Сравнение методов

	Многоуровневая удобная выборка	Кластерная выборка
Область	Методология опросов	Методология опросов
Семейство	Process / pipeline	Process / pipeline
Год появления	1980s–1990s (concurrent with multilevel modeling development)	Early-to-mid 20th century; canonical treatment 1953/1977
Автор метода	Emerged from multilevel/hierarchical research traditions	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice
Тип	Non-probability sampling design	Probability sampling design
Основополагающий источник	Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications (2nd ed.). Routledge. ISBN: 978-1848728462	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407
Другие названия	hierarchical convenience sampling, nested convenience sampling, multilevel accessibility sampling, multi-tier convenience sampling	cluster random sampling, area sampling, one-stage cluster sampling
Связанные	5	5
Сводка	Multi-level convenience sampling is a non-probability approach in which units are selected by convenience at each of two or more nested levels of a hierarchy — for example, recruiting whatever schools agree to participate and then enrolling all available students within those schools. It is widely used in organizational, educational, and health research where the researcher has limited control over access but must respect the nested structure of the population.	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.