The Promise and Peril of Using Value-Added Modeling to Measure Teacher Effectiveness

TRANSPORTATION AND INFRASTRUCTURE The Promise and Peril of Using Value-Added Modeling to Measure Teacher Effectiveness Value-added modeling (VAM), a collection of statistical techniques that uses multiple years of student test score data to estimate the effects of individual schools or teachers, has recently garnered a great deal of attention among both policymakers and researchers. For example, several states—including Tennessee, Pennsylvania and Ohio—are providing at least some of their schools and school districts with feedback about their performance based on VAM, and, in some statehouses, the idea of using VAM results to evaluate and reward administrators and teachers has been discussed. This interest on the part of policymakers reflects the promise of VAM, but many technical issues must be considered in the execution and application of VAM to ensure that policy decisions are based on sound information. Although there have been reviews of particular approaches, no previous reviews carefully compared recent VAM efforts or systematically discussed the wide variety of issues they raise. To address this problem, RAND researchers, funded by the Carnegie Corporation of New York, undertook a systematic review and evaluation of leading approaches to VAM. The goals of this investigation were to • delineate the technical issues raised by the use of VAM for measuring teacher performance • evaluate the practical impact of decisions regarding modeling techniques, variations in the quality of the data used in modeling processes, choices of outcome measures, and techniques for sampling student performance • identify gaps in the literature that could benefit from further research • inform the debate among both researchers and policymakers about the potential of VAM. In addition, the research team estimated the effects of math teachers for students in Grades 3–5, using math scores from a sample of schools in a large suburban district. This independent analysis permitted examination of the effects of certain variations in modeling strategies.