Open Access
June 2011 Missing data in value-added modeling of teacher effects
Daniel F. McCaffrey, J. R. Lockwood
Ann. Appl. Stat. 5(2A): 773-797 (June 2011). DOI: 10.1214/10-AOAS405


The increasing availability of longitudinal student achievement data has heightened interest among researchers, educators and policy makers in using these data to evaluate educational inputs, as well as for school and possibly teacher accountability. Researchers have developed elaborate “value-added models” of these longitudinal data to estimate the effects of educational inputs (e.g., teachers or schools) on student achievement while using prior achievement to adjust for nonrandom assignment of students to schools and classes. A challenge to such modeling efforts is the extensive numbers of students with incomplete records and the tendency for those students to be lower achieving. These conditions create the potential for results to be sensitive to violations of the assumption that data are missing at random, which is commonly used when estimating model parameters. The current study extends recent value-added modeling approaches for longitudinal student achievement data Lockwood et al. [J. Educ. Behav. Statist. 32 (2007) 125–150] to allow data to be missing not at random via random effects selection and pattern mixture models, and applies those methods to data from a large urban school district to estimate effects of elementary school mathematics teachers. We find that allowing the data to be missing not at random has little impact on estimated teacher effects. The robustness of estimated teacher effects to the missing data assumptions appears to result from both the relatively small impact of model specification on estimated student effects compared with the large variability in teacher effects and the downweighting of scores from students with incomplete data.


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Daniel F. McCaffrey. J. R. Lockwood. "Missing data in value-added modeling of teacher effects." Ann. Appl. Stat. 5 (2A) 773 - 797, June 2011.


Published: June 2011
First available in Project Euclid: 13 July 2011

zbMATH: 05961691
MathSciNet: MR2840175
Digital Object Identifier: 10.1214/10-AOAS405

Keywords: Data missing not at random , nonignorable missing data , pattern mixture model , random effects , selection models , student achievement

Rights: Copyright © 2011 Institute of Mathematical Statistics

Vol.5 • No. 2A • June 2011
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