Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Pilotnaturligt experiment× | Pilot Field Experiment× | |
|---|---|---|
| Ämnesområde | Försöksplanering | Försöksplanering |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 2000s–2010s (as formalized practice) | Mid-20th century (systematised 1960s–1990s) |
| Upphovsperson≠ | Combination of natural experiment tradition (Dunning, Angrist, Pischke) and pilot study methodology | Rooted in Campbell & Stanley (1966) experimental design tradition; formalised in clinical and social research through the 20th century |
| Typ≠ | Quasi-experimental feasibility design | Experimental design |
| Ursprungskälla≠ | Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press. ISBN: 9781107017412 | Campbell, D. T., & Stanley, J. C. (1966). Experimental and quasi-experimental designs for research. Rand McNally. ISBN: 978-0395307878 |
| Alias | feasibility natural experiment, preliminary natural experiment, pilot quasi-experiment, exploratory natural experiment | pilot field trial, small-scale field experiment, feasibility field experiment, exploratory field experiment |
| Närliggande≠ | 4 | 3 |
| Sammanfattning≠ | A pilot natural experiment is a small-scale preliminary study that exploits an existing exogenous event or policy variation to test whether a full natural experiment is viable. It preserves the core logic of natural experiments — using real-world discontinuities to approximate causal inference — while explicitly scoping the work to assess data availability, group comparability, effect detectability, and procedural feasibility before committing resources to a larger study. | A pilot field experiment is a small-scale, preliminary version of a planned full field experiment conducted in a naturalistic setting. It tests whether the intervention, randomisation procedure, measurement instruments, and logistical protocols are feasible before committing to a full-scale study. Results inform sample size calculations, refine treatment protocols, and identify procedural risks — saving resources and improving the quality of the definitive study. |
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