An Evaluation of Empirical Bayes's Estimation of Value-Added Teacher Performance Measures. (April 2015)

• Record Type: Journal Article
• Authors: Guarino, Cassandra M.; Maxfield, Michelle; Reckase, Mark D.; Thompson, Paul N.; Wooldridge, Jeffrey M.
• Is Part Of: Journal of educational and behavioral statistics. Volume 40:Number 2(2015)
• Page Start: 190
• Page End: 222
• Publication Date: 2015-04
• Subjects: empirical Bayes's estimation -- best linear unbiased predictor -- nonrandom assignment -- shrinkage estimators -- value-added measures; Educational statistics -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 370.2
• DOI: 10.3102/1076998615574771
• Languages: English
• ISSNs: 1076-9986

Abstract

Empirical Bayes's (EB) estimation has become a popular procedure used to calculate teacher value added, often as a way to make imprecise estimates more reliable. In this article, we review the theory of EB estimation and use simulated and real student achievement data to study the ability of EB estimators to properly rank teachers. We compare the performance of EB estimators with that of other widely used value-added estimators under different teacher assignment scenarios. We find that, although EB estimators generally perform well under random assignment (RA) of teachers to classrooms, their performance suffers under nonrandom teacher assignment. Under non-RA, estimators that explicitly (if imperfectly) control for the teacher assignment mechanism perform the best out of all the estimators we examine. We also find that shrinking the estimates, as in EB estimation, does not itself substantially boost performance.