Bandingkan metode

	Desain Solomon Empat Kelompok Teracak Klaster	Uji Coba Terkendali Acak Faktorial
Bidang	Desain Eksperimen	Desain Eksperimen
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	1949 (Solomon design); cluster extension formalized in 1990s	1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization)
Pencetus	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)
Tipe	Experimental design	Experimental trial design
Sumber perintis	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.
Alias	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
Terkait	6	6
Ringkasan	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.