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

Abstract

Value-added models have been widely used to assess the contributions of individual teachers and schools to students' academic growth based on longitudinal student achievement outcomes. There is concern, however, that ignoring the presence of missing values, which are common in longitudinal studies, can bias teachers' value-added scores. In this article, a flexible correlated random effects model is developed that jointly models the student responses and the student missing data indicators. Both the student responses and the missing data mechanism depend on latent teacher effects as well as latent student effects, and the correlation between the sets of random effects adjusts teachers' value-added scores for informative missing data. The methods are illustrated with data from calculus classes at a large public university and with data from an elementary school district.

References in corpus (3)

• Missing data in value-added modeling of teacher effects
• Efficient Maximum Likelihood Estimation of Multiple Membership Linear Mixed Models, with an Application to Educational Value-Added Assessments
• Computation of Maximum Likelihood Estimates for Multiresponse Generalized Linear Mixed Models with Non-nested, Correlated Random Effects

Cited by in corpus (1)

• Computation of Maximum Likelihood Estimates for Multiresponse Generalized Linear Mixed Models with Non-nested, Correlated Random Effects