Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Enquête relationnelle hiérarchique× | Recherche par enquête longitudinale× | |
|---|---|---|
| Domaine | Conception de la recherche | Conception de la recherche |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1980s–2002 (modern HLM-based survey tradition) | Mid-20th century (formalized ~1950s–1970s) |
| Auteur d'origine≠ | Raudenbush & Bryk (multilevel framework); Hox (multilevel survey analysis) | Survey methodology tradition; codified in social sciences by scholars including W.S. Robinson (1950) and later Scott Menard |
| Type≠ | Quantitative survey design with multilevel relational analysis | Quantitative observational research design |
| Source fondatrice≠ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922452 |
| Alias | nested relational survey, multilevel relational survey, HLM-based relational survey, hierarchical correlational survey | longitudinal survey study, repeated-measures survey, prospective survey design, panel survey |
| Apparentées≠ | 4 | 5 |
| Résumé≠ | A hierarchical relational survey combines the correlational goals of relational survey research with a multilevel data structure in which respondents are nested within higher-level units such as classrooms, schools, hospitals, or organizations. The design acknowledges that observations within the same group are not independent, and uses hierarchical linear modeling (HLM) or equivalent multilevel techniques to examine relationships among variables both within and between levels simultaneously. | 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. |
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