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Test Scaling and Value-Added Measurement

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Listed author(s):
  • Dale Ballou

    ()

    (Department of Leadership, Policy and Organizations, Peabody College, Vanderbilt University)

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    Abstract

    Conventional value-added assessment requires that achievement be reported on an interval scale. While many metrics do not have this property, application of item response theory (IRT) is said to produce interval scales. However, it is difficult to confirm that the requisite conditions are met. Even when they are, the properties of the data that make a test IRT scalable may not be the properties we seek to represent in an achievement scale, as shown by the lack of surface plausibility of many scales resulting from the application of IRT. An alternative, ordinal data analysis, is presented. It is shown that value-added estimates are sensitive to the choice of ordinal methods over conventional techniques. Value-added practitioners should ask themselves whether they are so confident of the metric properties of these scales that they are willing to attribute differences to the superiority of the latter. © 2009 American Education Finance Association

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    File URL: http://www.mitpressjournals.org/doi/pdf/10.1162/edfp.2009.4.4.351
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    Article provided by MIT Press in its journal Education Finance and Policy.

    Volume (Year): 4 (2009)
    Issue (Month): 4 (October)
    Pages: 351-383

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    Handle: RePEc:tpr:edfpol:v:4:y:2009:i:4:p:351-383
    Contact details of provider: Web page: http://mitpress.mit.edu/journals/

    Order Information: Web: http://www.mitpressjournals.org/loi/edfp

    Keywords: value-added assessment; test scaling; item response theory;

    Find related papers by JEL classification:

    • I20 - Health, Education, and Welfare - - Education - - - General
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

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    Cited by:

    1. Gadi Barlevy & Derek Neal, 2012. "Pay for Percentile," American Economic Review, American Economic Association, vol. 102(5), pages 1805-1831, August.
    2. Seth Gershenson, 2016. "Performance Standards and Employee Effort: Evidence From Teacher Absences," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 35(3), pages 615-638, 06.
    3. Barrett, Nathan & Toma, Eugenia F., 2013. "Reward or punishment? Class size and teacher quality," Economics of Education Review, Elsevier, vol. 35(C), pages 41-52.
    4. 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.
    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. Seth Gershenson & Diane Whitmore Schanzenbach, 2016. "Linking Teacher Quality, Student Attendance, and Student Achievement," Education Finance and Policy, MIT Press, vol. 11(2), pages 125-149, Spring.
    7. Gershenson, Seth & Holt, Stephen B. & Papageorge, Nicholas W., 2015. "Who Believes in Me? The Effect of Student-Teacher Demographic Match on Teacher Expectations," IZA Discussion Papers 9202, Institute for the Study of Labor (IZA).
    8. Wiswall, Matthew, 2013. "The dynamics of teacher quality," Journal of Public Economics, Elsevier, vol. 100(C), pages 61-78.

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