Table 1.
Level of Intervention | Type of Intervention | Method of Detection | Method of Prevention | Pros | Cons | Additional Ethical Issues |
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Questionnaire/Instrument | Questions in Survey | Inconsistent Reponses | Check for proper/consistent answers |
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Subjects may skip questions because of discomfort | |
Include same/similar/strange questions throughout study | Indicates level of attention | Can impact experimental design | ||||
Include questions of social desirability | Possibly help assess personality traits associated with providing inaccurate responses |
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Software for Administering Survey | No back button | Subjects can’t easily resubmit survey |
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Change order of questions with each administration |
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CAPTCHA* | Detects “bots” | |||||
Collect paradata (i.e., subject’s behavior, e.g., time stamp, how mouse moved on the screen) | Examines how subject responding to survey | Programs that allow tracking of paradata are costly | Ethical questions of what we can see with paradata – whether to disclose to participants what we can see of their behavior | |||
Tracking Non-Questionnaire Data | Personal Information | Similar/same email, username, password between “different” participants | Contact participant about “red flag,” and if no response, remove from study | Clears up misunderstandings |
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Needs to balance protecting integrity of data and subject privacy and confidentiality are particularly important |
Inaccurate/fake address & phone numbers | Researchers request to provide phone number/address to get through registration process | Participants need valid number in order to proceed | “Fraudsters” can create temporary phone numbers | |||
Check whether person, address, phone number is valid (through Facebook, whitepages.com, etc.) |
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Ask participants for a website where they are listed (e.g., Facebook) | May deter “fraudsters” and multiple submissions |
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Computer Information | IP Addresses | Same IP as another participant | Check whether IP address is the same or if it is encrypted | Can determine how many times participants took survey and whether participant fulfills location criteria (i.e., living in US) |
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Block IP address if participant is ineligible | Avoids “fraudsters” from participating | Could be dynamic IP address and not ineligible participant | ||||
Internet Cookies | Cookies detecting completion of study and multiple attempts access study | Enable cookies | Can detect multiple submissions by tracking the progress/completion of study |
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Tracking Survey URL | URL posted in unintended locations | Tracking/Googling URL on Internet | Can see if website where URL located is targeting proper audience | Doesn’t prevent “frausters” taking study multiple times | ||
Provide link in email to website and track referring URL |
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Researchers don’t always know the targeted population | ||||
Study Design | Informed Consent | Break up consent online and only provide compensation information at the end of all the forms |
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May deter eligible pa1rticipants | ||
Compensation | Many gift certificates mailed to same address | Mention that subjects will not be compensated if suspect of fraudulent behavior | Avoids paying “fraudsters” yet keeps incentive | |||
Only inform participants of their eligibility for the study after survey |
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Ask for mailing address (vs. email address) and verify addresses | Deters ineligible participants if researchers have means to verify addresses | May deter eligible participants (because of need to provide personal information) | ||||
Check if multiple gift certificates are being sent to one location | Can avoid paying participants if suspected of fraudulent behavior yet keeps incentive | Linking identification to data can threaten confidentiality | ||||
De-incentivize fraud by paying less and/or emphasizing research and the importance of social/community costs of fraud | Potential “fraudsters” may be persuaded not to skew results |
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Provide lottery for compensation (do not pay every person) | Gives researchers time to review and determine “fraudsters” before compensating |
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Including Interview | See whether subject already participated and/or is lying on responses | Audio Interview |
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Needs to balance protecting integrity of data and subject privacy and confidentiality are particularly important | |
Skype/”face-to-face” Interview | ||||||
IRBs | IRB Structure | Having an online/computer expert as a member of the IRB |
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Does not deter “fraudsters” from taking survey multiple times | ||
Have PIs Report Information on “Fraudsters” to IRB | IRBs can follow and monitor to make appropriate decisions for current and future studies | May deter “fraudsters” from participating |
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Broader Regulatory and other Entities | Reporting Information on “Fraudsters” | PIs create “fraudster” list for other PIs and share information |
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“Fraudsters” can create new names, emails, IP addresses for each study to avoid detection as a “fraudster” | Possible harm of individuals are incorrectly classified as “fraudsters” and reported externally? Need to ensure that characterization as “fraudster” is accurate | |
Reporting fraudulent behavior to Internet Crime Complaint Center (IC3.gov), OHRP or funders | May deter “fraudsters” from participating |
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Completely Automated Public Turing test to tell Computers and Humans Apart