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Bayesian Modeling of School Effects Using Hierarchical Models with Smoothing Priors

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Author Info
Mingliang Li (State University of New York at Buffalo)
Justin Tobias (Iowa State University)

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Abstract

We describe a new and flexible framework for modeling school effects. Like previous work in this area, we introduce an empirical model that evaluates school performance on the basis of student level test-score gains. Unlike previous work, however, we introduce a flexible model that relates follow-up student test scores to baseline student test scores and explore for possible nonlinearities in these relationships.Using data from High School and Beyond (HSB) and adapting the methodology described in Koop and Poirier (2004a), we test and reject the use of specifications that have been frequently used in research and as a basis for policy. We find that nonlinearities are important in the relationship between intake and follow-up achievement, that rankings of schools are sensitive to the model employed, and importantly, that commonly used specifications can give different and potentially misleading assessments of school performance. When estimating our preferred semiparametric specification, we find small but ``significant'' impacts of some school quality proxies (such as district-level expenditure per pupil) in the production of student achievement.

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Publisher Info
Article provided by Berkeley Electronic Press in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 9 (2005)
Issue (Month): 3 ()
Pages: 1271-1271
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Handle: RePEc:bep:sndecm:9:2005:3:1271-1271

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Related research
Keywords: Bayesian Modeling School Effects Hierarchical Models Smoothing Priors

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Betts, Julian R, 1995. "Does School Quality Matter? Evidence from the National Longitudinal Survey of Youth," The Review of Economics and Statistics, MIT Press, vol. 77(2), pages 231-50, May. [Downloadable!] (restricted)
  2. Eric A. Hanushek & John F. Kain & Steven G. Rivkin, 1998. "Teachers, Schools, and Academic Achievement," NBER Working Papers 6691, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  3. Koop, Gary & Poirier, Dale J., 2004. "Bayesian variants of some classical semiparametric regression techniques," Journal of Econometrics, Elsevier, vol. 123(2), pages 259-282, December. [Downloadable!] (restricted)
    Other versions:
  4. Min Yang & Harvey Goldstein & William Browne & Geoffrey Woodhouse, 2002. "Multivariate multilevel analyses of examination results," Journal Of The Royal Statistical Society Series A, Royal Statistical Society, vol. 127(1), pages 137-153. [Downloadable!] (restricted)
  5. Thomas J. Kane & Douglas O. Staiger, 2002. "The Promise and Pitfalls of Using Imprecise School Accountability Measures," Journal of Economic Perspectives, American Economic Association, vol. 16(4), pages 91-114, Fall. [Downloadable!] (restricted)
  6. Link, Charles R. & Mulligan, James G., 1991. "Classmates' effects on black student achievement in public school classrooms," Economics of Education Review, Elsevier, vol. 10(4), pages 297-310, December. [Downloadable!] (restricted)
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