Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Experimento de Campo con Ciego Simple× | Experimento de Campo× | |
|---|---|---|
| Campo | Diseño experimental | Diseño experimental |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | Mid-20th century (blinding conventions formalised 1940s–1960s) | 1920s–1930s (agriculture); 1990s–2000s (social sciences) |
| Autor original≠ | Established practice in experimental social science and clinical research; codified by Campbell & Stanley (1963) and Shadish, Cook & Campbell (2002) | Formalized by R. A. Fisher (1935); systematized in social sciences by Harrison & List (2004) |
| Tipo≠ | Controlled field experiment with partial blinding | Experimental design |
| Fuente seminal≠ | Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin. ISBN: 978-0395615560 | Harrison, G. W., & List, J. A. (2004). Field experiments. Journal of Economic Literature, 42(4), 1009–1055. DOI ↗ |
| Alias≠ | single-masked field experiment, field experiment with single blinding, single-blind natural-setting trial | field trial, natural field experiment, randomized field experiment, field RCT |
| Relacionados≠ | 6 | 5 |
| Resumen≠ | A single-blind field experiment combines real-world experimental conditions with partial blinding: either participants or outcome assessors — but not both — are kept unaware of treatment assignment. This design reduces demand characteristics or observer bias while preserving ecological validity, making it a practical middle ground when full double-blinding is logistically infeasible in naturalistic settings. | 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. |
| ScholarGateConjunto de datos ↗ |
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