Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Recherche descriptive hiérarchique× | Échantillonnage en grappes× | |
|---|---|---|
| Domaine≠ | Conception de la recherche | Méthodologie d'enquête |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1980s–1990s (multilevel descriptive formalization) | Early-to-mid 20th century; canonical treatment 1953/1977 |
| Auteur d'origine≠ | Formalized within survey and educational research traditions; associated with Hox, Raudenbush, Bryk, and Creswell | Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice |
| Type≠ | Quantitative observational/descriptive design | Probability sampling design |
| Source fondatrice≠ | Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications (2nd ed.). Routledge. ISBN: 978-1848728455 | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407 |
| Alias≠ | multilevel descriptive design, nested descriptive study, hierarchical survey design, stratified descriptive research | cluster random sampling, area sampling, one-stage cluster sampling |
| Apparentées≠ | 4 | 5 |
| Résumé≠ | Hierarchical descriptive research is an observational design that documents the current state of a phenomenon across two or more nested levels — for example, students within classrooms within schools, or employees within teams within organizations. Rather than testing hypotheses or explaining causation, it describes distributions, frequencies, and relationships at each level, making explicit the structured, layered nature of the population being studied. | 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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