方法对比
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| 多层次中介分析× | 分层线性模型 (HLM / 多层模型)× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Hypothesis test | Hypothesis test |
| 起源年份≠ | 2003 | 1986 |
| 提出者≠ | Kenny, Korchmaros & Bolger | Raudenbush & Bryk (popularized); Goldstein (parallel development) |
| 类型≠ | Multilevel structural model | Parametric nested-data regression |
| 开创性文献≠ | 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 |
| 别名≠ | multilevel mediation, hierarchical mediation, cross-level mediation, 1-1-1 mediation | HLM, MLM, multilevel modeling, multilevel analysis |
| 相关≠ | 8 | 4 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
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