JSM 2005 - Toronto

Abstract #304738

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Activity Number: 387
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #304738
Title: Bayesian Approaches to Value-added Modeling of Teacher and School Effects
Author(s): J. R. Lockwood*+ and Dan McCaffrey and Louis T. Mariano
Companies: RAND Corporation and RAND Corporation and RAND Corporation
Address: 201 N Craig St, Pittsburgh, PA, 15213, United States
Keywords: cross-classified models ; student achievement ; longitudinal data
Abstract:

"Value-added" models, using longitudinal student-level achievement data, are receiving intense interest as potential tools for school and teacher accountability. The statistical models producing value-added estimates of educator contributions to student learning require complex accounting of the linkage of students to teachers and schools throughout time and assumptions about how past educational experiences impact current achievement. These complexities challenge traditional likelihood estimation, particularly for large datasets becoming more common with the increased emphasis on standardized testing and the use of data to inform educational policy decisions. We discuss a general multivariate, longitudinal model for student outcomes and demonstrate how casting the model in the Bayesian framework affords both computational and inferential advantages. We present results about estimated teacher effects and the contributions of past teachers to current achievement using data from a large urban school district. We discuss extensions to our model that allow teacher effects to depend on latent student characteristics.

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