Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Кластерне рандомізоване польове експериментальне дослідження× | Польовий експеримент× | |
|---|---|---|
| Галузь | Планування експерименту | Планування експерименту |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1980s–1990s (formalized methodology) | 1920s–1930s (agriculture); 1990s–2000s (social sciences) |
| Автор методу≠ | 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) |
| Тип≠ | Randomized experimental design | Experimental design |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | 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 |
| Пов'язані≠ | 4 | 5 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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