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Test Scaling and Value-Added Measurement

Author

Listed:
  • Dale Ballou

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

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

Suggested Citation

  • Dale Ballou, 2009. "Test Scaling and Value-Added Measurement," Education Finance and Policy, MIT Press, vol. 4(4), pages 351-383, October.
    • Handle: RePEc:tpr:edfpol:v:4:y:2009:i:4:p:351-383
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    File URL: http://www.mitpressjournals.org/doi/pdf/10.1162/edfp.2009.4.4.351
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    Citations

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    Cited by:

    1. 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, June.
    2. 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.
    3. Alexander Robitzsch, 2021. "About the Equivalence of the Latent D-Scoring Model and the Two-Parameter Logistic Item Response Model," Mathematics, MDPI, vol. 9(13), pages 1-17, June.
    4. Gadi Barlevy & Derek Neal, 2012. "Pay for Percentile," American Economic Review, American Economic Association, vol. 102(5), pages 1805-1831, August.
    5. Daniel M. Bolt & Xiangyi Liao, 2022. "Item Complexity: A Neglected Psychometric Feature of Test Items?," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1195-1213, December.
    6. 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.
    7. 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.
    8. Alexander Robitzsch, 2024. "Estimation of Standard Error, Linking Error, and Total Error for Robust and Nonrobust Linking Methods in the Two-Parameter Logistic Model," Stats, MDPI, vol. 7(3), pages 1-21, June.
    9. 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.
    10. Seth Gershenson & Stephen B. Holt & Nicholas Papageorge, 2015. "Who Believes in Me? The Effect of Student-Teacher Demographic Match on Teacher Expectations," Upjohn Working Papers 15-231, W.E. Upjohn Institute for Employment Research.
    11. Benjamin R. Shear & Sean F. Reardon, 2021. "Using Pooled Heteroskedastic Ordered Probit Models to Improve Small-Sample Estimates of Latent Test Score Distributions," Journal of Educational and Behavioral Statistics, , vol. 46(1), pages 3-33, February.
    12. Derek C. Briggs & Ben Domingue, 2013. "The Gains From Vertical Scaling," Journal of Educational and Behavioral Statistics, , vol. 38(6), pages 551-576, December.
    13. Donald Boyd & Hamilton Lankford & Susanna Loeb & James Wyckoff, 2013. "Measuring Test Measurement Error," Journal of Educational and Behavioral Statistics, , vol. 38(6), pages 629-663, December.
    14. Brendan Houng & Moshe Justman, 2013. "Comparing Least-Squares Value-Added Analysis and Student Growth Percentile Analysis for Evaluating Student Progress and Estimating School Effects," Melbourne Institute Working Paper Series wp2013n07, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    15. David M. Quinn & Andrew D. Ho, 2021. "Ordinal Approaches to Decomposing Between-Group Test Score Disparities," Journal of Educational and Behavioral Statistics, , vol. 46(4), pages 466-500, August.
    16. Moshe Justman & Brendan Houng, 2013. "A Comparison Of Two Methods For Estimating School Effects And Tracking Student Progress From Standardized Test Scores," Working Papers 1316, Ben-Gurion University of the Negev, Department of Economics.
    17. Wiswall, Matthew, 2013. "The dynamics of teacher quality," Journal of Public Economics, Elsevier, vol. 100(C), pages 61-78.
    18. J. R. Lockwood & Daniel F. McCaffrey, 2014. "Correcting for Test Score Measurement Error in ANCOVA Models for Estimating Treatment Effects," Journal of Educational and Behavioral Statistics, , vol. 39(1), pages 22-52, February.

    More about this item

    Keywords

    value-added assessment; test scaling; item response theory;
    All these keywords.

    JEL classification:

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

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