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Extended Generalized Linear Latent and Mixed Model

^* To whom correspondence should be addressed. E-mail: esegawa{at}uic.edu.

	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.

First published on January 22, 2008, doi:10.3102/1076998607307359

Journal of Educational and Behavioral Statistics 2008;33:464.

A more recent version of this article appeared on December 1, 2008


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