Shrinkage of Value-Added Estimates and Characteristics of Students with Hard-to-Predict Achievement Levels. Issue 1 (1st January 2016)

• Record Type: Journal Article
• Authors: Herrmann, Mariesa; Walsh, Elias; Isenberg, Eric
• Is Part Of: Statistics and public policy. Volume 3:Issue 1(2016)
• Page Start: 1
• Page End: 10
• Publication Date: 2016-01-01
• Subjects: Bayesian methods -- Education -- Measurement error; Policy sciences -- Methodology -- Periodicals; Social sciences -- Statistical methods -- Periodicals; Medical statistics -- Methodology -- Periodicals; Statistics -- Periodicals; Medical statistics -- Methodology; Policy sciences -- Methodology; Social sciences -- Statistical methods; Statistics; Periodicals; 320.60727
• DOI: 10.1080/2330443X.2016.1182878
• Languages: English
• ISSNs: 2330-443X

Abstract

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.