Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Ristkasutav looduslik eksperiment× | Katkendliku ajasarja (ITS) analüüs× | |
|---|---|---|
| Valdkond≠ | Katsedisain | Põhjuslik järeldamine |
| Perekond≠ | Process / pipeline | Regression model |
| Tekkeaasta≠ | Crossover designs: mid-20th century; applied to natural experiments: 1990s–2000s | 2002 |
| Looja≠ | Drawn from crossover trial methods (Jones & Kenward) and natural experiment tradition (Mill, 1843; Dunning, 2012) | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| Tüüp≠ | Quasi-experimental design | Quasi-experimental segmented regression |
| Algallikas≠ | Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press. ISBN: 978-1107698000 | 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 | within-unit natural experiment, reversal natural experiment, crossover quasi-experiment | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | A crossover natural experiment exploits an externally imposed condition — a policy change, law, or environmental event — that exposes the same units (individuals, regions, firms) to both treatment and control states at different times. By observing each unit in multiple conditions, researchers use within-unit variation to estimate causal effects without researcher-controlled randomization, combining the internal validity advantage of crossover designs with the real-world relevance of natural experiments. | 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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