方法证据记录
Multilevel Modeling
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Multilevel (Hierarchical) Linear Modeling
分类方法记录 · process-pipeline / research-statistics
- Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. · DOI 10.2307/2075823
- Goldstein, H. (2011). Multilevel Statistical Models (4th ed.). Wiley-Blackwell. · DOI 10.1002/9780470973394
- Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428. · DOI 10.1037/0033-2909.86.2.420
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