Shrinkage of Value-Added Estimates and Characteristics of Students with Hard-to-Predict Achievement Levels

Abstract

It is common in the implementation of teacher accountability systems to use empirical Bayes shrinkage to adjust teacher value-added estimates by their level of precision. Because value-added estimates based on fewer students and students with “hard-to-predict” achievement will be less precise, the procedure could have differential impacts on the probability that the teachers of fewer students or students with hard-to-predict achievement will be assigned consequences. This article investigates how shrinkage affects the value-added estimates of teachers of hard-to-predict students. We found that teachers of students with low prior achievement and who receive free lunch tend to have less precise value-added estimates. However, in our sample, shrinkage had no statistically significant effect on the relative probability that teachers of hard-to-predict students received consequences.