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Test Measurement Error and Inference from Value-Added Models


  • Koedel Cory

    () (University of Missouri-Columbia)

  • Leatherman Rebecca

    () (University of Missouri-Columbia)

  • Parsons Eric

    () (University of Missouri-Columbia)


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.

Suggested Citation

  • Koedel Cory & Leatherman Rebecca & Parsons Eric, 2012. "Test Measurement Error and Inference from Value-Added Models," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 12(1), pages 1-37, November.
  • Handle: RePEc:bpj:bejeap:v:12:y:2012:i:1:p:1-37:n:59

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    References listed on IDEAS

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    Cited by:

    1. Dionissi Aliprantis, 2014. "When Should Children Start School?," Journal of Human Capital, University of Chicago Press, vol. 8(4), pages 481-536.
    2. Cory Koedel & Jiaxi Li, 2016. "The Efficiency Implications Of Using Proportional Evaluations To Shape The Teaching Workforce," Contemporary Economic Policy, Western Economic Association International, vol. 34(1), pages 47-62, January.
    3. Matthew Johnson & Stephen Lipscomb & Brian Gill, 2013. "Sensitivity of Teacher Value-Added Estimates to Student and Peer Control Variables," Mathematica Policy Research Reports 3f875df699534c72b9e57c39d, Mathematica Policy Research.
    4. 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.
    5. repec:mpr:mprres:7748 is not listed on IDEAS
    6. Cory Koedel & Eric Parsons & Michael Podgursky & Mark Ehlert, 2015. "Teacher Preparation Programs and Teacher Quality: Are There Real Differences Across Programs?," Education Finance and Policy, MIT Press, vol. 10(4), pages 508-534, October.
    7. Steven Dieterle & Cassandra M. Guarino & Mark D. Reckase & Jeffrey M. Wooldridge, 2015. "How do Principals Assign Students to Teachers? Finding Evidence in Administrative Data and the Implications for Value Added," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 34(1), pages 32-58, January.
    8. repec:mpr:mprres:7941 is not listed on IDEAS
    9. Koedel, Cory & Mihaly, Kata & Rockoff, Jonah E., 2015. "Value-added modeling: A review," Economics of Education Review, Elsevier, vol. 47(C), pages 180-195.
    10. 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 2b140369be0242ac83eeb5b0a, Mathematica Policy Research.
    11. repec:umc:wpaper:1308 is not listed on IDEAS

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    • I20 - Health, Education, and Welfare - - Education - - - General


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