ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2019
Indices of non-ignorable selection bias for proportions estimated from non-probability samples.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Generalizing randomized trial findings to a target population using complex survey population data.Statistics in medicine · 2021Trial
- A Sensitivity Analysis Framework Using the Proxy Pattern-Mixture Model for Generalization of Experimental Results.Statistics in medicine · 2025Article
- Analyzing Potential Non-Ignorable Selection Bias in an Off-Wave Mail Survey Implemented in a Long-Standing Panel Study.Journal of survey statistics and methodology · 2025Article
- Exploring the Big Data Paradox for various estimands using vaccination data from the global COVID-19 Trends and Impact Survey (CTIS).Science advances · 2024Article
- The unmet need for mental health support among persons with disabilities in Somalia: Principal correlates and barriers to access.Global mental health (Cambridge, England) · 2024Article
- Evaluating Pre-election Polling Estimates Using a New Measure of Non-ignorable Selection Bias.Public opinion quarterly · 2023Article
- ASSESSING SELECTION BIAS IN REGRESSION COEFFICIENTS ESTIMATED FROM NONPROBABILITY SAMPLES WITH APPLICATIONS TO GENETICS AND DEMOGRAPHIC SURVEYS.The annals of applied statistics · 2021Article
- Social Capital Changes After COVID-19 Lockdown Among Youths in China: COVID-19 Impact on Lifestyle Change Survey (COINLICS).Frontiers in public health · 2021Article
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Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.