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

Published online by Cambridge University Press:  01 January 2025

Michela Battauz  and
Ruggero Bellio
Michela Battauz*
Affiliation:
Department of Statistics, University of Udine
Ruggero Bellio
Affiliation:
Department of Statistics, University of Udine
*
Requests for reprints should be sent to Michela Battauz, Department of Statistics, University of Udine, Via Treppo 18, 33100 Udine, Italy. E-mail: battauz@dss.uniud.it
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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.

Keywords

heteroscedastic errorIRTmaximum likelihood estimationmeasurement errorRasch modelstructural model
Type
Original Paper
Information
Psychometrika , Volume 76 , Issue 1 , January 2011 , pp. 40 - 56
Copyright
Copyright © 2010 The Psychometric Society

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