Shrinkage of Value-Added Estimates and Characteristics of Students with Hard-to-Predict Achievement Levels
Working Paper 17
Publisher: Cambridge, MA: Mathematica Policy Research
Apr 12, 2013
This working paper investigates how empirical Bayes shrinkage, an approach commonly used in implementing teacher accountability systems, affects the value-added estimates of teachers of students with hard-to-predict achievement levels, such as students who have low prior achievement and receive free lunch. Teachers of these students tend to have less precise value-added estimates than teachers of other types of students. Shrinkage increases their estimates’ precision and reduces the absolute value of their value-added estimates. However, this paper found shrinkage has no statistically significant effect on the relative probability that teachers of hard-to-predict students receive value-added estimates that fall in the extremes of the value-added distribution and, as a result, receive consequences in the accountability system.