An Evaluation of Empirical Bayes’s Estimation of Value-Added Teacher Performance Measures

Cassandra Guarino, Michelle Maxfield, Mark D. Reckase, Paul N. Thompson and Jeffrey Wooldridge
Additional contact information
Michelle Maxfield: Amazon
Mark D. Reckase: Michigan State University
Paul N. Thompson: Oregon State University

Journal of Educational and Behavioral Statistics, 2015, vol. 40, issue 2, 190-222

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.

Keywords: empirical Bayes’s estimation; best linear unbiased predictor; nonrandom assignment; shrinkage estimators; value-added measures (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (24)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:40:y:2015:i:2:p:190-222

DOI: 10.3102/1076998615574771

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