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The Intertemporal Variability of Teacher Effect Estimates

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Author Info

  • Daniel F. McCaffrey

    ()
    (RAND Corporation)

  • Tim R. Sass

    ()
    (Department of Economics, Florida State University)

  • J. R. Lockwood

    ()
    (RAND Corporation)

  • Kata Mihaly

    ()
    (RAND Corporation)

Abstract

The utility of value-added estimates of teachers' effects on student test scores depends on whether they can distinguish between high- and low-productivity teachers and predict future teacher performance. This article studies the year-to-year variability in value-added measures for elementary and middle school mathematics teachers from five large Florida school districts. We find year-to-year correlations in value-added measures in the range of 0.2–0.5 for elementary school and 0.3–0.7 for middle school teachers. Much of the variation in measured teacher performance (roughly 30–60 percent) is due to sampling error from “noise” in student test scores. Persistent teacher effects account for about 50 percent of the variation not due to noise for elementary teachers and about 70 percent for middle school teachers. The remaining variance is due to teacher-level time-varying factors, but little of it is explained by observed teacher characteristics. Averaging estimates from two years greatly improves their ability to predict future performance. © 2009 American Education Finance Association

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Bibliographic Info

Article provided by MIT Press in its journal Education Finance and Policy.

Volume (Year): 4 (2009)
Issue (Month): 4 (October)
Pages: 572-606

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Handle: RePEc:tpr:edfpol:v:4:y:2009:i:4:p:572-606

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Related research

Keywords: value-added estimation; teacher effect; student test scores; teacher performance; Florida;

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Cited by:
  1. Goldhaber, Dan & Cowan, James & Walch, Joe, 2013. "Is a good elementary teacher always good? Assessing teacher performance estimates across subjects," Economics of Education Review, Elsevier, Elsevier, vol. 36(C), pages 216-228.
  2. Wiswall, Matthew, 2013. "The dynamics of teacher quality," Journal of Public Economics, Elsevier, Elsevier, vol. 100(C), pages 61-78.
  3. Harris, Douglas N. & Sass, Tim R., 2014. "Skills, productivity and the evaluation of teacher performance," Economics of Education Review, Elsevier, Elsevier, vol. 40(C), pages 183-204.
  4. Cory Koedel & Julian R. Betts, 2011. "Does Student Sorting Invalidate Value-Added Models of Teacher Effectiveness? An Extended Analysis of the Rothstein Critique," Education Finance and Policy, MIT Press, MIT Press, vol. 6(1), pages 18-42, January.
  5. Stephen Lipscomb & Brian Gill & Kevin Booker & Matthew Johnson, 2010. "Estimating Teacher and School Effectiveness in Pittsburgh: Value-Added Modeling and Results," Mathematica Policy Research Reports, Mathematica Policy Research 7644, Mathematica Policy Research.
  6. Goldhaber, Dan & Liddle, Stephanie & Theobald, Roddy, 2013. "The gateway to the profession: Assessing teacher preparation programs based on student achievement," Economics of Education Review, Elsevier, Elsevier, vol. 34(C), pages 29-44.
  7. Gregory F. Branch & Eric A. Hanushek & Steven G. Rivkin, 2012. "Estimating the Effect of Leaders on Public Sector Productivity: The Case of School Principals," NBER Working Papers 17803, National Bureau of Economic Research, Inc.

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