grad-hlm
階層線性模型 (Hierarchical Linear Modeling)
Overview
Hierarchical Linear Modeling (HLM), also called multilevel modeling, accounts for the nested structure of data where lower-level units (e.g., students, employees) are clustered within higher-level units (e.g., schools, firms). By partitioning variance into within-group and between-group components and allowing intercepts and slopes to vary randomly, HLM produces unbiased estimates and correct standard errors.
When to Use
- Data has a hierarchical or nested structure (individuals within groups)
- Intra-class correlation (ICC) is non-trivial (rule of thumb: ICC > 0.05)
- Research questions involve cross-level interactions (group-level moderators of individual-level effects)
- Repeated measures or longitudinal data nested within subjects (growth models)
When NOT to Use
- Data are not nested or clustering is negligible (ICC near zero)
- Number of groups is very small (fewer than 20 Level-2 units)
- Interest is purely in fixed effects with no group-level predictors
- The nesting structure is crossed, not hierarchical (use crossed random effects instead)
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