Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Analiză de Mediere Multilevel× | Modelarea Liniară Ierarhică (HLM / Modelare Multilevel)× | |
|---|---|---|
| Domeniu | Statistică | Statistică |
| Familie | Hypothesis test | Hypothesis test |
| Anul apariției≠ | 2003 | 1986 |
| Autorul original≠ | Kenny, Korchmaros & Bolger | Raudenbush & Bryk (popularized); Goldstein (parallel development) |
| Tip≠ | Multilevel structural model | Parametric nested-data regression |
| Sursa seminală≠ | Kenny, D. A., Korchmaros, J. D., & Bolger, N. (2003). Lower level mediation in multilevel models. Psychological Methods, 8(2), 115–128. DOI ↗ | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 |
| Denumiri alternative≠ | multilevel mediation, hierarchical mediation, cross-level mediation, 1-1-1 mediation | HLM, MLM, multilevel modeling, multilevel analysis |
| Înrudite≠ | 8 | 4 |
| Rezumat≠ | Multilevel mediation analysis is a parametric structural method that estimates indirect (mediated) effects within hierarchically nested data, such as students within schools or employees within organisations. Formalised for lower-level mediation in multilevel models by Kenny, Korchmaros and Bolger (2003), it simultaneously handles individual-level (1-1-1) and group-level (2-2-1 or 2-1-1) mediation pathways in a single coherent framework. | 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. |
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