Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Многостепенно типично-случаево извадково изследване× | Многостепенно клъстерно случајно извадково изследване× | |
|---|---|---|
| Област | Методология на проучванията | Методология на проучванията |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1990s–2000s | 1950s-1970s (cluster sampling); multilevel extension formalized 1980s-1990s |
| Създател≠ | Draws on Patton (typical case sampling) and multilevel research traditions (Hox, Raudenbush) | W. G. Cochran (cluster sampling foundations); extended into multilevel contexts by survey methodologists |
| Тип≠ | Purposive sampling strategy | Probability sampling design |
| Основополагащ източник≠ | Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications. ISBN: 978-0761919711 | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407 |
| Други названия≠ | multilevel typical case selection, hierarchical typical case sampling, nested typical case sampling | hierarchical cluster sampling, nested cluster sampling, multi-stage cluster sampling, clustered multilevel sampling |
| Свързани | 6 | 6 |
| Резюме≠ | Multi-level typical case sampling is a purposive strategy that selects representative, average-profile units at each level of a hierarchical structure — for example, typical classrooms within typical schools, or typical employees within typical departments. It is used when the research goal is to describe or illustrate the ordinary functioning of a nested phenomenon rather than to capture its extremes or full variation. | 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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