Latent Regression in Loglinear Rasch Models

Abstract This article presents a framework for the use of latent variables as outcomes in regression analysis. Based on loglinear Rasch models where item parameters are known or estimated using conditional maximum likelihood a simple and fast estimation algorithm is proposed. The interpretation of regression parameters in the presence of random effects is discussed. A regression model based on a loglinear Rasch model is used to model general and specific group differences in an occupational health study.

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