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

• Activity Number: 482
• Type: Invited
• Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
• Sponsor: JEBS-Journal of Educational and Behavioral Statistics
• Author(s): Peter Zygmunt Schochet*+ and Hanley Chiang
• Companies: Mathematica Policy Research and Mathematica Policy Research
• Keywords: Value-added models ; Performance measurement systems ; Student learning gains ; False negative and positive error rates

Abstract

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 a realistic performance measurement system scheme based on hypothesis testing, we develop error rate formulas based on OLS and Empirical Bayes estimators. Empirical results suggest that value-added estimates are likely to be noisy using the amount of data that are typically used in practice. Type I and II 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 and 20 percent, respectively. Lower error rates can be achieved if schools are the performance unit. The results suggest that policymakers must carefully conside