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Correcting Selection Bias in Standardized Test Scores Comparisons

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  • Onil Boussim

Abstract

This paper addresses the issue of sample selection bias when comparing countries using International assessments like PISA (Program for International Student Assessment). Despite its widespread use, PISA rankings may be biased due to different attrition patterns in different countries, leading to inaccurate comparisons. This study proposes a methodology to correct for sample selection bias using a quantile selection model. Applying the method to PISA 2018 data, I find that correcting for selection bias significantly changes the rankings (based on the mean) of countries' educational performances. My results highlight the importance of accounting for sample selection bias in international educational comparisons.

Suggested Citation

  • Onil Boussim, 2023. "Correcting Selection Bias in Standardized Test Scores Comparisons," Papers 2309.10642, arXiv.org, revised Jun 2024.
  • Handle: RePEc:arx:papers:2309.10642
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    1. repec:hal:pseose:halshs-01030825 is not listed on IDEAS
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    4. Francisco H. G. Ferreira & Jérémie Gignoux, 2014. "The Measurement of Educational Inequality: Achievement and Opportunity," The World Bank Economic Review, World Bank, vol. 28(2), pages 210-246.
    5. Victor Chernozhukov & Ivan Fernandez-Val & Siyi Luo, 2023. "Distribution regression with sample selection and UK wage decomposition," CeMMAP working papers 09/23, Institute for Fiscal Studies.
    6. Patrick Mcewan & Jeffery Marshall, 2004. "Why does academic achievement vary across countries? Evidence from Cuba and Mexico," Education Economics, Taylor & Francis Journals, vol. 12(3), pages 205-217.
    7. Ministry of Human Resource Development, GOI, 2020. "National Education Policy 2020," Working Papers id:13106, eSocialSciences.
    8. Heckman, James J, 1974. "Shadow Prices, Market Wages, and Labor Supply," Econometrica, Econometric Society, vol. 42(4), pages 679-694, July.
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