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

Mariesa Herrmann, Elias Walsh, Eric Isenberg and Alexandra Resch

Mathematica Policy Research Reports from Mathematica Policy Research

Abstract: 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.

Keywords: Value-Added; Estimates; Students; Achievement; Working Paper 17 (search for similar items in EconPapers)
Pages: 25
Date: 2013-04-12
New Economics Papers: this item is included in nep-ure
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Citations: View citations in EconPapers (8)

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