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.
Supported in partVerified against the publisher's abstract
The description shows the book covers multilevel models, supporting that such methods exist, but not the grain-choice framing.
“comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models”
- 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
Verification¶
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Registry ID ref:53402513a045 · see in the full table