Academic Journal
Mitigating Fraud in Online Surveys: A Methodological Approach.
| Τίτλος: | Mitigating Fraud in Online Surveys: A Methodological Approach. |
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| Συγγραφείς: | Towne KM; University of Kansas School of Nursing, USA.; University of Mount Union, USA.; Case Western Reserve University, USA., Polivka B; University of Kansas School of Nursing, USA. |
| Πηγή: | Western journal of nursing research [West J Nurs Res] 2026 Jun; Vol. 48 (6), pp. 661-669. Date of Electronic Publication: 2026 Mar 04. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Sage Publications Country of Publication: United States NLM ID: 7905435 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1552-8456 (Electronic) Linking ISSN: 01939459 NLM ISO Abbreviation: West J Nurs Res Subsets: MEDLINE |
| Imprint Name(s): | Publication: Beverly Hills, CA : Sage Publications Original Publication: [Anaheim, Calif.] Phillips-Allen. |
| Ιατρικοί όροι (MeSH): | Data Collection*/methods , Data Collection*/standards , Fraud*/prevention & control , Internet*, Surveys and Questionnaires/standards ; Research Design/standards ; Humans ; Data Accuracy |
| Περίληψη: | Background: Online surveys offer data collection benefits and pitfalls, especially when utilizing crowdsourcing platforms such as Research Match and Prolific for recruitment. Objective: The purpose of this methodology report was to describe strategies used to fortify an online REDCap research survey in which fraudulent and suspicious responses were identified and mitigated. Methods: A two-pronged approach was designed to identify initial design limitations and engage in evidence-based redesign, which included scam alert features, study design changes, survey structure improvements, and crowdsourcing platform considerations. Results: A proactive, eight-step data cleaning protocol was designed and implemented in collaboration with the institutional review board and REDCap data experts. The same fraudulent records were identified within multiple steps of data cleaning, suggesting fraudulent records demonstrate multiple suspicious indicators. Conclusion: Exciting opportunities in online data collection come with the risks of compromised data quality, resource waste, and damage to population health. Evidence-based protocols must be proactively designed to deter and detect early fraudulent results. |
| Competing Interests: | Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. |
| Contributed Indexing: | Keywords: data collection; data quality; fraud—prevention and control; internet methodology; nursing; online research; online survey; research; scam; survey fraud; web-survey design |
| Entry Date(s): | Date Created: 20260304 Date Completed: 20260710 Latest Revision: 20260710 |
| Update Code: | 20260711 |
| DOI: | 10.1177/01939459251414538 |
| PMID: | 41778284 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1552-8456 |
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| DOI: | 10.1177/01939459251414538 |