পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| প্রাগমেটিক মাল্টিপল বেসলাইন ডিজাইন× | ব্যাহত সময় সিরিজ (Interrupted Time Series - ITS) বিশ্লেষণ× | |
|---|---|---|
| ক্ষেত্র≠ | পরীক্ষামূলক নকশা | কার্যকারণ অনুমান |
| পরিবার≠ | Process / pipeline | Regression model |
| উদ্ভবের বছর≠ | 1968 (original MBD); pragmatic adaptation formalized in 2000s–2010s | 2002 |
| প্রবর্তক≠ | 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) |
| ধরন≠ | Single-case experimental design variant | Quasi-experimental segmented regression |
| মৌলিক উৎস≠ | 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 ↗ |
| অপর নাম≠ | PMBD, pragmatic MBD, real-world multiple baseline design, flexible multiple baseline design | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| সম্পর্কিত≠ | 3 | 5 |
| সারসংক্ষেপ≠ | 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. |
| ScholarGateডেটাসেট ↗ |
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