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| Multilevel Modeling× | Varianzanalyse (ANOVA)× | |
|---|---|---|
| Fachgebiet | Forschungsstatistik | Forschungsstatistik |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1992 | 1925 |
| Urheber≠ | Anthony Bryk and Stephen Raudenbush | Ronald A. Fisher |
| Typ | Method | Method |
| Wegweisende Quelle≠ | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ | Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd. link ↗ |
| Aliasnamen≠ | HLM, mixed-effects models, random effects models, MLM | ANOVA, F-test |
| Verwandt≠ | 3 | 4 |
| Zusammenfassung≠ | 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. | ANOVA is a parametric statistical method developed by Ronald A. Fisher in 1925 that tests whether means differ significantly across three or more independent groups. By partitioning total variance into between-group and within-group components, ANOVA determines whether observed differences are likely due to treatment effects or random variation, making it fundamental to comparative research across medicine, psychology, agriculture, and engineering. |
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