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Indices of non-ignorable selection bias for proportions estimated from non-probability samples.

ABSTRACT: Rising costs of survey data collection and declining response rates have caused researchers to turn to non-probability samples to make descriptive statements about populations. However, unlike probability samples, non-probability samples may produce severely biased descriptive estimates due to selection bias. The paper develops and evaluates a simple model-based index of the potential selection bias in estimates of population proportions due to non-ignorable selection mechanisms. The index depends on an inestimable parameter ranging from 0 to 1 that captures the amount of deviation from selection at random and is thus well suited to a sensitivity analysis. We describe modified maximum likelihood and Bayesian estimation approaches and provide new and easy-to-use R functions for their implementation. We use simulation studies to evaluate the ability of the proposed index to reflect selection bias in non-probability samples and show how the index outperforms a previously proposed index that relies on an underlying normality assumption. We demonstrate the use of the index in practice with real data from the National Survey of Family Growth.

SUBMITTER: Andridge RR

PROVIDER: S-EPMC7724611 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Indices of non-ignorable selection bias for proportions estimated from non-probability samples.

Andridge Rebecca R RR West Brady T BT Little Roderick J A RJA Boonstra Philip S PS Alvarado-Leiton Fernanda F

Journal of the Royal Statistical Society. Series C, Applied statistics 20190802 5

Rising costs of survey data collection and declining response rates have caused researchers to turn to non-probability samples to make descriptive statements about populations. However, unlike probability samples, non-probability samples may produce severely biased descriptive estimates due to selection bias. The paper develops and evaluates a simple model-based index of the potential selection bias in estimates of population proportions due to non-ignorable selection mechanisms. The index depen ...[more]

PMID: 33304001

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Dataset Information

Indices of non-ignorable selection bias for proportions estimated from non-probability samples.

Publications

Indices of non-ignorable selection bias for proportions estimated from non-probability samples.

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