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Test Measurement Error and Inference from Value-Added Models

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Abstract

It is widely known that standardized tests are noisy measures of student learning, but value added models (VAMs) rarely take direct account of measurement error in student test scores. We examine the extent to which modifying VAMs to include information about test measurement error (TME) can improve inference. 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 with which to modify our value-added models. In the real-data portion of our analysis we use estimates of TME provided by a major test publisher. We find that inference from VAMs is improved by making simple TME adjustments to the models. This is a notable result because the improvement can be had at zero cost.

Suggested Citation

  • Cory Koedel & Rebecca Leatherman & Eric Parsons, 2012. "Test Measurement Error and Inference from Value-Added Models," Working Papers 1201, Department of Economics, University of Missouri.
  • Handle: RePEc:umc:wpaper:1201
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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

    More about this item

    Keywords

    value added models; value added; teacher value added; test measurement error; teacher evaluation;

    JEL classification:

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

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