Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Адаптивний природний експеримент× | Природний експеримент× | |
|---|---|---|
| Галузь | Планування експерименту | Планування експерименту |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 2000s–2010s (systematic application in policy and social science evaluation) | 1990s (formal methodological articulation); earlier in epidemiology (John Snow, 1854) |
| Автор методу≠ | Synthesizes natural experiment tradition (Meyer 1995; Dunning 2012) with adaptive design principles (Wald 1947; Chow & Chang 2008) | Varied; systematized in econometrics and political science (e.g., Meyer 1995; Angrist & Krueger 1991) |
| Тип≠ | Quasi-experimental adaptive research design | Quasi-experimental research design |
| Основоположне джерело≠ | Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press. ISBN: 978-1107698000 | Meyer, B. D. (1995). Natural and quasi-experiments in economics. Journal of Business and Economic Statistics, 13(2), 151–161. DOI ↗ |
| Інші назви | adaptive quasi-experiment, adaptive exogenous shock design, adaptive as-if randomization, sequential natural experiment | natural quasi-experiment, naturally occurring experiment, exogenous shock design, as-if randomization |
| Пов'язані≠ | 4 | 3 |
| Підсумок≠ | An adaptive natural experiment combines the causal logic of the natural experiment — exploiting real-world events that assign individuals to conditions in a plausibly exogenous way — with pre-specified adaptive monitoring rules that allow the analytic protocol to be modified based on accumulating data. This hybrid design is used in economics, epidemiology, and policy evaluation when the natural event unfolds over time and interim evidence can legitimately inform decisions about data collection scope, subgroup focus, or analytic strategy without compromising causal validity. | A natural experiment exploits a real-world event, policy, or circumstance that assigns individuals to treatment and control conditions in a way that is plausibly random — or at least exogenous to the outcome of interest. Because the researcher does not control assignment, it occupies a middle ground between a true randomized controlled trial and purely observational research, offering stronger causal leverage than conventional observational designs when the as-if randomization assumption holds. |
| ScholarGateНабір даних ↗ |
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