方法对比

	单盲对照组实验设计	交叉对照组实验设计
领域	实验设计	实验设计
方法族	Process / pipeline	Process / pipeline
起源年份	Mid-20th century (blinding standards consolidated ~1950s–1970s)	Mid-20th century; systematic treatment from 1980s onward
提出者	Classical experimental tradition; blinding formalized in 20th-century clinical trial methodology	Established in clinical pharmacology and agricultural research; formalized by B. Jones & M. G. Kenward
类型	Controlled experimental design	Experimental design
开创性文献	Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin. ISBN: 978-0395615560	Jones, B., & Kenward, M. G. (2003). Design and Analysis of Cross-Over Trials (2nd ed.). Chapman and Hall/CRC. ISBN: 978-1584883500
别名	single-masked controlled experiment, single-blind controlled trial, SB-CGD, single-blind parallel-group design	crossover controlled trial, within-subject crossover with control, AB/BA crossover controlled design, repeated-measures crossover with control arm
相关	6	6
摘要	A single-blind control group experimental design is a controlled experiment in which participants are kept unaware of whether they are receiving the active treatment or a control condition, while researchers and outcome assessors remain unmasked. The design uses a designated control group as the baseline for comparison, allowing causal inference about the treatment effect while limiting participant-driven response biases such as the placebo effect and demand characteristics.	A crossover control group experimental design is an experimental approach in which participants are randomly assigned to sequences of conditions that include both a treatment and a control (no-treatment or placebo) period, with each participant experiencing both the experimental and control conditions in succession. By using each participant as their own control across periods, this design sharply reduces between-subject variability and typically requires fewer participants than parallel group trials to achieve equivalent statistical power.