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Latent Covariates in Generalized Linear Models: A Rasch Model Approach

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Advances in Statistical Methods for the Health Sciences

Part of the book series: Statistics for Industry and Technology ((SIT))

Abstract

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.

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Author information

Authors and Affiliations

  1. National Institute of Occupational Health, Denmark

    Karl Bang Christensen

  2. University of Copenhagen, Copenhagen, Denmark

    Karl Bang Christensen

Authors
  1. Karl Bang Christensen
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Editor information

Editors and Affiliations

  1. UFR of Pharmaceutical Sciences, 1 rue Gaston Veil, 44035, Nantes Cedex 1, France

    Jean-Louis Auget

  2. Department of Mathematics and Statistics, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S 4K1, Canada

    N. Balakrishnan

  3. Laboratoire de Statistique Théorie et Appliquée, Université Pierre et Marie Curie, 175 rue de Chevaleret, 75013, Paris, France

    Mounir Mesbah

  4. Center for Statistics, Hasselt University, Agoralaan-Building D, 3590, Diepenbeek, Belgium

    Geert Molenberghs

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Christensen, K.B. (2007). Latent Covariates in Generalized Linear Models: A Rasch Model Approach. In: Auget, JL., Balakrishnan, N., Mesbah, M., Molenberghs, G. (eds) Advances in Statistical Methods for the Health Sciences. Statistics for Industry and Technology. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4542-7_6

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