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Bayesian Methods for Scalable Multivariate Value-Added Assessment

Author

Listed:
  • J. R. Lockwood
  • Daniel F. McCaffrey
  • Louis T. Mariano
  • Claude Setodji

Abstract

There is increased interest in value-added models relying on longitudinal student-level test score data to isolate teachers’ contributions to student achievement. The complex linkage of students to teachers as students progress through grades poses both substantive and computational challenges. This article introduces a multivariate Bayesian formulation of the longitudinal model developed by McCaffrey, Lockwood, Koretz, Louis, and Hamilton (2004) that explicitly parameterizes the long-term effects of past teachers on student outcomes in future years and shows how the Bayesian approach makes estimation feasible even for large data sets. The article presents empirical results using reading and mathematics achievement data from a large urban school district, providing estimates of teacher effect persistence and examining how different assumptions about persistence impact estimated teacher effects. It also examines the impacts of alternative methods of accounting for missing teacher links and of joint versus marginal modeling of reading and mathematics.

Suggested Citation

  • J. R. Lockwood & Daniel F. McCaffrey & Louis T. Mariano & Claude Setodji, 2007. "Bayesian Methods for Scalable Multivariate Value-Added Assessment," Journal of Educational and Behavioral Statistics, , vol. 32(2), pages 125-150, June.
    • Handle: RePEc:sae:jedbes:v:32:y:2007:i:2:p:125-150
    DOI: 10.3102/1076998606298039
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    Citations

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    Cited by:

    1. Brian A. Jacob & Lars Lefgren & David Sims, 2008. "The Persistence of Teacher-Induced Learning Gains," NBER Working Papers 14065, National Bureau of Economic Research, Inc.
    2. Gordon B. Dahl & Lance Lochner, 2012. "The Impact of Family Income on Child Achievement: Evidence from the Earned Income Tax Credit," American Economic Review, American Economic Association, vol. 102(5), pages 1927-1956, August.
    3. Han Bing & McCaffrey Daniel & Springer Matthew G. & Gottfried Michael, 2012. "Teacher Effect Estimates and Decision Rules for Establishing Student-Teacher Linkages: What are the Implications for High-Stakes Personnel Policies in an Urban School District?," Statistics, Politics and Policy, De Gruyter, vol. 3(2), pages 1-24, July.
    4. Kevin Lang, 2010. "Measurement Matters: Perspectives on Education Policy from an Economist and School Board Member," Journal of Economic Perspectives, American Economic Association, vol. 24(3), pages 167-182, Summer.
    5. Eric Parsons & Cory Koedel & Li Tan, 2019. "Accounting for Student Disadvantage in Value-Added Models," Journal of Educational and Behavioral Statistics, , vol. 44(2), pages 144-179, April.
    6. Josh Kinsler, 2016. "Teacher Complementarities in Test Score Production: Evidence from Primary School," Journal of Labor Economics, University of Chicago Press, vol. 34(1), pages 29-61.
    7. Vosters, Kelly N. & Guarino, Cassandra M. & Wooldridge, Jeffrey M., 2018. "Understanding and evaluating the SAS® EVAAS® Univariate Response Model (URM) for measuring teacher effectiveness," Economics of Education Review, Elsevier, vol. 66(C), pages 191-205.

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