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Value-added modeling (VAM) to estimate school and teacher effects is currently of considerable interest to researchers and policymakers. Recent reports suggest that VAM demonstrates the importance of teachers as a source of variance in student outcomes. Policymakers see VAM as a possible component of education reform through improved teacher evaluations or as part of test-based accountability. They are intrigued by VAM because of the view that its complex statistical techniques can provide estimates of the effects of teachers and schools that are not distorted by the powerful effects of such noneducational factors as family background. This monograph clarifies the primary questions raised by the use of VAM for measuring teacher effects, reviews the most important recent applications of VAM, and discusses a variety of statistical and measurement issues that might affect the validity of VAM inferences. The authors identify numerous possible sources of error and bias in teacher effects and recommend a number of steps for future research into these potential errors. They conclude that the research base is currently insufficient to support the use of VAM for high-stakes decisions about individual teachers or schools. It is important that policymakers, practitioners, and VAM researchers work together, so that research is informed by the practical needs and constraints facing users of VAM and that implementation of the models is informed by an understanding of what inferences and decisions the research currently supports.

The research described in this report was conducted by RAND Education for the Carnegie Corporation of New York.

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