Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Utafiti wa sampuli za nguzo za majaribio× | Sampuli ya Kwenye Kundi (Cluster Sampling)× | |
|---|---|---|
| Nyanja | Metodolojia ya Dodoso | Metodolojia ya Dodoso |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | Mid-20th century (cluster sampling foundations); 2000s (pilot study formalization) | Early-to-mid 20th century; canonical treatment 1953/1977 |
| Mwanzilishi≠ | 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; roots in early 20th-century U.S. Census Bureau survey practice |
| Aina≠ | Probability sampling feasibility design | Probability sampling design |
| Chanzo asilia≠ | 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.). Wiley. ISBN: 978-0471162407 |
| Majina mbadala≠ | pilot area sampling, feasibility cluster sample, preliminary cluster survey, pilot cluster survey | cluster random sampling, area sampling, one-stage cluster sampling |
| Zinazohusiana≠ | 4 | 5 |
| Muhtasari≠ | 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. | Cluster sampling is a probability sampling technique in which the population is divided into naturally occurring groups (clusters), a random sample of clusters is selected, and all — or a random subset of — members within each selected cluster are studied. It is especially practical when a complete population list is unavailable or when units are geographically dispersed, making individual random selection prohibitively expensive. One-stage cluster sampling surveys every member of selected clusters; two-stage designs add a second random draw within clusters. |
| ScholarGateSeti ya data ↗ |
|
|