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| 整群随机对照试验× | 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≠ | 1978–1980s | 1992 |
| 提出者≠ | Cornfield (1978); systematised by Donner and colleagues (1980s) | Anthony Bryk and Stephen Raudenbush |
| 类型≠ | Experimental design | Method |
| 开创性文献≠ | Donner, A., & Klar, N. (2000). Design and Analysis of Cluster Randomization Trials in Health Research. Arnold. ISBN: 978-0340652978 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| 别名 | cluster RCT, group-randomized trial, community randomized trial, cluster-randomized experiment | HLM, mixed-effects models, random effects models, MLM |
| 相关≠ | 4 | 3 |
| 摘要≠ | A cluster randomized controlled trial (cluster RCT) is an experimental design in which intact social or organisational groups — such as schools, clinics, villages, or workplaces — are randomly assigned to treatment conditions rather than individual participants. Outcomes are still measured at the individual level, but the unit of randomization is the cluster. This design is essential when an intervention is delivered to whole groups, when there is a risk of contamination between participants in the same setting, or when individual randomization is logistically or ethically impractical. | 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. |
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