Latent Covariates in Generalized Linear Models: A Rasch Model Approach
Karl Bang Christensen · Birkhäuser Boston eBooks · 2007
Study of multivariate data in situations where a variable of interest is unobservable (latent) and only measured indirectly is widely applied. Item response models are powerful tools for measurement and have been extended to incorporate latent structure. The (log-linear) Rasch model is a simple item response model where tests of fit and item parameter estimation can take place without assumptions about the distribution of the latent variable. Inclusion of a latent variable as predictor in standard regression models such as logistic or Poisson regression models is discussed, and a study of the relation between psychosocial work environment and absence from work is used to illustrate and motivate the results.