Сравнение методов

	Экспериментальный дизайн с блокированием и измерениями до и после вмешательства	Экспериментальный дизайн с контрольной группой
Область	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Process / pipeline
Год появления	1935 (blocking, Fisher); 1963 (pretest-posttest + blocking synthesis, Campbell & Stanley)	1935 (Fisher); 1963 (Campbell & Stanley codification)
Автор метода	Donald T. Campbell & Julian C. Stanley (systematized); blocking technique from Ronald A. Fisher	Ronald A. Fisher; systematised by Donald T. Campbell & Julian C. Stanley
Тип	Experimental design	Experimental research design
Основополагающий источник	Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.	Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.
Другие названия	blocked pre-post design, RBPP design, block-randomized pretest-posttest design, randomized block pre-post control group design	controlled experiment, true experimental design, randomized controlled design, treatment-control design
Связанные	6	4
Сводка	The blocked pretest-posttest experimental design combines blocking — grouping participants into homogeneous strata before randomization — with pre- and post-intervention measurement. Blocking controls for known sources of variability (e.g., baseline ability, gender, site), while the pretest-posttest structure quantifies change scores directly. Together, they reduce error variance and increase statistical power compared to a simple pretest-posttest design, making this approach well suited to educational, clinical, and behavioral intervention studies.	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.