Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Адаптивний дизайн множинних базових ліній× | Адаптивний експеримент× | |
|---|---|---|
| Галузь | Планування експерименту | Планування експерименту |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1968 (multiple baseline base); adaptive extensions discussed from ~2000s onward | 1940s–1970s (sequential foundations); formalised in clinical and behavioural research by 1980s–2000s |
| Автор методу≠ | Baer, Wolf & Risley (multiple baseline foundation); adaptive modifications developed within single-case methodology community | Abraham Wald (sequential analysis foundation); expanded by Robbins, Armitage, and others |
| Тип≠ | Single-case experimental design (SCED) | Experimental research design |
| Основоположне джерело≠ | Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97. DOI ↗ | Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC. ISBN: 978-1584886761 |
| Інші назви | adaptive MBD, flexible multiple baseline design, adaptive SCED multiple baseline, data-driven multiple baseline design | adaptive design, response-adaptive randomization, adaptive trial, adaptive randomization |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | The Adaptive Multiple Baseline Design is a single-case experimental design that applies the standard multiple baseline logic — staggering intervention onset across two or more tiers (behaviors, settings, or participants) — but allows phase-change decisions to be guided by ongoing data review rather than fixed, pre-specified schedules. This flexibility makes the design more responsive to participant variability while preserving the core replication logic that supports causal inference. | An adaptive experiment is an experimental design in which pre-specified rules allow the protocol to be modified — such as reallocating participants to better-performing arms, stopping early for efficacy or futility, or changing sample size — based on accumulating interim data, while maintaining statistical validity. Adaptive designs are widely used in clinical trials, behavioural economics, and online platform testing to improve efficiency and ethics without sacrificing inferential rigour. |
| ScholarGateНабір даних ↗ |
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