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| Адаптивни ABAB дизајн× | Analiza prekinutih vremenskih serija (ITS)× | |
|---|---|---|
| Oblast≠ | Eksperimentalni dizajn | Kauzalno zaključivanje |
| Porodica≠ | Process / pipeline | Regression model |
| Godina nastanka≠ | 1984 (foundational ABAB); adaptive extensions ~2000s–2010s | 2002 |
| Tvorac≠ | Extended from Barlow & Hersen's ABAB reversal tradition; adaptive rules formalized in behavioral and clinical single-subject research (late 20th–early 21st century) | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| Tip≠ | Single-subject experimental design | Quasi-experimental segmented regression |
| Temeljni izvor≠ | Barlow, D. H., & Hersen, M. (1984). Single Case Experimental Designs: Strategies for Studying Behavior Change (2nd ed.). Pergamon Press. ISBN: 978-0205143641 | 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 ↗ |
| Drugi nazivi≠ | adaptive reversal design, adaptive single-subject ABAB, ABAB with adaptive phase-change rules, dynamic ABAB design | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| Srodne≠ | 2 | 5 |
| Sažetak≠ | The Adaptive ABAB Design is a single-subject experimental methodology that extends the classic ABAB reversal design by incorporating data-driven, prospective decision rules to determine when to transition between baseline (A) and intervention (B) phases. Rather than fixing phase lengths in advance, the researcher uses pre-specified criteria — such as stability thresholds, slope targets, or effect-size benchmarks — to guide each phase change, improving both experimental control and clinical responsiveness. | 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. |
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