines cluster randomization — assigning intact groups such as schools, clinics, or communities to conditions — with the Solomon four-group structure that isolates the effect of pretesting. Four clusters (or sets of clusters) are created: two receive the treatment and two serve as controls, with only one treatment cluster and one control cluster receiving a pretest, while the others go straight to the posttest. This structure simultaneously controls for pretest sensitization and the logistical constraint that individual randomization is infeasible.	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.