方法对比
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| 分层验证性研究× | 多层模型× | |
|---|---|---|
| 领域≠ | 研究设计 | 研究统计学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1980s–2000s | 1992 |
| 提出者≠ | Raudenbush & Bryk; Hox; Goldstein | Anthony Bryk and Stephen Raudenbush |
| 类型≠ | Quantitative confirmatory research design | Method |
| 开创性文献≠ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| 别名 | multilevel confirmatory research, nested confirmatory design, hierarchical hypothesis-testing research, HCR | HLM, mixed-effects models, random effects models, MLM |
| 相关≠ | 5 | 3 |
| 摘要≠ | Hierarchical confirmatory research is a quantitative design that tests pre-specified hypotheses about relationships or group differences in data that have a natural nested (hierarchical) structure — such as students clustered within classrooms, patients within hospitals, or employees within organizations. By explicitly modeling the hierarchy, it avoids the inflation of Type I error that occurs when nested data are analyzed as though observations were independent. | 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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