Latent Class Regression on Latent Factors

There are two types of latent variable modeling often used in health sciences research; structural equation modeling with continuous factors, and latent class analysis with unordered-categorical latent segments. This paper develops a statistical methodology for a more general model with both continuous and categorical latent variables. Observed measurement types are allowed to include continuous and ordered categorical responses. Model fitting methods and associated statistical inference procedures are discussed. This methodology can be useful in health science applications, where a health condition outcome variable is a latent classification, and some of possible predictor variables are psychological/behavioral constructs. An example relating a underlying eating disorder condition to a physical appearance satisfaction construct is presented.

By: Yasuo Amemiya; Melanie Wall; Jia Guo

Published in: RC23391 in 2004


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