Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Стратифицированная выборка с непропорциональным распределением× | Многоступенчатая выборка× | |
|---|---|---|
| Область | Методология опросов | Методология опросов |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1934 | 1950s–1960s (formalized in Kish 1965 and Cochran 1977) |
| Автор метода≠ | Jerzy Neyman | Leslie Kish; William G. Cochran |
| Тип | Probability sampling design | Probability sampling design |
| Основополагающий источник≠ | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407 | Kish, L. (1965). Survey Sampling. John Wiley & Sons. ISBN: 978-0471109495 |
| Другие названия | disproportionate stratified sampling, unequal-probability stratified sampling, oversampling stratified design, non-proportional stratified sampling | multistage cluster sampling, multi-stage sampling, nested sampling, hierarchical sampling |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Disproportional stratified sampling divides the population into mutually exclusive strata and deliberately draws different proportions from each stratum — oversampling small or analytically important subgroups and undersampling large ones. Post-hoc weighting restores population-level representativeness when overall estimates are needed. First formalised by Jerzy Neyman in 1934, it is the standard approach when subgroup-level precision matters as much as total-population estimates. | Multistage sampling is a probability-based design that selects a sample by working through two or more successive levels of a population hierarchy — for example, first selecting regions, then districts within those regions, then households within those districts. It makes large-scale surveys practical when a complete population list is unavailable or when the population is geographically dispersed, by concentrating fieldwork within a manageable number of sampled units at each stage. |
| ScholarGateНабор данных ↗ |
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