Сравнение методов

	Дизайн Соломона с четырьмя группами и перекрестным назначением	Экспериментальный дизайн с контрольной группой
Область	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Process / pipeline
Год появления	1949 (base design); crossover adaptation developed through later methodological literature	1935 (Fisher); 1963 (Campbell & Stanley codification)
Автор метода	Richard L. Solomon (base design); crossover extension via repeated-measures methodology	Ronald A. Fisher; systematised by Donald T. Campbell & Julian C. Stanley
Тип	Experimental design (pretest-sensitization control + within-subjects crossover)	Experimental research design
Основополагающий источник	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.
Другие названия	crossover S4G design, within-subjects Solomon design, repeated-measures Solomon four-group design	controlled experiment, true experimental design, randomized controlled design, treatment-control design
Связанные	5	4
Сводка	The Crossover Solomon Four-Group Design merges two powerful experimental strategies: the Solomon four-group design's control for pretest sensitization and the crossover design's within-subjects efficiency. Participants are randomly assigned to one of four groups that vary in whether they receive a pretest and in the sequence of treatment and control conditions, allowing the researcher to simultaneously estimate treatment effects, pretest effects, and their interaction while controlling for individual differences through repeated measurement.	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.