A Model for Teacher Effects from Longitudinal Data Without Assuming Vertical Scaling

Published in: Journal of Educational and Behavioral Statistics, v. 35, June 2010, p. 253-279

Posted on RAND.org on January 05, 2011

by Louis T. Mariano, Daniel F. McCaffrey, J. R. Lockwood

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There is an increasing interest in using longitudinal measures of student achievement to estimate individual teacher effects. Current multivariate models assume each teacher has a single effect on student outcomes that persists undiminished to all future test administrations (complete persistence [CP]) or can diminish with time but remains perfectly correlated (variable persistence [VP]). However, when state assessments do not use a vertical scale or the evolution of the mix of topics present across a sequence of vertically aligned assessments changes as students advance in school, these assumptions of persistence may not be consistent with the achievement data. We develop the "generalized persistence" (GP) model, a Bayesian multivariate model for estimating teacher effects that accommodates longitudinal data that are not vertically scaled by allowing less than perfect correlation of a teacher's effects across test administrations. We illustrate the model using mathematics assessment data.

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