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Selection Bias in Web Surveys
Open accessAbstract
Summary: At first sight, web surveys seem to be an interesting and attractive means of data collection. They provide simple, cheap, and fast access to a large group of potential respondents. However, web surveys are not without methodological problems. Specific groups in the populations are under‐represented because they have less access to Internet. Furthermore, recruitment of respondents is often based on self‐selection. Both under‐coverage and self‐selection may lead to biased estimates. This paper describes these methodological problems. It also explores the effect of various correction techniques (adjustment weighting and use of reference surveys). This all leads to the question whether properly design web surveys can be used for data collection. The paper attempts to answer this question. It concludes that under‐coverage problems may solve itself in the future, but that self‐selection leads to unreliable survey outcomes.
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“The reliability and validity of web-based self-reported mental disorder symptom clusters remains ambiguous,71 certainly for the current population”
From Mental Disorder Symptoms among Public Safety Personnel in Canada (2018) · cites this paper as [71]
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“In contrast, the current rates may be inflated because of a self-selection bias of motivated responding by clinically symptomatic persons.71”
From Mental Disorder Symptoms among Public Safety Personnel in Canada (2018) · cites this paper as [71]
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do NASCIMENTO, W. G., VIEIRA, R. B., da Silva, R. M. G., SOARES, A. C. S., & Pinto, F. R. M. (2017) · Revista on line de Política e Gestão Educacional · doi:10.22633/rpge.v21.n.esp2.2017.10192 · Cited by 1
Conducting Online Crime and Safety Surveys with British Farmers
Smith, K. (2021) · International Criminal Justice Review · doi:10.1177/10575677211042262 · Cited by 1
“Such a situation would lead to self-selection bias where your sample may no longer represent the target population (Khazaal et al., 2014) with some researchers arguing that self-selection through online surveys may lead to unreliable survey outcomes (Bethlehem, 2010).”
BackgroundJemielniak, D. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780198839705.003.0004 · Cited by 1
Chen, Y., Han, Y., He, Y., & Xu, J. (2022) · Proceedings of the 2022 2nd International Conference on Modern Educational Technology and Social Sciences (ICMETSS 2022) · doi:10.2991/978-2-494069-45-9_94 · Cited by 1
Yin, E., Cameron, A. J., Schultz, S. M., White, C. M., Vanderlee, L., Hammond, D. G., & Sacks, G. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20054054 · Cited by 1
“Respondents were recruited using nonprobability-based sampling from a commercial panel, meaning that despite the national sample, the findings should not be presumed to provide nationally representative estimates [61,62].”
MethodFranco, G. D. (2023) · Quaderni dell Osservatorio elettorale QOE - IJES · doi:10.36253/qoe-13782 · Cited by 1
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The public perception of urban vegetation in metropolitan regions of Costa Rica
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Postpartum Burnout Among Women of Childbearing Age: A Neglected Global Public Health Problem
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Chiang, V., Guirguis, L. M., Warren, A. L., Topps, D., Anderson, S., & Hecker, K. G. (2023) · Exploratory Research in Clinical and Social Pharmacy · doi:10.1016/j.rcsop.2023.100357 · Cited by 1
“52 In conclusion, the above study describes a sample of pharmacists in Alberta being assessed using a survey of variety of common clinical vignettes.”
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MindSet: A Bias-Detection Interface Using a Visual Human-in-the-Loop Workflow
Kalananthan, S., Kichutkin, A., Shang, Z., Strausz, A., Bautiste, F. J. S., & El‐Assady, M. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-50485-3_8 · Cited by 1
Depressive symptoms and their correlates among predoctoral dental students in the United States
Finkelman, M., Joseph, A., Khoynezhad, S., & Bordin, T. B. (2024) · Journal of Dental Education · doi:10.1002/jdd.13492 · Cited by 1
Cisel, M. (2024) · Open Learning The Journal of Open Distance and e-Learning · doi:10.1080/02680513.2024.2345118 · Cited by 1
Device effects: Results from choice experiments in an agritourism context
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“Self-selection bias has been reported in online survey studies (Bethlehem, 2010; Greenacre, 2016).”
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Qiu, H. S., Zhao, Z. H., Yu, T. K., Dabbish, L., & Vasilescu, B. (2024) · Apress eBooks · doi:10.1007/978-1-4842-9651-6_14 · Cited by 1
Bringing local food education to workplaces: Assessing needs for a health and wellness program
Gusto, C., Campbell, C. G., Wallau, A., & Wood, W. (2024) · Journal of Agriculture Food Systems and Community Development · doi:10.5304/jafscd.2024.134.013 · Cited by 1
Review of Developmental Studies on Children in Infancy, Early, and Middle Childhood:
Fujisawa, K. K. (2024) · The Annual Report of Educational Psychology in Japan · doi:10.5926/arepj.63.1 · Cited by 1
Metadata
- Authors
- Bethlehem, J.
- Affiliations
- Centraal Bureau voor de Statistiek (NL)
- Corresponding author
- Bethlehem, Jelke
- Venue
- International Statistical Review
- Publisher
- Wiley
- Published
- 2010-08
- Volume
- 78
- Issue
- 2
- Pages
- 161-188
- ISSN
- 0306-7734 (ISSN-L)
- 1751-5823
- OpenAlex
- W2129332419
- Cited by
- 1,315
- References
- 37
- Citations by year
- 2026: 228 · 2025: 173 · 2024: 151 · 2023: 149 · 2022: 122 · 2021: 144 · 2020: 109 · 2019: 47 · 2018: 38 · 2017: 54 · 2016: 20 · 2015: 32 · 2014: 19 · 2013: 14 · 2012: 6
- FWCI
- 29.95
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Statistical Review (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Census and Population Estimation
- Keywords
- online surveys, selection bias, undercoverage, self-selection, internet access, survey design, participant recruitment
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