方法对比
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| 簇随机全因子实验× | 多层模型× | |
|---|---|---|
| 领域≠ | 实验设计 | 研究统计学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | Late 20th–early 21st century (formalized ~1998–2014) | 1992 |
| 提出者≠ | Synthesis of cluster randomization (Murray, 1998) and factorial design traditions (Fisher, 1935; Collins et al., 2014) | Anthony Bryk and Stephen Raudenbush |
| 类型≠ | Experimental design | Method |
| 开创性文献≠ | Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials. Oxford University Press. ISBN: 978-0195120264 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| 别名 | cluster RCT full factorial, group-randomized full factorial design, CRT full factorial, cluster full factorial trial | HLM, mixed-effects models, random effects models, MLM |
| 相关≠ | 6 | 3 |
| 摘要≠ | A cluster-randomized full factorial experiment assigns intact groups (clusters) rather than individuals to every possible combination of two or more experimental factors. All factor-level combinations are tested simultaneously, enabling estimation of both main effects and all interaction effects, while preserving the integrity of naturally occurring social or organizational units such as schools, clinics, or communities. | 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. |
| ScholarGate数据集 ↗ |
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