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General Departmental Seminar Series


Generalized Linear Latent Variable Modeling for Multi-Group Studies

Jens Eickhoff, Ph.D., Department of Biostatistics and Medical Informatics, UW-Madison

Friday, March 28, 2003, 12 p.m.

6225 Medical Sciences Center (CSSC), 1300 University Avenue

ABSTRACT

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 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. A Monte Carlo EM algorithm is proposed for parameter estimation. The convergence assessment and standard error estimation are addressed. To validate the benefits of our approach, a simulation study is conducted. An application of this approach in a substance abuse prevention study is also presented.


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