Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Natural experiment creuament× | Natural Experiment× | |
|---|---|---|
| Camp | Disseny experimental | Disseny experimental |
| Família | Process / pipeline | Process / pipeline |
| Any d'origen≠ | Crossover designs: mid-20th century; applied to natural experiments: 1990s–2000s | 1990s (formal methodological articulation); earlier in epidemiology (John Snow, 1854) |
| Autor original≠ | Drawn from crossover trial methods (Jones & Kenward) and natural experiment tradition (Mill, 1843; Dunning, 2012) | Varied; systematized in econometrics and political science (e.g., Meyer 1995; Angrist & Krueger 1991) |
| Tipus≠ | Quasi-experimental design | Quasi-experimental research design |
| Font seminal≠ | 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 ↗ |
| Àlies≠ | within-unit natural experiment, reversal natural experiment, crossover quasi-experiment | natural quasi-experiment, naturally occurring experiment, exogenous shock design, as-if randomization |
| Relacionats≠ | 5 | 3 |
| Resum≠ | 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. | 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. |
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