Journal article
Extended Generalized Linear Latent and Mixed Model
Journal of educational and behavioral statistics, Vol.33(4), pp.464-484
12/01/2008
DOI: 10.3102/1076998607307359
Abstract
The generalized linear latent and mixed modeling (GLLAMM framework) includes many models such as hierarchical and structural equation models. However, GLLAMM cannot currently accommodate some models because it does not allow some parameters to be random, GLLAMM is extended to overcome the limitation by adding a submodel that specifies a distribution of the additional random effects (Extended-GLLAMM). The extension is extremely simple to implement through the Bayesian framework with the WinBUGS software. Our approach is illustrated through the analysis of data from a youth tobacco cessation study.
Details
- Title: Subtitle
- Extended Generalized Linear Latent and Mixed Model
- Creators
- Eisuke Segawa - Medical Social SciencesSherry Emery - University of Illinois at ChicagoSusan J. Curry - University of Illinois at Chicago
- Resource Type
- Journal article
- Publication Details
- Journal of educational and behavioral statistics, Vol.33(4), pp.464-484
- Publisher
- Sage
- DOI
- 10.3102/1076998607307359
- ISSN
- 1076-9986
- eISSN
- 1935-1054
- Number of pages
- 21
- Language
- English
- Date published
- 12/01/2008
- Academic Unit
- Health Management and Policy; Community and Behavioral Health
- Record Identifier
- 9984366372102771
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