Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Enquête relationnelle hiérarchique× | Modélisation multiniveau× | |
|---|---|---|
| Domaine≠ | Conception de la recherche | Statistiques de recherche |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1980s–2002 (modern HLM-based survey tradition) | 1992 |
| Auteur d'origine≠ | Raudenbush & Bryk (multilevel framework); Hox (multilevel survey analysis) | Anthony Bryk and Stephen Raudenbush |
| Type≠ | Quantitative survey design with multilevel relational analysis | Method |
| Source fondatrice≠ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| Alias | nested relational survey, multilevel relational survey, HLM-based relational survey, hierarchical correlational survey | HLM, mixed-effects models, random effects models, MLM |
| Apparentées≠ | 4 | 3 |
| 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. | Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies. |
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