Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Experiment de Camp Aleatoritzat per Clústers× | Experiment de camp× | |
|---|---|---|
| Camp | Disseny experimental | Disseny experimental |
| Família | Process / pipeline | Process / pipeline |
| Any d'origen≠ | 1980s–1990s (formalized methodology) | 1920s–1930s (agriculture); 1990s–2000s (social sciences) |
| Autor original≠ | David M. Murray (group-randomized trials framework); applied broadly in public health and education research | Formalized by R. A. Fisher (1935); systematized in social sciences by Harrison & List (2004) |
| Tipus≠ | Randomized experimental design | Experimental design |
| Font seminal≠ | Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials. Oxford University Press. ISBN: 978-0195120424 | Harrison, G. W., & List, J. A. (2004). Field experiments. Journal of Economic Literature, 42(4), 1009–1055. DOI ↗ |
| Àlies | CRFE, cluster-randomized trial in the field, group-randomized field experiment, community-randomized field experiment | field trial, natural field experiment, randomized field experiment, field RCT |
| Relacionats≠ | 4 | 5 |
| Resum≠ | A cluster randomized field experiment (CRFE) assigns intact groups — schools, villages, clinics, workplaces — rather than individuals to treatment or control conditions, and the experiment is conducted in real-world settings rather than a laboratory. Randomization at the group level controls for contamination between conditions while preserving the ecological validity of the natural environment. It is the dominant design for evaluating community-level, school-based, or workplace interventions in public health, education policy, and development economics. | 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. |
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