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A Comment on "A Systematic Review and Meta-Analysis of the Evidence on Learning During the COVID-19 Pandemic"

Author

Listed:
  • Buliskeria, Nino
  • Elminejad, Ali
  • Havranek, Tomas
  • Irsova, Zuzana
  • Jurajda, Stepan
  • Kapicka, Marek
  • Luskova, Martina

Abstract

Betthäuser et al. (2023) examine the effects of the COVID-19 pandemic on the learning progress of school-aged children. They collect 291 estimates from 42 studies. Their meta-analysis-corrected estimate implies a substantial decline in students' learning (Cohen's d = −0.14, 95% confidence interval −0.17 to −0.10). First, we successfully reproduce the main results and the majority of supporting figures. Second, we provide additional analysis addressing publication bias by implementing correction techniques: PET-PEESE (funnelbased), 3PSM (selection model), and RoBMA (model averaging). Additionally, we implement novel approaches that account for the strength of biased selection favoring affirmative results in the sample of analyzed studies. Third, we use techniques that assume the presence of p-hacking (MAIVE, RTMA). Using these methods, the corrected effect ranges from −0.25 to −0.11 with high statistical significance. While our analysis does reveal some evidence of selection bias in underlying data (primary studies), these phenomena do not appear to systematically distort the overall findings of the original study.

Suggested Citation

  • Buliskeria, Nino & Elminejad, Ali & Havranek, Tomas & Irsova, Zuzana & Jurajda, Stepan & Kapicka, Marek & Luskova, Martina, 2025. "A Comment on "A Systematic Review and Meta-Analysis of the Evidence on Learning During the COVID-19 Pandemic"," I4R Discussion Paper Series 223, The Institute for Replication (I4R).
  • Handle: RePEc:zbw:i4rdps:223
    as

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    References listed on IDEAS

    as
    1. Abel Brodeur & Scott Carrell & David Figlio & Lester Lusher, 2023. "Unpacking P-hacking and Publication Bias," American Economic Review, American Economic Association, vol. 113(11), pages 2974-3002, November.
    2. Abel Brodeur & Nikolai Cook & Anthony Heyes, 2020. "Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics," American Economic Review, American Economic Association, vol. 110(11), pages 3634-3660, November.
    3. Bastian A. Betthäuser & Anders M. Bach-Mortensen & Per Engzell, 2023. "A systematic review and meta-analysis of the evidence on learning during the COVID-19 pandemic," Nature Human Behaviour, Nature, vol. 7(3), pages 375-385, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Replication; Robustness; Meta-analysis; COVID-19; Education; Learning deficit;
    All these keywords.

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models

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