Shrinkage of Value-Added Estimates and Characteristics of Students with Hard-to-Predict Achievement Levels
AbstractThis 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 estimatesâ€™ 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.
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Bibliographic InfoPaper provided by Mathematica Policy Research in its series Mathematica Policy Research Reports with number 7748.
Date of creation: 12 Apr 2013
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Value-Added; Estimates; Students; Achievement;
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- I - Health, Education, and Welfare
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