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Does Self-Selection Affect Samples’ Representativeness in Online Surveys? An Investigation in Online Video Game Research
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BACKGROUND: The number of medical studies performed through online surveys has increased dramatically in recent years. Despite their numerous advantages (eg, sample size, facilitated access to individuals presenting stigmatizing issues), selection bias may exist in online surveys. However, evidence on the representativeness of self-selected samples in online studies is patchy. OBJECTIVE: Our objective was to explore the representativeness of a self-selected sample of online gamers using online players' virtual characters (avatars). METHODS: All avatars belonged to individuals playing World of Warcraft (WoW), currently the most widely used online game. Avatars' characteristics were defined using various games' scores, reported on the WoW's official website, and two self-selected samples from previous studies were compared with a randomly selected sample of avatars. RESULTS: We used scores linked to 1240 avatars (762 from the self-selected samples and 478 from the random sample). The two self-selected samples of avatars had higher scores on most of the assessed variables (except for guild membership and exploration). Furthermore, some guilds were overrepresented in the self-selected samples. CONCLUSIONS: Our results suggest that more proficient players or players more involved in the game may be more likely to participate in online surveys. Caution is needed in the interpretation of studies based on online surveys that used a self-selection recruitment procedure. Epidemiological evidence on the reduced representativeness of sample of online surveys is warranted.
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“Self-selection was a key factor in the participant selection process due to the increased likelihood for participants to demonstrate (a) a noticeable interest in the topic, (b) willingness to contribute to the goals of the study, and (c) a higher likelihood of expertise/understanding of the topic (Khazaal et al. 2014).”
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Metadata
- Authors
- Khazaal, Y., van Singer, M., Chatton, A., Achab, S., Zullino, D., Rothen, S., Khan, R., Billieux, J., & Thorens, G.
- Affiliations
- University Hospital of Geneva (CH)
- University of Geneva (CH)
- Corresponding author
- Khazaal, Yasser
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2014-07-07
- Volume
- 16
- Issue
- 7
- Pages
- e164
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC4115258
- OpenAlex
- W2081232991
- Cited by
- 201
- References
- 26
- Citations by year
- 2026: 12 · 2025: 12 · 2024: 21 · 2023: 16 · 2022: 19 · 2021: 17 · 2020: 22 · 2019: 15 · 2018: 18 · 2017: 17 · 2016: 11 · 2015: 18 · 2014: 3
- FWCI
- 53.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Archive ouverte UNIGE (University of Geneva) (publishedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by)
- Digital Access to Libraries (submittedVersion, other-oa)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- 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
- self-selection bias, sample representativeness, online surveys, online game players, avatars, World of Warcraft, selection bias, random sampling, guild membership, online games
- MeSH terms
- Adult, Analysis of Variance, Computer Simulation, Data Collection, Humans, Male, Video Games, Internet
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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