方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 实用多基线设计× | 中断时间序列（ITS）分析× | |
|---|---|---|
| 领域≠ | 实验设计 | 因果推断 |
| 方法族≠ | Process / pipeline | Regression model |
| 起源年份≠ | 1968 (original MBD); pragmatic adaptation formalized in 2000s–2010s | 2002 |
| 提出者≠ | Adapted from Baer, Wolf & Risley (1968); pragmatic variant developed within single-case methodology community | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| 类型≠ | Single-case experimental design variant | Quasi-experimental segmented regression |
| 开创性文献≠ | 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 ↗ | Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355. DOI ↗ |
| 别名≠ | PMBD, pragmatic MBD, real-world multiple baseline design, flexible multiple baseline design | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| 相关≠ | 3 | 5 |
| 摘要≠ | The Pragmatic Multiple Baseline Design is a single-case experimental design that staggers intervention introduction across multiple participants, settings, or behaviors in real-world conditions where strict experimental control is impractical. By relaxing some idealized constraints — such as perfectly stable baselines or rigid staggering timelines — it preserves the core logic of the multiple baseline while accommodating clinical, educational, or community realities. It is especially valued when withholding treatment for ethical reasons is untenable and when practitioners need evidence from naturalistic settings. | Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope. |
| ScholarGate数据集 ↗ |
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