Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Hierarhiskā aptaujas pētniecība× | Klasteru izlase× | |
|---|---|---|
| Nozare≠ | Pētījuma dizains | Aptauju metodoloģija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1986–1992 (formalization of multilevel methods for nested survey data) | Early-to-mid 20th century; canonical treatment 1953/1977 |
| Autors≠ | Developed through contributions of Aitkin, Longford, Goldstein, Bryk, and Raudenbush in the 1980s–1990s | Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice |
| Tips≠ | Quantitative survey design with multilevel analysis | Probability sampling design |
| Pirmavots≠ | Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed.). Sage. ISBN: 978-1849202015 | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407 |
| Citi nosaukumi≠ | multilevel survey research, nested survey design, multilevel survey design, HLM-based survey research | cluster random sampling, area sampling, one-stage cluster sampling |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | Hierarchical survey research is a quantitative design that collects survey data from respondents who are naturally nested within higher-level units — such as students within classrooms, employees within organizations, or patients within hospitals — and uses multilevel (hierarchical linear) modeling to analyze variation at each level simultaneously. It is the standard approach whenever survey data have a clustered structure that would violate the independence assumption of ordinary regression. | 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. |
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