Covariate Measurement Error Adjustment for Multilevel Models With Application to Educational Data

This article proposes a multilevel model for the assessment of school effectiveness where the intake achievement is a predictor and the response variable is the achievement in the subsequent periods. The achievement is a latent variable that can be estimated on the basis of an item response theory model and hence subject to measurement error. Ignoring covariate measurement error leads to biased parameter estimates. To address this problem, a likelihood-based measurement error adjustment for multilevel models is proposed. In particular, the method deals with a covariate measured with error that has a random coefficient. An application to educational data from the Italian region of Lombardy illustrates the method.

Manuscript received January 21, 2009 Revision received November 24, 2009 Accepted January 17, 2010