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| Esperimento sul Campo× | Esperimento Naturale× | |
|---|---|---|
| Campo | Disegno sperimentale | Disegno sperimentale |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1920s–1930s (agriculture); 1990s–2000s (social sciences) | 1990s (formal methodological articulation); earlier in epidemiology (John Snow, 1854) |
| Ideatore≠ | Formalized by R. A. Fisher (1935); systematized in social sciences by Harrison & List (2004) | Varied; systematized in econometrics and political science (e.g., Meyer 1995; Angrist & Krueger 1991) |
| Tipo≠ | Experimental design | Quasi-experimental research design |
| Fonte seminale≠ | Harrison, G. W., & List, J. A. (2004). Field experiments. Journal of Economic Literature, 42(4), 1009–1055. DOI ↗ | Meyer, B. D. (1995). Natural and quasi-experiments in economics. Journal of Business and Economic Statistics, 13(2), 151–161. DOI ↗ |
| Alias | field trial, natural field experiment, randomized field experiment, field RCT | natural quasi-experiment, naturally occurring experiment, exogenous shock design, as-if randomization |
| Correlati≠ | 5 | 3 |
| Sintesi≠ | A field experiment applies the logic of a randomized controlled trial in a naturally occurring, real-world environment rather than an artificial laboratory. Participants are randomly assigned to treatment and control conditions while going about everyday activities, allowing researchers to estimate causal effects with high internal validity while preserving a level of ecological realism that laboratory settings cannot offer. The design is especially prominent in economics, public health, political science, and development research. | 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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