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| Многостепенна слоеста извадка× | Многостепенно клъстерно случајно извадково изследване× | |
|---|---|---|
| Област | Методология на проучванията | Методология на проучванията |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1950s–1970s | 1950s-1970s (cluster sampling); multilevel extension formalized 1980s-1990s |
| Създател≠ | Formalized by Leslie Kish and William G. Cochran in the mid-20th century survey sampling literature | W. G. Cochran (cluster sampling foundations); extended into multilevel contexts by survey methodologists |
| Тип | Probability sampling design | Probability sampling design |
| Основополагащ източник≠ | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407 | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407 |
| Други названия | hierarchical stratified sampling, nested stratified sampling, multilevel stratified design, stratified multilevel sampling | hierarchical cluster sampling, nested cluster sampling, multi-stage cluster sampling, clustered multilevel sampling |
| Свързани | 6 | 6 |
| Резюме≠ | Multi-level stratified sampling applies stratification at two or more hierarchical levels of a nested population structure — for example, first stratifying geographic regions, then stratifying schools within each region, then stratifying classrooms within each school. This layered control over the composition of the sample at every level reduces variance and supports analysis at each level of the hierarchy, making it a powerful design for large-scale educational, epidemiological, and organizational surveys. | Multi-level cluster sampling is a probability sampling design for hierarchically structured populations — such as students nested within classrooms within schools within districts. Clusters are randomly selected at each level of the hierarchy before individual units are sampled within the final-level clusters. The design mirrors the natural nesting of real-world populations and enables efficient large-scale data collection while supporting multilevel statistical analysis. |
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