Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzlīmeņu ērtības izlase× | Daudzlīmeņu kopu paraugu izlase× | |
|---|---|---|
| Nozare | Aptauju metodoloģija | Aptauju metodoloģija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1980s–1990s (concurrent with multilevel modeling development) | 1950s-1970s (cluster sampling); multilevel extension formalized 1980s-1990s |
| Autors≠ | Emerged from multilevel/hierarchical research traditions | W. G. Cochran (cluster sampling foundations); extended into multilevel contexts by survey methodologists |
| Tips≠ | Non-probability sampling design | Probability sampling design |
| Pirmavots≠ | 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 |
| Citi nosaukumi | hierarchical convenience sampling, nested convenience sampling, multilevel accessibility sampling, multi-tier convenience sampling | hierarchical cluster sampling, nested cluster sampling, multi-stage cluster sampling, clustered multilevel sampling |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | 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. | 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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