Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Пропорциональная многоступенчатая выборка× | Стратифицированная выборка× | |
|---|---|---|
| Область | Методология опросов | Методология опросов |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1950s–1960s | 1977 |
| Автор метода≠ | Leslie Kish; William G. Cochran (theoretical foundations) | William G. Cochran |
| Тип≠ | Probability sampling design | Probability-based survey sampling design |
| Основополагающий источник≠ | Kish, L. (1965). Survey Sampling. John Wiley & Sons. (Chapters 6–7 on multistage and PPS designs.) ISBN: 978-0471489009 | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7 |
| Другие названия | proportional PPS multistage sampling, multistage probability proportional to size sampling, proportionate multistage cluster sampling, PPS multistage sampling | Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme |
| Связанные≠ | 6 | 2 |
| Сводка≠ | Proportional multistage sampling is a probability sampling design that selects units across two or more hierarchical stages — for example, regions, then districts, then households — where the number of units drawn at each stage is proportional to the size of each higher-level unit. By weighting selection probabilities to match cluster size, it produces self-weighting samples that closely mirror the population structure and simplify variance estimation. | Stratified sampling is a probability sampling design in which the target population is partitioned into non-overlapping, exhaustive subgroups called strata, and independent probability samples are drawn within each stratum. Formalized by William G. Cochran in Sampling Techniques (1977), the method exploits known population structure to reduce variance and guarantee representativeness of all major subgroups, making it a cornerstone of large-scale survey research and official statistics. |
| ScholarGateНабор данных ↗ |
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