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Measuring Knowledge and Learning

Author

Listed:
  • James J. Heckman
  • Jin Zhou

Abstract

Empirical studies in the economics of education, the measurement of skill gaps, the impacts of interventions on skill formation, and the value-added literature rely on psychometrically validated test scores. Test scores are taken as measures of an invariant scale of human capital compared over time and people. We examine if conventional skill measures are comparable on mastery of specific task knowledge. An unusually rich dataset from an early childhood intervention is used to test the assumption of scale invariance. We reject the scale invariance hypothesis for multiple skills and cast doubt on the uncritical use of test scores in research.

Suggested Citation

  • James J. Heckman & Jin Zhou, 2022. "Measuring Knowledge and Learning," NBER Working Papers 29990, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:29990
    Note: CH ED TWP
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    References listed on IDEAS

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    1. Joachim Freyberger, 2021. "Normalizations and misspecification in skill formation models," Papers 2104.00473, arXiv.org, revised Sep 2025.
    2. Heather C. Hill, 2009. "Evaluating value-added models: A validity argument approach," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 28(4), pages 700-709.
    3. Francesco Agostinelli & Morteza Saharkhiz & Matthew J. Wiswall, 2019. "Home and School in the Development of Children," NBER Working Papers 26037, National Bureau of Economic Research, Inc.
    4. Marta Rubio-Codina & María Caridad Araujo & Orazio P. Attanasio & Sally Grantham-McGregor, 2016. "Concurrent Validity and Feasibility of Short Tests Currently Used to Measure Early Childhood Development in Large Scale Studies: Methodology and Results," IDB Publications (Working Papers) 95556, Inter-American Development Bank.
    5. Flavio Cunha & James J. Heckman, 2008. "Formulating, Identifying and Estimating the Technology of Cognitive and Noncognitive Skill Formation," Journal of Human Resources, University of Wisconsin Press, vol. 43(4).
    6. Flavio Cunha & James J. Heckman & Susanne M. Schennach, 2010. "Estimating the Technology of Cognitive and Noncognitive Skill Formation," Econometrica, Econometric Society, vol. 78(3), pages 883-931, May.
    7. Flavio Cunha & Eric Nielsen & Benjamin Williams, 2021. "The Econometrics of Early Childhood Human Capital and Investments," Annual Review of Economics, Annual Reviews, vol. 13(1), pages 487-513, August.
    8. Petra E. Todd & Kenneth I. Wolpin, 2007. "The Production of Cognitive Achievement in Children: Home, School, and Racial Test Score Gaps," Journal of Human Capital, University of Chicago Press, vol. 1(1), pages 91-136.
    9. Jonah E. Rockoff, 2004. "The Impact of Individual Teachers on Student Achievement: Evidence from Panel Data," American Economic Review, American Economic Association, vol. 94(2), pages 247-252, May.
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    11. Rubio-Codina, Marta & Araujo, María Caridad & Attanasio, Orazio P. & Grantham-McGregor, Sally, 2016. "Concurrent Validity and Feasibility of Short Tests Currently Used to Measure Early Childhood Development in Large Scale Studies: Methodology and Results," IDB Publications (Working Papers) 7823, Inter-American Development Bank.
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    Cited by:

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    3. Chesney, Alexander J. & Carrell, Scott E., 2024. "Brains versus brawn: Ordinal rank effects in job training," Journal of Public Economics, Elsevier, vol. 238(C).

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    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing

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