Generalized Linear Latent Variable Modeling for Mutli-Group Studies

Latent variable modeling is commonly used in behavioral, social, and medical science research. The models used in such analysis relate all observed variables to latent common factors. In many applications, the observations are highly non-normal or discrete, e.g., polytomous responses or counts. The existing approaches for non-normal observations are applicable only for polytomous outcomes, and use models unsuitable for multi-group analysis. We propose a new generalized linear model approach for latent variable analysis that can handle a broad class of non-normal and discrete observations, and that furnishes meaningful interpretation and inference in multi-group studies through maximum likelihood analysis. AMon te Carlo EM algorithm is proposed for parameter estimation. The convergence assessment and standard error estimation are addressed. An application of this new approach in a substance abuse prevention study is presented.

By: Jens C. Eickhoff, Yasuo Amemiya

Published in: RC22981 in 2003


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