Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Recherche hiérarchique transversale× | Échantillonnage en grappes× | |
|---|---|---|
| Domaine≠ | Conception de la recherche | Méthodologie d'enquête |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1980s–1990s (formalized with HLM software and methodology) | Early-to-mid 20th century; canonical treatment 1953/1977 |
| Auteur d'origine≠ | Raudenbush & Bryk; Goldstein; Snijders & Bosker (multilevel modeling tradition) | Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice |
| Type≠ | Quantitative observational design | Probability sampling design |
| Source fondatrice≠ | 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 |
| Alias≠ | multilevel cross-sectional design, nested cross-sectional study, clustered cross-sectional research, HCS design | cluster random sampling, area sampling, one-stage cluster sampling |
| Apparentées≠ | 2 | 5 |
| Résumé≠ | Hierarchical cross-sectional research is a quantitative observational design that collects data from individuals nested within higher-level units — such as students within schools, patients within hospitals, or employees within organizations — at a single point in time. By accounting for the non-independence of clustered observations through multilevel modeling, it enables researchers to simultaneously examine individual-level and group-level predictors of an outcome without violating 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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