Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Pilot Cluster Sampling× | Proporcionālā klasteru atlase× | |
|---|---|---|
| Nozare | Aptauju metodoloģija | Aptauju metodoloģija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | Mid-20th century (cluster sampling foundations); 2000s (pilot study formalization) | 1950s–1960s |
| Autors≠ | 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) | Formalized by William G. Cochran and Leslie Kish |
| Tips≠ | Probability sampling feasibility design | Probability sampling design |
| Pirmavots≠ | 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 ↗ | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407 |
| Citi nosaukumi | pilot area sampling, feasibility cluster sample, preliminary cluster survey, pilot cluster survey | PPS cluster sampling, proportional-to-size cluster sampling, size-proportional cluster sampling, probability proportional to size sampling |
| Saistītās≠ | 4 | 6 |
| Kopsavilkums≠ | 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. | Proportional cluster sampling selects naturally occurring groups (clusters) from a population with probability proportional to each cluster's size, so that larger clusters have a higher chance of selection while every individual element retains an equal overall inclusion probability. This design efficiently handles large, geographically dispersed populations and is the backbone of national health, education, and social surveys worldwide. |
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