<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

A Simulation Study of Diagnostics for Selection Bias

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"A Simulation Study of Diagnostics for Selection Bias"}

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...

A Simulation Study of Diagnostics for Selection Bias

Online veröffentlicht: 13. Sept. 2021

Seitenbereich: 751 - 769

Eingereicht: 01. Juli 2019

Akzeptiert: 01. Nov. 2020

DOI: https://doi.org/10.2478/jos-2021-0033

Schlüsselwörter
Non-ignorable selection bias, survey nonresponse, multiple imputation, pattern mixture model

© 2021 Philip S. Boonstra et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Simulation Study of Diagnostics for Selection Bias

Online veröffentlicht: 13. Sept. 2021

Seitenbereich: 751 - 769

Eingereicht: 01. Juli 2019

Akzeptiert: 01. Nov. 2020

DOI: https://doi.org/10.2478/jos-2021-0033

SchlüsselwörterNon-ignorable selection bias, survey nonresponse, multiple imputation, pattern mixture model

© 2021 Philip S. Boonstra et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Non-ignorable selection bias, survey nonresponse, multiple imputation, pattern mixture model