Сравнение методов

	Многоуровневая взвешенная выборка	Стратифицированная выборка
Область	Методология опросов	Методология опросов
Семейство	Process / pipeline	Process / pipeline
Год появления	1960s–1980s (developed alongside large-scale survey programs)	1977
Автор метода	Leslie Kish (probability sampling theory); complex survey methodologists	William G. Cochran
Тип	Probability sampling design	Probability-based survey sampling design
Основополагающий источник	Kish, L. (1965). Survey Sampling. John Wiley & Sons. New York. ISBN: 978-0471109495	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7
Другие названия	hierarchical weighted sampling, nested weighted sampling, multilevel probability weighting, weighted hierarchical sampling	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
Связанные	6	2
Сводка	Multi-level weighted sampling is a probability-based survey design that draws samples from hierarchically nested populations — such as students within classrooms within schools within districts — and assigns design weights at each level to account for unequal selection probabilities. The resulting weighted data enable unbiased population-level inference despite the complex, non-proportional structure of the sampling frame. It is the backbone of major international assessments such as PISA and TIMSS.	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.