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Measurement Matters: Perspectives on Education Policy from an Economist and School Board Member

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  • Kevin Lang

Abstract

From the perspective of someone who is both an economist and a former school board member, this paper assesses the use of test-score data by policy-makers and academic researchers. The absence of interval scales often makes the interpretation of test-scores and the indicators derived from them highly problematic. Economists have both been deficient in alerting policy-makers to the inherent difficulties in using such data and cavalier in their own treatment of them. Test score data often merely confirm what administrators already know. Nevertheless they can be useful if used as a trigger for further investigation, and, in particular, as a lever for encouraging principals and other administrators to act on information about teacher (or school) quality.

Suggested Citation

  • Kevin Lang, 2010. "Measurement Matters: Perspectives on Education Policy from an Economist and School Board Member," Carlo Alberto Notebooks 143, Collegio Carlo Alberto.
    • Handle: RePEc:cca:wpaper:143
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    References listed on IDEAS

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    1. Kevin Lang, 2007. "Introduction to Poverty and Discrimination," Introductory Chapters, in: Poverty and Discrimination, Princeton University Press.
    2. 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.
    3. 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.
    4. J. R. Lockwood & Daniel F. McCaffrey & Louis T. Mariano & Claude Setodji, 2007. "Bayesian Methods for Scalable Multivariate Value-Added Assessment," Journal of Educational and Behavioral Statistics, , vol. 32(2), pages 125-150, June.
    5. Brian A. Jacob & Lars Lefgren, 2004. "Remedial Education and Student Achievement: A Regression-Discontinuity Analysis," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 226-244, February.
    6. Brian A. Jacob & Lars Lefgren, 2008. "Can Principals Identify Effective Teachers? Evidence on Subjective Performance Evaluation in Education," Journal of Labor Economics, University of Chicago Press, vol. 26(1), pages 101-136.
    7. Daniel F. McCaffrey & J. R. Lockwood & Daniel Koretz & Thomas A. Louis & Laura Hamilton, 2004. "Models for Value-Added Modeling of Teacher Effects," Journal of Educational and Behavioral Statistics, , vol. 29(1), pages 67-101, March.
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    Cited by:

    1. Sonia Bhalotra & Martin Karlsson & Therese Nilsson & Nina Schwarz, 2022. "Infant Health, Cognitive Performance, and Earnings: Evidence from Inception of the Welfare State in Sweden," The Review of Economics and Statistics, MIT Press, vol. 104(6), pages 1138-1156, November.
    2. Elizabeth U. Cascio & Douglas O. Staiger, 2012. "Knowledge, Tests, and Fadeout in Educational Interventions," NBER Working Papers 18038, National Bureau of Economic Research, Inc.
    3. Schwerdt, Guido & West, Martin R. & Winters, Marcus A., 2017. "The effects of test-based retention on student outcomes over time: Regression discontinuity evidence from Florida," Journal of Public Economics, Elsevier, vol. 152(C), pages 154-169.
    4. Timothy N. Bond & Kevin Lang, 2013. "The Evolution of the Black-White Test Score Gap in Grades K–3: The Fragility of Results," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1468-1479, December.
    5. Carsten Schroeder & Shlomo Yitzhaki, 2020. "Exploring the robustness of country rankings by educational attainment," Journal of Economics, Springer, vol. 129(3), pages 271-296, April.
    6. Forbes Silke & Gordon Nora, 2012. "When Educators Are the Learners: Private Contracting by Public Schools," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 12(1), pages 1-29, July.
    7. Joshua B. Gilbert & Zachary Himmelsbach & James Soland & Mridul Joshi & Benjamin W. Domingue, 2024. "Estimating Heterogeneous Treatment Effects with Item-Level Outcome Data: Insights from Item Response Theory," Papers 2405.00161, arXiv.org, revised Aug 2024.
    8. Mariesa A. Herrmann & Jonah E. Rockoff, 2012. "Worker Absence and Productivity: Evidence from Teaching," Journal of Labor Economics, University of Chicago Press, vol. 30(4), pages 749-782.
    9. 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.
    10. Nirav Mehta, 2019. "Measuring quality for use in incentive schemes: The case of “shrinkage” estimators," Quantitative Economics, Econometric Society, vol. 10(4), pages 1537-1577, November.
    11. Jeffrey R. Bloem & Andrew J. Oswald, 2022. "The Analysis of Human Feelings: A Practical Suggestion for a Robustness Test," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(3), pages 689-710, September.
    12. Diego Azqueta Oyarzun & Guillermina Gavaldon, 2014. "The economic assessment of education: Social Efficiency or Social Reconstruction?," Investigaciones de Economía de la Educación volume 9, in: Adela García Aracil & Isabel Neira Gómez (ed.), Investigaciones de Economía de la Educación 9, edition 1, volume 9, chapter 51, pages 969-978, Asociación de Economía de la Educación.
    13. Eric R. Nielsen, 2015. "Achievement Gap Estimates and Deviations from Cardinal Comparability," Finance and Economics Discussion Series 2015-40, Board of Governors of the Federal Reserve System (U.S.).
    14. Wan, Sirui & Bond, Timothy N. & Lang, Kevin & Clements, Douglas H. & Sarama, Julie & Bailey, Drew H., 2021. "Is intervention fadeout a scaling artefact?," Economics of Education Review, Elsevier, vol. 82(C).
    15. Eric R. Nielsen, 2015. "The Income-Achievement Gap and Adult Outcome Inequality," Finance and Economics Discussion Series 2015-41, Board of Governors of the Federal Reserve System (U.S.).

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    More about this item

    Keywords

    NCLB; No Child Left Behind; value-added-measurement in education; accountability; high-stakes testing;
    All these keywords.

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare

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