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A Simulation Study of Diagnostics for Selection Bias

Author

Listed:
  • Boonstra Philip S.
  • Little Roderick J.A.

    (University of Michigan, Department of Biostatistics, 1415 Washington Heights, Ann Arbor, MI 48109-2029, Michigan, 48105, U.S.A.)

  • West Brady T.

    (University of Michigan, Institute for Social Research, Survey Methodology Program, 426 Thompson Street, Ann Arbor, Michigan 48106, U.S.A.)

  • Andridge Rebecca R.

    (The Ohio State University, 1841 Neil Avenue, 242 Cunz Hall, Columbus, Ohio 43210, U.S.A.)

  • Alvarado-Leiton Fernanda

    (University of Michigan, Survey Methodology Progam, 4134 ISR-Thompson 426 Thompson St Ann Arbor, Michigan, U.S.A.)

Abstract

A non-probability sampling mechanism arising from nonresponse or non-selection is likely to bias estimates of parameters with respect to a target population of interest. This bias poses a unique challenge when selection is ‘non-ignorable’, that is, dependent on the unobserved outcome of interest, since it is then undetectable and thus cannot be ameliorated. We extend a simulation study by Nishimura et al. (2016) adding two recently published statistics: the ‘standardized measure of unadjusted bias’ (SMUB) and ‘standardized measure of adjusted bias’ (SMAB), which explicitly quantify the extent of bias (in the case of SMUB) or nonignorable bias (in the case of SMAB) under the assumption that a specified amount of nonignorable selection exists. Our findings suggest that this new sensitivity diagnostic is more correlated with, and more predictive of, the true, unknown extent of selection bias than other diagnostics, even when the underlying assumed level of non-ignorability is incorrect.

Suggested Citation

  • Boonstra Philip S. & Little Roderick J.A. & West Brady T. & Andridge Rebecca R. & Alvarado-Leiton Fernanda, 2021. "A Simulation Study of Diagnostics for Selection Bias," Journal of Official Statistics, Sciendo, vol. 37(3), pages 751-769, September.
    • Handle: RePEc:vrs:offsta:v:37:y:2021:i:3:p:751-769:n:2
    DOI: 10.2478/jos-2021-0033
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    References listed on IDEAS

    as
    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Raphael Nishimura & James Wagner & Michael Elliott, 2016. "Alternative Indicators for the Risk of Non-response Bias: A Simulation Study," International Statistical Review, International Statistical Institute, vol. 84(1), pages 43-62, April.
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