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| 多层重测信度× | 多层模型× | |
|---|---|---|
| 领域≠ | 心理测量学 | 研究统计学 |
| 方法族≠ | Latent structure | Process / pipeline |
| 起源年份≠ | 1979 (ICC foundation); multilevel extension: 1990s–2000s | 1992 |
| 提出者≠ | Shrout & Fleiss (ICC foundation); multilevel extension by Goldstein, Snijders, and others | Anthony Bryk and Stephen Raudenbush |
| 类型≠ | Reliability estimation under hierarchical data | Method |
| 开创性文献≠ | Shrout, P. E. & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428. DOI ↗ | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| 别名 | hierarchical test-retest reliability, multilevel ICC reliability, nested test-retest reliability, ML-TRT reliability | HLM, mixed-effects models, random effects models, MLM |
| 相关≠ | 5 | 3 |
| 摘要≠ | Multilevel test-retest reliability estimates how consistently a measurement instrument produces the same scores across repeated administrations when observations are nested within higher-level units — such as patients within clinics or students within classrooms. It partitions total score variance across levels using intraclass correlation coefficients derived from multilevel models. | 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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