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| Kísérleti klaszterszintű mintavételezés× | Adaptív klaszterszámbavétel× | |
|---|---|---|
| Tudományterület | Kérdőíves felmérések módszertana | Kérdőíves felmérések módszertana |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | Mid-20th century (cluster sampling foundations); 2000s (pilot study formalization) | 1990 |
| Megalkotó≠ | Rooted in W. G. Cochran's cluster sampling theory (1953) combined with pilot-study methodology formalized by Lancaster, Dodd & Williamson (2004) and Thabane et al. (2010) | Steven K. Thompson |
| Típus≠ | Probability sampling feasibility design | Probability-based adaptive sampling design |
| Alapmű≠ | Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 10(1), 1. DOI ↗ | Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059. DOI ↗ |
| Alternatív nevek | pilot area sampling, feasibility cluster sample, preliminary cluster survey, pilot cluster survey | ACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive sampling |
| Kapcsolódó≠ | 4 | 6 |
| Összefoglaló≠ | Pilot cluster sampling is the application of a cluster sampling protocol on a small, preliminary scale to evaluate the feasibility, logistics, and parameter estimates needed before committing to a full-scale cluster survey. A subset of clusters is randomly selected and fully surveyed, yielding estimates of the intraclass correlation (ICC), design effect, recruitment rates, and operational costs. These findings directly inform the sample size and cluster allocation of the definitive survey. | Adaptive cluster sampling (ACS) is a probability-based design in which an initial random sample of units triggers the inclusion of neighboring units whenever a predefined condition — typically a threshold count of a rare attribute — is satisfied. Developed by Steven K. Thompson in 1990, ACS is especially powerful for estimating the abundance or distribution of rare, spatially clustered populations such as endangered species, disease hotspots, or hard-to-reach social groups. |
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