Extended generalized linear latent and mixed model

Eisuke Segawa; Sherry Emery; Susan J. Curry

doi:10.3102/1076998607307359

Extended generalized linear latent and mixed model

Eisuke Segawa^*, Sherry Emery, Susan J. Curry

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

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.

Original language	English (US)
Pages (from-to)	464-484
Number of pages	21
Journal	Journal of Educational and Behavioral Statistics
Volume	33
Issue number	4
DOIs	https://doi.org/10.3102/1076998607307359
State	Published - Dec 2008
Externally published	Yes

Keywords

Bayesian statistics
GLLAMM
Latent variables
MCMC
Multilevel models

ASJC Scopus subject areas

Education
Social Sciences (miscellaneous)

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10.3102/1076998607307359

Cite this

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title = "Extended generalized linear latent and mixed model",

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.",

keywords = "Bayesian statistics, GLLAMM, Latent variables, MCMC, Multilevel models",

author = "Eisuke Segawa and Sherry Emery and Curry, {Susan J.}",

note = "Funding Information: This study was funded by The Robert Wood Johnson Foundation (Grant 04-6472). Support was also provided by the National Cancer Institute and the Centers for Disease Control and Prevention through the U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute (Contract 233-02-0087).",

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AU - Segawa, Eisuke

AU - Emery, Sherry

AU - Curry, Susan J.

N1 - Funding Information: This study was funded by The Robert Wood Johnson Foundation (Grant 04-6472). Support was also provided by the National Cancer Institute and the Centers for Disease Control and Prevention through the U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute (Contract 233-02-0087).

PY - 2008/12

Y1 - 2008/12

N2 - 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.

AB - 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.

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KW - GLLAMM

KW - Latent variables

KW - MCMC

KW - Multilevel models

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JF - Journal of Educational and Behavioral Statistics

IS - 4

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Extended generalized linear latent and mixed model

Abstract

Keywords

ASJC Scopus subject areas

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