Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Adaptiivne üksikjuhtumiga katseplaneering× | Katkendliku ajasarja (ITS) analüüs× | |
|---|---|---|
| Valdkond≠ | Katsedisain | Põhjuslik järeldamine |
| Perekond≠ | Process / pipeline | Regression model |
| Tekkeaasta≠ | Classical SSED: 1960s–1970s; adaptive extensions formalised: 2000s–2010s | 2002 |
| Looja≠ | Evolved from classical single-case designs (Skinner, Sidman); adaptive features formalised in clinical N-of-1 literature (Zucker, Schmid, Nikles et al.) | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| Tüüp≠ | Experimental single-subject design with adaptive decision rules | Quasi-experimental segmented regression |
| Algallikas≠ | Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press. ISBN: 978-0195341881 | 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 ↗ |
| Rööpnimetused≠ | Adaptive SSED, Adaptive N-of-1 design, Adaptive single-case experimental design, Adaptive SCE design | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| Seotud≠ | 4 | 5 |
| Kokkuvõte≠ | Adaptive single-subject experimental design (adaptive SSED) is an experimental methodology in which a single participant or unit is repeatedly observed under systematically alternated conditions — baseline and intervention — while pre-specified decision rules allow the researcher or clinician to modify treatment parameters, phase lengths, or condition sequences in response to continuously collected data. It merges the internal validity of classical single-case experimental designs with the flexibility of adaptive trial logic, making it especially valuable in clinical, behavioral, and applied settings where individual response trajectories vary substantially. | 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. |
| ScholarGateAndmestik ↗ |
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