Salīdzināt metodes

	Bloķētais Solomona četru grupu dizains	Kontrolgrupas eksperimentālais dizains
Nozare	Eksperimentu plānošana	Eksperimentu plānošana
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1949 (base); blocking extension applied in behavioral and social sciences from mid-20th century onward	1935 (Fisher); 1963 (Campbell & Stanley codification)
Autors	Richard L. Solomon (base design, 1949); blocking integrated from classical experimental design tradition (Fisher, 1935)	Ronald A. Fisher; systematised by Donald T. Campbell & Julian C. Stanley
Tips	Experimental design	Experimental research design
Pirmavots	Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46(2), 137–150.	Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.
Citi nosaukumi	Blocked S4G, randomized blocked Solomon design, Solomon four-group with blocking	controlled experiment, true experimental design, randomized controlled design, treatment-control design
Saistītās	6	4
Kopsavilkums	The blocked Solomon four-group design combines Solomon's classic four-group structure — which disentangles pretest sensitization effects from treatment effects — with blocking on a known nuisance variable. Participants are first grouped into homogeneous blocks (e.g., by baseline ability, gender, or site), then randomly assigned within each block to one of four conditions: pretested treatment, pretested control, unpretested treatment, and unpretested control. This structure simultaneously controls for maturation, pretest reactivity, and block-level variance, making it one of the strongest quasi-controlled experimental frameworks available.	Control group experimental design is a fundamental experimental structure in which participants are assigned to at least two groups — a treatment group that receives the intervention and a control group that does not — so that the effect of the intervention can be isolated by comparing outcomes across groups. Randomisation of assignment strengthens causal inference by balancing known and unknown confounders.