Challenges and Lessons Learned in Managing Web-Based Survey Fraud for the Garnering Effective Outreach and Research in Georgia for Impact Alliance–Community Engagement Alliance Survey Administrations
Notice bibliographique
Résumé
Background: Convenience, privacy, and cost-effectiveness associated with web-based data collection have facilitated the recent expansion of web-based survey research. Importantly, however, practical benefits of web-based survey research, to scientists and participants alike, are being overshadowed by the dramatic rise in suspicious and fraudulent survey submissions. Misinformation associated with survey fraud compromises data quality and data integrity with important implications for scientific conclusions, clinical practice, and social benefit. Transparency in reporting on methods used to prevent and manage suspicious and fraudulent submissions is key to protecting the veracity of web-based survey data; yet, there is limited discussion on the use of antideception strategies during all phases of survey research to detect and eliminate low-quality and fraudulent responses. Objective: This study aims to contribute to an evolving evidence base on data integrity threats associated with web-based survey research by describing study design strategies and antideception tools used during the web-based administration of the Garnering Effective Outreach and Research in Georgia for Impact Alliance-Community Engagement Alliance (GEORGIA CEAL) Against COVID-19 Disparities project surveys. Methods: GEORGIA CEAL was established in response to the COVID-19 pandemic and the need for rapid, yet, valid, community-informed, and community-owned research to guide targeted responses to a dynamic, public health crisis. GEORGIA CEAL Surveys I (April 2021 to June 2021) and II (November 2021 to January 2022) received institutional review board approval from the Morehouse School of Medicine and adhered to the CHERRIES (Checklist for Reporting Results of Internet E-Surveys). Results: A total of 4934 and 4905 submissions were received for Surveys I and II, respectively. A small proportion of surveys (Survey I: n=1336, 27.1% and Survey II: n=1024, 20.9%) were excluded due to participant ineligibility, while larger proportions (Survey I: n=1516, 42.1%; Survey II: n=1423, 36.7%) were flagged and removed due to suspicious activity; 2082 (42.2%) and 2458 (50.1%) of GEORGIA CEAL Surveys I and II, respectively, were retained for analysis. Conclusions: Suspicious activity during GEORGIA CEAL Survey I administration prompted the inclusion of additional security tools during Survey II design and administration (eg, hidden questions, Completely Automated Public Turing Test to Tell Computers and Humans Apart verification, and security questions), which proved useful in managing and detecting fraud and resulted in a higher retention rate across survey waves. By thorough discussion of experiences, lessons learned, and future directions for web-based survey research, this study outlines challenges and best practices for designing and implementing a robust defense against survey fraud. Finally, we argue that, in addition to greater transparency and discussion, community stakeholders need to be intentionally and mindfully engaged, via approaches grounded in community-based participatory research, around the potential for research to enable scientific discoveries in order to accelerate investment in quality, legitimate survey data.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,266 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».