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Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Modélisation Linéaire Hiérarchique (HLM / Modélisation Multiniveaux)× | Analyse de variance à un facteur× | |
|---|---|---|
| Domaine | Statistique | Statistique |
| Famille | Hypothesis test | Hypothesis test |
| Année d'origine≠ | 1986 | 1925 |
| Auteur d'origine≠ | Raudenbush & Bryk (popularized); Goldstein (parallel development) | Ronald A. Fisher |
| Type≠ | Parametric nested-data regression | Parametric mean comparison |
| Source fondatrice≠ | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd. link ↗ |
| Alias≠ | HLM, MLM, multilevel modeling, multilevel analysis | one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA |
| Apparentées | 4 | 4 |
| Résumé≠ | Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels. | One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925. |
| ScholarGateJeu de données ↗ |
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