Fitting Value-Added Models in R

Harold C. Doran and J. R. Lockwood

Journal of Educational and Behavioral Statistics, 2006, vol. 31, issue 2, 205-230

Abstract: Value-added models of student achievement have received widespread attention in light of the current test-based accountability movement. These models use longitudinal growth modeling techniques to identify effective schools or teachers based upon the results of changes in student achievement test scores. Given their increasing popularity, this article demonstrates how to perform the data analysis necessary to fit a general value-added model using the nlme package available for the R statistics environment. We demonstrate techniques for inspecting the data prior to fitting the model, walk a practitioner through a sample analysis, and discuss general extensions commonly found across the literature that may be incorporated to enhance the basic model presented, including the estimation of multiple outcomes and teacher effects.

Keywords: accountability; hierarchical linear model; longitudinal data analysis; mixed-effects models; nlme; value-added model (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:31:y:2006:i:2:p:205-230

DOI: 10.3102/10769986031002205

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