So sánh phương pháp

	Thiết kế Solomon bốn nhóm chéo	Thiết kế thực nghiệm Crossover Pretest-Posttest
Lĩnh vực	Thiết kế thí nghiệm	Thiết kế thí nghiệm
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1949 (base design); crossover adaptation developed through later methodological literature	1963 (Campbell & Stanley framework); crossover methodology formalized 1980s–2000s
Người khởi xướng	Richard L. Solomon (base design); crossover extension via repeated-measures methodology	Donald T. Campbell & Julian C. Stanley (pretest-posttest framework); Stephen Senn (crossover trial methodology)
Loại	Experimental design (pretest-sensitization control + within-subjects crossover)	Within-subjects experimental design
Công trình gốc	Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46(2), 137–150.	Senn, S. (2002). Cross-over Trials in Clinical Research (2nd ed.). Wiley. ISBN: 978-0471496533
Tên gọi khác	crossover S4G design, within-subjects Solomon design, repeated-measures Solomon four-group design	within-subjects pretest-posttest design, repeated-measures crossover design, AB/BA pretest-posttest design, crossover repeated-measures design
Liên quan	5	5
Tóm tắt	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.	A crossover pretest-posttest experimental design is a within-subjects experiment in which each participant receives two or more treatments in a randomized sequence, with outcome measurements taken both before and after each treatment period. By serving as their own control across conditions, participants allow direct intra-individual comparison, dramatically increasing statistical power while reducing the sample size required relative to a parallel-group design.