Shrinkage of Value-Added Estimates and Characteristics of Students with Hard-to-Predict Achievement Levels (Journal Article)

Publisher: Statistics and Public Policy, vol. 3, issue 1 (published online ahead of print, subscription required)
May 04, 2016
Mariesa Herrmann, Elias Walsh, and Eric Isenberg
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.