方法对比
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| 纵向研究× | 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≠ | Late 19th–early 20th century; methodologically codified through the 20th century | 1992 |
| 提出者≠ | No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John Willett | Anthony Bryk and Stephen Raudenbush |
| 类型≠ | Quantitative (or mixed) observational research design | Method |
| 开创性文献≠ | Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922841 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| 别名 | longitudinal study, longitudinal design, prospective longitudinal study, repeated-measures observational study | HLM, mixed-effects models, random effects models, MLM |
| 相关≠ | 4 | 3 |
| 摘要≠ | Longitudinal research is an observational design in which the same participants, groups, or units are measured repeatedly over an extended period. Rather than capturing a single snapshot, it tracks change, stability, and temporal sequencing of variables — making it the primary non-experimental strategy for studying development, growth, decline, and the unfolding of causal processes across time. | 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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