Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Lauka klasteru izlase× | Laukos balstīta stratificētā izlase× | |
|---|---|---|
| Nozare | Aptauju metodoloģija | Aptauju metodoloģija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1950s (theory); 1970s–1980s (field survey practice) | 1934 (Neyman's stratified sampling theory); field applications throughout 20th century |
| Autors≠ | William G. Cochran (theoretical foundations); WHO EPI programme (field application) | Jerzy Neyman (stratified sampling theory); applied broadly in field survey practice |
| Tips | Probability sampling design | Probability sampling design |
| Pirmavots≠ | World Health Organization. (1991). Training for mid-level managers: The EPI coverage survey. WHO/EPI/MLM/91.10. World Health Organization. link ↗ | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407 |
| Citi nosaukumi | field cluster sampling, in-field cluster sampling, area cluster sampling (field), field survey cluster design | field stratified sampling, stratified field survey sampling, in-field stratified sampling, field survey stratification |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | Field-based cluster sampling is a probability sampling method in which naturally occurring geographic or administrative groups (clusters) are first randomly selected, and then data are collected in person from units within those clusters. It is the standard design for large-scale field surveys in public health, agriculture, education, and humanitarian response, where compiling a full population list is impractical but clusters such as villages, schools, or census tracts can be identified and physically accessed. | Field-based stratified sampling divides a geographically dispersed or heterogeneous target population into internally homogeneous subgroups (strata) defined by features observable in the field — such as land use type, habitat zone, administrative district, or community category — and then independently draws random samples from each stratum during on-site data collection. The approach combines the precision gains of stratification with the logistical realities of fieldwork, ensuring that every identifiable subgroup of the landscape or community is represented in the final data set. |
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