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Test Measurement Error and Inference from Value-Added Models

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  • Koedel Cory

    ()
    (University of Missouri-Columbia)

  • Leatherman Rebecca

    ()
    (University of Missouri-Columbia)

  • Parsons Eric

    ()
    (University of Missouri-Columbia)

Abstract

It is widely known that standardized tests are noisy measures of student learning, but value added models (VAMs) rarely account for test measurement error (TME). We incorporate information about TME directly into VAMs, focusing on TME that derives from the testing instrument itself. Our analysis is divided into two parts – one based on simulated data and the other based on administrative micro data from Missouri. In the simulations we control the data generating process, which ensures that we obtain accurate TME metrics. In the real-data portion of our analysis we use estimates of TME provided by a major test publisher. In both the simulations and real-data analyses, we find that inference from VAMs is improved by making simple TME adjustments to the models. The improvement is larger in the simulations, but even in the real-data analysis the improvement is on the order of what one could expect if teacher-level sample sizes were increased by 11 to 17 percent.

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Bibliographic Info

Article provided by De Gruyter in its journal The B.E. Journal of Economic Analysis & Policy.

Volume (Year): 12 (2012)
Issue (Month): 1 (November)
Pages: 1-37

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Handle: RePEc:bpj:bejeap:v:12:y:2012:i:1:p:1-37:n:59

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References

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  1. Daniel Aaronson & Lisa Barrow & William Sander, 2002. "Teachers and student achievement in the Chicago public high schools," Working Paper Series WP-02-28, Federal Reserve Bank of Chicago.
  2. Donald Boyd & Hamilton Lankford & Susanna Loeb & James Wyckoff, 2011. "Teacher Layoffs: An Empirical Illustration of Seniority versus Measures of Effectiveness," Education Finance and Policy, MIT Press, vol. 6(3), pages 439-454, July.
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  6. Gadi Barlevy & Derek Neal, 2009. "Pay for percentile," Working Paper Series WP-09-09, Federal Reserve Bank of Chicago.
  7. Heather C. Hill, 2009. "Evaluating value-added models: A validity argument approach," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 28(4), pages 700-709.
  8. 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, vol. 6(1), pages 18-42, January.
  9. Raj Chetty & John N. Friedman & Jonah E. Rockoff, 2011. "The Long-Term Impacts of Teachers: Teacher Value-Added and Student Outcomes in Adulthood," NBER Working Papers 17699, National Bureau of Economic Research, Inc.
  10. Eric A. Hanushek & Steven G. Rivkin, 2010. "Generalizations about Using Value-Added Measures of Teacher Quality," American Economic Review, American Economic Association, vol. 100(2), pages 267-71, May.
  11. Douglas O. Staiger & Jonah E. Rockoff, 2010. "Searching for Effective Teachers with Imperfect Information," Journal of Economic Perspectives, American Economic Association, vol. 24(3), pages 97-118, Summer.
  12. Thomas J. Kane & Douglas O. Staiger, 2008. "Estimating Teacher Impacts on Student Achievement: An Experimental Evaluation," NBER Working Papers 14607, National Bureau of Economic Research, Inc.
  13. 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.
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Cited by:
  1. Mariesa Herrmann & Elias Walsh & Eric Isenberg & Alexandra Resch, 2013. "Shrinkage of Value-Added Estimates and Characteristics of Students with Hard-to-Predict Achievement Levels," Mathematica Policy Research Reports 7748, Mathematica Policy Research.
  2. Dieterle, Steven G. & Guarino, Cassandra & Reckase, Mark D. & Wooldridge, Jeffrey M., 2012. "How do Principals Assign Students to Teachers? Finding Evidence in Administrative Data and the Implications for Value-added," IZA Discussion Papers 7112, Institute for the Study of Labor (IZA).
  3. Eric Parsons, 2013. "The Icarus Syndrome: Why Do Some High Flyers Soar While Others Fall?," Working Papers 1308, Department of Economics, University of Missouri.
  4. Matthew Johnson & Stephen Lipscomb & Brian Gill, 2013. "Sensitivity of Teacher Value-Added Estimates to Student and Peer Control Variables," Mathematica Policy Research Reports 7941, Mathematica Policy Research.
  5. Cory Koedel & Mark Ehlert & Eric Parsons & Michael Podgursky, 2012. "Selecting Growth Measures for School and Teacher Evaluations," Working Papers 1210, Department of Economics, University of Missouri.
  6. Cory Koedel & Jiaxi Li, 2014. "The Efficiency Implications of Using Proportional Evaluations to Shape the Teaching Workforce," Working Papers 1402, Department of Economics, University of Missouri.

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