TAILIEUCHUNG - Missing Data in Value-Added Modeling of Teacher Effects

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. | RAND EDUCATION CHILDREN AND FAMILIES EDUCATION AND THE ARTS ENERGY AND ENVIRONMENT HEALTH AND HEALTH CARE INFRASTRUCTURE AND TRANSPORTATION INTERNATIONAL AFFAIRS LAW AND BUSINESS NATIONAL SECURITY POPULATION AND AGING PUBLIC SAFETY SCIENCE AND TECHNOLOGY TERRORISM AND HOMELAND SECURITY The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. This electronic document was made available from as a public service of the RAND Corporation. Skip all front matter Jump to Page 1 6 Support RAND Browse Reports Bookstore Make a charitable contribution For More Information Visit RAND at Explore RAND Education View document details Reprints This product is part of the RAND Corporation reprint series. RAND reprints present previously published journal articles book chapters and reports with the permission of the publisher. RAND reprints have been formally reviewed in accordance with the publisher s editorial policy and are compliant with RAND s rigorous quality assurance standards for quality and objectivity. The Annals of Applied Statistics 2011 Vol. 5 No. 2A 773-797 DOI 10-AOAS405 Institute ofMathematical Statistics 2011 MISSING DATA IN VALUE-ADDED MODELING OF TEACHER EFFECTS 1 By Daniel F. McCaffrey and J. R. LOCKWOOD The RAND Corporation 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 . 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

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