Abstract
ABSTRACTIt 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.
Highlights
Due to the incentives provided by the federal Race to the Top program, districts and states are rapidly implementing new teacher evaluation systems to make high-stakes decisions about tenure, pay, and retention, based in part on statistical measures of teacher effectiveness
The first column displays the results from the studentteacher-level regressions of the squared residuals on student characteristics and provides evidence that some groups of students have significantly larger residuals than others
The hard-to-predict students are those who are eligible for free lunch, receive special education services, and have lower pre-test scores
Summary
Due to the incentives provided by the federal Race to the Top program, districts and states are rapidly implementing new teacher evaluation systems to make high-stakes decisions about tenure, pay, and retention, based in part on statistical measures of teacher effectiveness. Because precision depends on the number of students and the characteristics of the students in a teacher’s class, shrinkage could potentially reduce the probability that teachers of these types of students receive consequences This might be desirable in evaluation systems because differences in teachers’ probabilities of being misclassified could be considered unfair and have deleterious effects on teachers’ incentives to teach classes that include certain groups of students. Our purpose is to examine how shrinkage affects the probability that teachers of hard-to-predict students are classified at the extremes of the value-added distribution of teacher effectiveness, because many evaluation systems use these thresholds to determine consequences
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