方法对比
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| 整群随机化实验室实验× | 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≠ | 1990s (formalized; cluster randomization principles developed in 1970s-1980s) | 1992 |
| 提出者≠ | David M. Murray (group-randomized trial methodology); built on classical cluster sampling in experimental design | Anthony Bryk and Stephen Raudenbush |
| 类型≠ | Controlled laboratory experiment with cluster-level randomization | Method |
| 开创性文献≠ | Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials. Oxford University Press. ISBN: 978-0195120363 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| 别名 | cluster-randomized lab experiment, group-randomized laboratory study, cluster RCT laboratory variant, clustered lab trial | HLM, mixed-effects models, random effects models, MLM |
| 相关≠ | 6 | 3 |
| 摘要≠ | A cluster randomized laboratory experiment assigns intact groups — such as lab sections, cohorts, or naturally formed teams — rather than individual participants, to experimental conditions. All participants within a cluster receive the same treatment. The design is used when individual randomization would cause contamination between conditions, while retaining the controlled environment of a laboratory setting. | 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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