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Lurking Inferential Monsters? Quantifying Selection Bias In Evaluations Of School Programs

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  • Ben Weidmann
  • Luke Miratrix

Abstract

This study examines whether unobserved factors substantially bias education evaluations that rely on the Conditional Independence Assumption. We add 14 new within‐study comparisons to the literature, all from primary schools in England. Across these 14 studies, we generate 42 estimates of selection bias using a simple approach to observational analysis. A meta‐analysis of these estimates suggests that the distribution of underlying bias is centered around zero. The mean absolute value of estimated bias is 0.03σ, and none of the 42 estimates are larger than 0.11σ. Results are similar for math, reading, and writing outcomes. Overall, we find no evidence of substantial selection bias due to unobserved characteristics. These findings may not generalize easily to other settings or to more radical educational interventions, but they do suggest that non‐experimental approaches could play a greater role than they currently do in generating reliable causal evidence for school education.

Suggested Citation

  • Ben Weidmann & Luke Miratrix, 2021. "Lurking Inferential Monsters? Quantifying Selection Bias In Evaluations Of School Programs," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 40(3), pages 964-986, June.
  • Handle: RePEc:wly:jpamgt:v:40:y:2021:i:3:p:964-986
    DOI: 10.1002/pam.22236
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    1. Greta Morando & Lucinda Platt, 2022. "The Impact of Centre‐based Childcare on Non‐cognitive Skills of Young Children," Economica, London School of Economics and Political Science, vol. 89(356), pages 908-946, October.
    2. Gonzalo Nunez-Chaim & Henry G. Overman & Capucine Riom, 2024. "Does subsidising business advice improve firm performance? Evidence from a large RCT," CEP Discussion Papers dp1977, Centre for Economic Performance, LSE.
    3. John Deke & Mariel Finucane & Daniel Thal, "undated". "The BASIE (BAyeSian Interpretation of Estimates) Framework for Interpreting Findings from Impact Evaluations: A Practical Guide for Education Researchers," Mathematica Policy Research Reports 5a0d5dff375d42048799878be, Mathematica Policy Research.
    4. Sam Sims & Jake Anders & Matthew Inglis & Hugues Lortie-Forgues & Ben Styles & Ben Weidmann, 2023. "Experimental education research: rethinking why, how and when to use random assignment," CEPEO Working Paper Series 23-07, UCL Centre for Education Policy and Equalising Opportunities, revised Aug 2023.

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