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Structural Modeling of Measurement Error in Generalized Linear Models with Rasch Measures as Covariates

Abstract

This paper proposes a structural analysis for generalized linear models when some explanatory variables are measured with error and the measurement error variance is a function of the true variables. The focus is on latent variables investigated on the basis of questionnaires and estimated using item response theory models. Latent variable estimates are then treated as observed measures of the true variables. This leads to a two-stage estimation procedure which constitutes an alternative to a joint model for the outcome variable and the responses given to the questionnaire. Simulation studies explore the effect of ignoring the true error structure and the performance of the proposed method. Two illustrative examples concern achievement data of university students. Particular attention is given to the Rasch model.

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Correspondence to Michela Battauz.

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Battauz, M., Bellio, R. Structural Modeling of Measurement Error in Generalized Linear Models with Rasch Measures as Covariates. Psychometrika 76, 40–56 (2011). https://doi.org/10.1007/s11336-010-9195-z

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  • DOI: https://doi.org/10.1007/s11336-010-9195-z

Keywords

  • heteroscedastic error
  • IRT
  • maximum likelihood estimation
  • measurement error
  • Rasch model
  • structural model