方法对比
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| 自适应重复测量实验设计× | 交叉前后测实验设计× | |
|---|---|---|
| 领域 | 实验设计 | 实验设计 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2000s (integration of adaptive principles with classic pre-post structure) | 1963 (Campbell & Stanley framework); crossover methodology formalized 1980s–2000s |
| 提出者≠ | Synthesizes Campbell & Stanley (1963) pretest-posttest framework with adaptive design methodology formalized by Chow & Chang (2000s) | Donald T. Campbell & Julian C. Stanley (pretest-posttest framework); Stephen Senn (crossover trial methodology) |
| 类型≠ | Experimental design | Within-subjects experimental design |
| 开创性文献≠ | Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally. link ↗ | Senn, S. (2002). Cross-over Trials in Clinical Research (2nd ed.). Wiley. ISBN: 978-0471496533 |
| 别名 | adaptive pre-post design, adaptive pretest-posttest trial, adaptive two-period design, pre-post adaptive experiment | within-subjects pretest-posttest design, repeated-measures crossover design, AB/BA pretest-posttest design, crossover repeated-measures design |
| 相关≠ | 6 | 5 |
| 摘要≠ | An adaptive pretest-posttest experimental design measures all participants before and after an intervention while allowing pre-specified modifications to the trial — such as sample size re-estimation, treatment arm dropping, or randomization ratio adjustment — based on accumulated interim data. It combines the interpretive power of change-score analysis with the efficiency gains and ethical safeguards of adaptive methodology, making it particularly valuable in clinical, educational, and behavioral research where early data can inform better resource allocation. | 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. |
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