What Are Error Rates for Classifying Teacher and School Performance Using Value-Added Models?

This article addresses likely error rates for measuring teacher and school performance in the upper elementary grades using value-added models applied to student test score gain data. Using formulas based on ordinary least squares and empirical Bayes estimators, error rates for comparing a teacher’s performance to the average are likely to be about 25 percent with three years of data and 35 percent with one year of data. Corresponding error rates for overall false positive and negative errors are 10 percent and 20 percent, respectively. The results suggest that policymakers must carefully consider likely system error rates when using value-added estimates to make high-stakes decisions regarding educators.