Methods for Incorporating Measurement Error in Value-Added Models and Teacher Classifications

Abstract

The position statement on value-added models published by the American Statistical Association (ASA) suggested guidelines and issues to consider when using value-added models as a component of a teacher evaluation system. One suggestion offered is that value-added results should be accompanied by measures of precision. It is important, however, to go beyond simply reporting measures of precision alongside point estimates, but instead to formally include measures of precision into the value-added models and teacher classification systems. This practice will lead to improved inferences and reduced misclassification rates. Therefore, the aim of this article is to offer two suggested approaches for including measures of precision into the value-added model process. The first suggestion is to account for measurement error in student test scores and is motivated by the claim that measurement error is of little concern when the model conditions on at least three test scores. The second suggestion is to directly incorporate standard errors of the point estimates when forming overall classifications regarding teacher effects. This is intended to demonstrate ways in which teacher misclassification rates can be minimized.