Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Recherche par enquête hiérarchique× | Recherche par enquête longitudinale× | |
|---|---|---|
| Domaine | Conception de la recherche | Conception de la recherche |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1986–1992 (formalization of multilevel methods for nested survey data) | Mid-20th century (formalized ~1950s–1970s) |
| Auteur d'origine≠ | Developed through contributions of Aitkin, Longford, Goldstein, Bryk, and Raudenbush in the 1980s–1990s | Survey methodology tradition; codified in social sciences by scholars including W.S. Robinson (1950) and later Scott Menard |
| Type≠ | Quantitative survey design with multilevel analysis | Quantitative observational research 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 | Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922452 |
| Alias | multilevel survey research, nested survey design, multilevel survey design, HLM-based survey research | longitudinal survey study, repeated-measures survey, prospective survey design, panel survey |
| Apparentées≠ | 6 | 5 |
| Résumé≠ | 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. | Longitudinal survey research collects structured questionnaire data from the same individuals (or units) at two or more points in time. Unlike a one-shot cross-sectional survey, this design captures change, stability, and temporal ordering of variables — enabling researchers to track trajectories, test causal sequences, and distinguish cohort effects from aging effects within a quantitative framework. |
| ScholarGateJeu de données ↗ |
|
|