Extended generalized linear latent and mixed model

Eisuke Segawa*, Sherry Emery, Susan J. Curry

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 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 languageEnglish (US)
Pages (from-to)464-484
Number of pages21
JournalJournal of Educational and Behavioral Statistics
Volume33
Issue number4
DOIs
StatePublished - Dec 2008

Keywords

  • Bayesian statistics
  • GLLAMM
  • Latent variables
  • MCMC
  • Multilevel models

ASJC Scopus subject areas

  • Education
  • Social Sciences (miscellaneous)

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