A Simulation Study of Diagnostics for Selection Bias

Boonstra Philip S. (), Little Roderick J.A. (), West Brady T. (), Andridge Rebecca R. () and Alvarado-Leiton Fernanda ()
Additional contact information
Boonstra Philip S.: University of Michigan, Department of Biostatistics, 1415 Washington Heights, Ann Arbor, MI 48109-2029, Michigan, 48105, U.S.A.
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.

Journal of Official Statistics, 2021, vol. 37, issue 3, 751-769

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.

Keywords: Non-ignorable selection bias; survey nonresponse; multiple imputation; pattern mixture model (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2478/jos-2021-0033 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:37:y:2021:i:3:p:751-769:n:2

DOI: 10.2478/jos-2021-0033

Access Statistics for this article

Journal of Official Statistics is currently edited by Annica Isaksson and Ingegerd Jansson

More articles in Journal of Official Statistics from Sciendo
Bibliographic data for series maintained by Peter Golla ().