The limitations of online surveys
Open accessAbstract
Online surveys are growing in popularity, perhaps because they are an easy, convenient, and inexpensive means of data collection. Online surveys commonly suffer from two serious methodological limitations: the population to which they are distributed cannot be described, and respondents with biases may select themselves into the sample. Research is of value only when the findings from a sample can be generalized to a meaningful population. When the population addressed by the survey cannot be described, and when the sample is contaminated by respondents with biases, findings from online surveys cannot be generalized and may therefore mislead.
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There is a possibility of self-selection bias because the study had no control over sample selection, potentially introducing bias towards individuals more interested or knowledgeable about the topic.
“Firstly, there is a possibility of self-selection bias [1] because we had no control over sample selection. This may introduce a bias towards individuals who are more interested or knowledgeable about the topic.”
From My Code Is Less Secure with Gen AI: Surveying Developers' Perceptions of the Impact of Code Generation Tools on Security (2025) · cites this paper as [1]
The Online Surveys section says that respondents who are sufficiently interested in the subject may select themselves into a sample and skew its findings. The Concluding Notes likewise warn about voluntary participation and self-selection. Our copy does not say that respondents may be more knowledgeable about the topic. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe previous studies' anonymous online samples may be unrepresentative because the population to which the survey was distributed cannot be described.
“For one, the population to which the survey was distributed cannot be described; this means that the respondents may be unrepresentative in some meaningful way (Andrade, 2020).”
From Content Moderator Mental Health and Associations with Coping Styles: Replication and Extension of Previous Studies (2025) · cites this paper as Andrade, 2020
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Research works printed but not linked: 3
- [1]Evans JR Mathur A. The value of online surveys. Internet Res 2005; 15: 196– 219.
- [3]Ameen S Praharaj SK. Problems in using WhatsApp groups for survey research. Indian J Psychiatry 2020; 62: 327– 328. PMC7368450 32773879
- [4]Menon V Muraleedharan A. Internet-based surveys: relevance, methodological considerations and troubleshooting strategies. Gen Psychiatry 2020; 33: e100264. PMC7398086 32818170
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“For one, the population to which the survey was distributed cannot be described; this means that the respondents may be unrepresentative in some meaningful way (Andrade, 2020).”
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- Authors
- Andrade, C.
- Affiliations
- National Institute of Mental Health and Neurosciences (IN)
- Corresponding author
- Andrade, Chittaranjan
- Venue
- Indian Journal of Psychological Medicine
- Publisher
- SAGE Publications
- Published
- 2020-10-13
- Volume
- 42
- Issue
- 6
- Pages
- 575-576
- ISSN
- 0253-7176 (ISSN-L)
- 0975-1564
- PMC
- PMC7735245
- OpenAlex
- W3096081831
- Cited by
- 618
- References
- 0
- Citations by year
- 2026: 93 · 2025: 138 · 2024: 148 · 2023: 125 · 2022: 90 · 2021: 23 · 2020: 1
- FWCI
- 80.26
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Indian Journal of Psychological Medicine (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Keywords
- online surveys, sampling bias, self-selection bias, generalizability, survey methodology, respondent selection, population representativeness
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