researchers found that MT participants are not anonymous, as
their unique ID number can be used to identify them across
any Amazon property, including revealing their user profiles,
wish lists and product reviews (Lease et al., 2013). Revelation
of this fact left researchers and IRBs scrambling with respect
to protocols that had been previously approved as exempt and
consent forms that assured anonymity.
Concerns about participant privacy and anonymity can
arise in other ways, namely due to identifying information
that online survey tools collect by default, such as Internet
protocol addresses, geographical coordinates and email
addresses. When researchers and IRBs are unaware that such
information is collected automatically, erroneous decisions
about participant privacy may lead to false guarantees of
anonymity and confidentiality to participants. Knowledge of
data security (or lack thereof) is equally critical in this regard.
To fully evaluate the level of privacy for a study, researchers
and IRBs need to know exactly how an online research service
works with respect to safeguards for secure data storage, who
has access to the data at any given time (researchers, employees
of the service, et al.) and the means of data transmission both
when participants are performing an experiment and when
researchers are retrieving their data (Buchanan & Hvizdak,
2009). Of course, even once all those factors are taken into
account, the specter of hacking and data theft remains, while
admittedly on the extreme end of concerns.
Equally worrisome is the possibility of human error.
Unfortunately, that happened to a colleague when an Internet
survey provider inadvertently allowed research participants’
names, phone numbers and email addresses to become public
for a short time. While, thankfully, no survey data were
displayed, some participants did report that they received
more spam and unwanted contact. Although this type of
situation falls into the unanticipated problem category, it
nonetheless raises the issue of what researchers are responsible
for when they take their studies online.
Efforts for change
Identifying and managing that responsibility are tricky in a
research landscape that is relatively young and ever-changing.
Questions about what researchers should know and convey to
subjects do not have clear answers when one tries to apply the
existing ethical guidelines and regulations to circumstances
they were not designed to address. However, efforts to
change that are underway. For example, the Secretary’s
Advisory Committee on Human Research Protections of
the U.S. Department of Health and Human Services has
drafted an advisory document titled “Considerations and
Recommendations concerning Internet Research and Human
Subjects Research Regulations” ( www.hhs.gov/ohrp/sachrp/
commsec/attachmentbsecletter20.pdf), which serves as a nice
primer for those engaging in online research.
Collaboration between researchers and IRBs with their
local information technology and security experts is also
advisable to keep abreast of technological and security issues
relevant to research. In addition, consent forms should be
written to inform participants of the vagaries of online
• Buchanan, E. A. & Hvizdak, E. E. (2009).
Online survey tools: Ethical and methodological
concerns of human research ethics committees.
Journal of Empirical Research on Human
Research Ethics, 4( 2), 37–48.
• Lease, M., Hullman, J., Bigham, J.P.,
Bernstein, M. S., Kim, J., Lasecki, W. S., …
Miller, R. C. (2013). Mechanical Turk is not
anonymous. In Social Science Research
Network (SSRN) Online.
• Mason, W. & Suri, S. (2012). Conducting
behavioral research on Amazon’s Mechanical
Turk. Behavior Research Methods, 44( 1), 1–23.
• Paolacci, G., Chandler, J., & Ipeirotis, P.
G. (2010). Running experiments on Amazon
Mechanical Turk. Judgment and Decision
Making, 5( 5), 411–419.
• Pennsylvania State University (2007).
Guidelines for computer- and internet-based
research involving human participants.
Retrieved from www.research.psu.edu/policies/
• Schadt, E. E. (2012). The changing privacy
landscape in the era of big data. Molecular
Systems Biology, 8(612), 1–3.
• Shapiro, D. N., Chandler, J., & Mueller, P. A.
(2013). Using Mechanical Turk to study clinical
populations. Clinical Psychological Science, 1,