Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Recherche comparative sur données de panel× | Modélisation multiniveau× | |
|---|---|---|
| Domaine≠ | Conception de la recherche | Statistiques de recherche |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1970s–1980s (formal integration of comparative and panel designs) | 1992 |
| Auteur d'origine≠ | Developed across social science disciplines; seminal formalizations by Cheng Hsiao (panel econometrics) and Melvin Kohn (comparative sociology) | Anthony Bryk and Stephen Raudenbush |
| Type≠ | Quantitative longitudinal comparative design | Method |
| Source fondatrice≠ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. ISBN: 978-1107038691 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| Alias | cross-national panel study, comparative longitudinal panel, pooled cross-sectional time-series design, multi-group panel design | HLM, mixed-effects models, random effects models, MLM |
| Apparentées | 3 | 3 |
| Résumé≠ | Comparative panel research tracks the same individuals, organizations, or macro-level units (e.g., countries, regions) across multiple time points while simultaneously comparing findings across two or more distinct groups or contexts. By combining the temporal depth of panel measurement with the analytical leverage of systematic comparison, this design can distinguish change processes that are universal from those that are context-specific — a capability neither pure panel nor single-sample longitudinal designs offer on their own. | 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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