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Missing, presumed different: Quantifying the risk of attrition bias in education evaluations

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  • Ben Weidmann
  • Luke Miratrix

Abstract

We estimate the magnitude of attrition bias for 10 randomized controlled trials (RCTs) in education. We make use of a unique feature of administrative school data in England that allows us to analyse post‐test academic outcomes for nearly all students, including those who originally dropped out of the RCTs we analyse. We find that the typical magnitude of attrition bias is 0.015 effect size units (ES), with no estimate greater than 0.034 ES. This suggests that, in practice, the risk of attrition bias is limited. However, this risk should not be ignored as we find some evidence against the common ‘Missing At Random’ assumption. Attrition appears to be more problematic for treated units. We recommend that researchers incorporate uncertainty due to attrition bias, as well as performing sensitivity analyses based on the types of attrition mechanisms that are observed in practice.

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  • Ben Weidmann & Luke Miratrix, 2021. "Missing, presumed different: Quantifying the risk of attrition bias in education evaluations," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 732-760, April.
    • Handle: RePEc:bla:jorssa:v:184:y:2021:i:2:p:732-760
    DOI: 10.1111/rssa.12677
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    1. Dalia Ghanem & Sarojini Hirshleifer & Karen Ortiz-Becerra, 2019. "Testing Attrition Bias in Field Experiments," Working Papers 202218, University of California at Riverside, Department of Economics, revised Oct 2022.
    2. Ghanem, Dalia & Hirshleifer, Sarojini & Ortiz-Becerra, Karen, 2019. "Testing Attrition Bias in Field Experiments," 2019 Annual Meeting, July 21-23, Atlanta, Georgia 291215, Agricultural and Applied Economics Association.
    3. David Greenberg & Burt S. Barnow, 2014. "Flaws in Evaluations of Social Programs," Evaluation Review, , vol. 38(5), pages 359-387, October.
    4. Harvey Goldstein & James R. Carpenter & William J. Browne, 2014. "Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non-linear terms," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(2), pages 553-564, February.
    5. John Deke & Hanley Chiang, 2017. "The WWC Attrition Standard," Evaluation Review, , vol. 41(2), pages 130-154, April.
    6. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-323, May.
    7. Rolf Sundberg, 2003. "Conditional statistical inference and quantification of relevance," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 299-315, February.
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