So sánh phương pháp

	Thiết kế thực nghiệm tiền kiểm-hậu kiểm thực dụng	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	1963 (pre-post design); 1967 (pragmatic distinction)	1963 (Campbell & Stanley framework); crossover methodology formalized 1980s–2000s
Người khởi xướng	Schwartz & Lellouch (pragmatic framing); Campbell & Stanley (pre-post design)	Donald T. Campbell & Julian C. Stanley (pretest-posttest framework); Stephen Senn (crossover trial methodology)
Loại	Experimental / quasi-experimental design	Within-subjects experimental design
Công trình gốc	Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637-648.	Senn, S. (2002). Cross-over Trials in Clinical Research (2nd ed.). Wiley. ISBN: 978-0471496533
Tên gọi khác	pragmatic pre-post design, real-world pretest-posttest study, effectiveness pre-post design, pragmatic before-after study	within-subjects pretest-posttest design, repeated-measures crossover design, AB/BA pretest-posttest design, crossover repeated-measures design
Liên quan	5	5
Tóm tắt	A pragmatic pretest-posttest experimental design combines the before-after measurement structure of the classic pre-post design with the real-world, high-external-validity ethos of pragmatic research. Participants are assessed on relevant outcomes before an intervention is delivered in routine or naturalistic conditions, then re-assessed afterward. The goal is to estimate the effectiveness of the intervention as it actually works in practice rather than under ideal, tightly controlled efficacy conditions.	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.