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Value-added models have been widely used to assess the contributions ofindividual teachers and schools to students' academic growth based onlongitudinal student achievement outcomes. There is concern, however, thatignoring the presence of missing values, which are common in longitudinalstudies, can bias teachers' value-added scores. In this article, a flexiblecorrelated random effects model is developed that jointly models the studentresponses and the student missing data indicators. Both the student responsesand the missing data mechanism depend on latent teacher effects as well aslatent student effects, and the correlation between the sets of random effectsadjusts teachers' value-added scores for informative missing data. The methodsare illustrated with data from calculus classes at a large public universityand with data from an elementary school district.

A Correlated Random Effects Model for Nonignorable Missing Data in Value-Added Assessment of Teacher Effects