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| Jednoślepowy eksperyment naturalny× | Eksperyment naturalny× | |
|---|---|---|
| Dziedzina | Planowanie eksperymentów | Planowanie eksperymentów |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | Late 20th century (formalized practice) | 1990s (formal methodological articulation); earlier in epidemiology (John Snow, 1854) |
| Twórca≠ | Conceptual synthesis of natural experiment tradition (Haavelmo, 1944; Campbell & Stanley, 1963) with single-blind methodology | Varied; systematized in econometrics and political science (e.g., Meyer 1995; Angrist & Krueger 1991) |
| Typ≠ | Quasi-experimental design with partial blinding | Quasi-experimental research design |
| Źródło pierwotne≠ | 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 ↗ |
| Inne nazwy≠ | single-masked natural experiment, blinded quasi-experiment, single-blind exogenous assignment study | natural quasi-experiment, naturally occurring experiment, exogenous shock design, as-if randomization |
| Pokrewne≠ | 2 | 3 |
| Podsumowanie≠ | A single-blind natural experiment leverages an exogenous, researcher-uncontrolled event — such as a policy change, lottery, or natural disaster — to create treatment and comparison groups, while applying single-blind procedures so that either the participants or the outcome assessors (but not both) are unaware of group assignment. This design combines the causal leverage of natural variation with reduced measurement bias from blinding. | 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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