Data Analysis Using Regression and Multilevel/Hierarchical Models¶
Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Grain of Analysis
- The prime also accommodates multi-level phenomena: many phenomena have structure at several levels simultaneously (cellular/tissue/organ; individual/team/organisation; word/sentence/paragraph/document), so the grain choice becomes a choice of which level to resolve at rather than "the" correct level, and multi-grain methods — hierarchical models, nested-code schemes — exist for exactly this case.
This sourceHierarchical/multilevel models fit structure at several levels simultaneously, the method for phenomena with structure at multiple grains.
- The prime also accommodates multi-level phenomena: many phenomena have structure at several levels simultaneously (cellular/tissue/organ; individual/team/organisation; word/sentence/paragraph/document), so the grain choice becomes a choice of which level to resolve at rather than "the" correct level, and multi-grain methods — hierarchical models, nested-code schemes — exist for exactly this case.
Mechanisms¶
- Common Factor or Random-Effect Model
- Its strength is that it works precisely when separation is impossible after the fact, giving a principled, precision-weighted purge of shared variance; partial pooling
This sourceTreats partial pooling as a compromise between separate and pooled estimates for noisy groups.
- Its strength is that it works precisely when separation is impossible after the fact, giving a principled, precision-weighted purge of shared variance; partial pooling
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Registry ID ref:53402513a045 · see in the full table