We investigate whether commonly used value-added estimation strategies produce accurate estimates of teacher effects under a variety of scenarios. We estimate teacher effects in simulated student achievement data sets that mimic plausible types of student grouping and teacher assignment scenarios. We find that no one method accurately captures true teacher effects in all scenarios, and the potential for misclassifying teachers as high- or low-performing can be substantial. A dynamic ordinary least squares estimator is more robust across scenarios than other estimators. Misspecifying dynamic relationships can exacerbate estimation problems.

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