Identifying and Managing Fraudulent Participants in Online Qualitative Research.
Background: As online data collection proliferates, health care researchers face challenges with fraudulent research participants. While substantial literature on preventing and detecting fraudulent responses in surveys is available, a dearth of nursing literature focuses on identifying and addressi...
| Publicado en: | Nursing Research Vol. 75; no. 3; pp. 214 - 220 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
May/Jun2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=193171752&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193171752 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00296562 1HA jtl: Nursing Research issn: 00296562 maglogo: N pubinfo: dt: May/Jun2026 vid: 75 iid: 3 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 193171752 193171752 193171752 10.1097/NNR.0000000000000886 193171752 ppf: 214 ppct: 6 formats: tig: atl: Identifying and Managing Fraudulent Participants in Online Qualitative Research. aug: au: Crossen, Emily M. Harper, Mary G. Maloney, Patsy affil: Emily M. Crossen, PhD, RN, NPD-BC, is a Senior Professional Development Specialist, Clinical Education, Informatics, Practice, and Quality, Boston Children's Hospital, Boston, MA sug: subj: Fraud Prevention and Control Research Subjects Research Personnel Psychosocial Factors Qualitative Studies Internet Human Random Sample Descriptive Research Focus Groups Conceptual Framework Semi-Structured Interview Interview Guides Descriptive Statistics Questionnaires Research, Nursing ab: Background: As online data collection proliferates, health care researchers face challenges with fraudulent research participants. While substantial literature on preventing and detecting fraudulent responses in surveys is available, a dearth of nursing literature focuses on identifying and addressing fraudulent participation in online qualitative studies. Objectives: The aims of this article are to highlight the researchers' experience with fraudulent research participants in an online qualitative study, enhance awareness of imposter participants, and propose strategies for the identification of imposters' participation that threatens research validity. Methods: A qualitative, descriptive study was designed to explore nursing professional development (NPD) specialists' use of NPD practice judgment. Participants were recruited for online focus groups via email using a national association's email list. A $25 gift card was offered to participants. Suspected fraudulent participants led to the cancellation of focus groups and prompted a review of initial recruitment email responses. A framework for detecting fraudulent responses was developed based on this review. Results: Out of 2,368 volunteers, 28 were invited to participate in 4 focus groups. During the initial focus group, participants did not use their cameras despite the facilitator's request, and they failed to provide meaningful responses. These suspicious actions prompted the facilitator to end the focus group. Subsequently, the researchers investigated the phenomenon of imposter participants in qualitative research and analyzed their respondents' applications for signs of fraud. Indications of fraudulent responses to the call for volunteers included an unexpectedly large number of applications received in rapid succession, Gmail addresses with similar naming conventions, and common IP addresses originating outside the United States. Discussion: Examination of recruitment survey responses, including volume, timing, and email structure, suggests potential "bot" use for volunteer survey completion. Qualitative researchers must implement measures to prevent fraudulent participants, enhance data scanning protocols to detect suspicious responses, and exclude fraudulent data to maintain research integrity. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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