Overview
An IRB will generally request a description of how participants will be recruited, reimbursed and interacted with. Additionally IRBs always request information about how anonymity of the participants is protected. Members of the IRB board may not be familiar with Amazon Turk, and it may be helpful to include a brief description of MTurk in your IRB application. Note that many MTurk studies will be exempt from review, provided that the nature of MTurk is explained clearly enough, and the anonymity of the data collection process is made clear.
Below is a template that some may find helpful in getting started with their IRB form for studies that recruit participants from MTurk. This form provides a brief description of MTurk and its procedures. Please note that you need to modify this form to match the specifics of your study, and that each IRB has different requirements and may request additional information about MTurk not provided here. For those using TurkPrime, a template is provided at the bottom of this post.
Who will the participants be and how will they be selected? Where will the research be performed?
The participants will be recruited via Amazon Mechanical Turk, an online crowdsourcing platform. The purpose of Mechanical Turk (MTurk) is to help people (participants) find paid tasks.
MTurk is one of the suites of Amazon.com Web Services. In recent years MTurk had been extensively used in social science research. MTurk enables researchers to recruit participants to perform tasks such as filling out surveys, opinion polls, cognitive psychological studies, and many others. Researchers advertise their studies on MTurk, and participants chose only those studies that interest them. Participants are paid for completing the studies. Payment is transferred directly to the participants’ credit cards immediately after the completion of a study.
Amazon Turk had been extensively used by psychologists in the last few years for participant recruitment (Buhrmester, et al, 2011, Litman et al, 2014). Participants on Amazon Turk see a list of potential jobs (referred to as HITs) when they log into their MTurk account. The price is provided next to the name of the HIT along with the approximate length of time that the HIT will take. Participants are free to choose the HITs that they are interested in taking, from a long list of thousands of tasks. The name of our HIT will be “Enter the name of your HIT”. The survey takes approximately “enter approximate time complete study” minutes to complete. The participants will be paid “enter Dollar amount” for filling out the survey. Once they click on the HIT they will be taken directly to the survey (attached) which provides further information about the study. The survey/study will be hosted on “enter a platform such as Qualtric, Survey Monkey etc. ”. MTurk rules state that participants can terminate the study by returning the HIT at any time, without any penalty.
How will anonymity and confidentiality be guaranteed?
Participants on MTurk have a unique Worker ID, which is semi-random alphanumeric string. Participants’ Worker IDs are all that the researcher knows about the participants. All collected data is associated with only this Worker ID. Additionally, MTurk has several mechanisms in place to protect unauthorized access including protecting the security of information during transmission by using Secure Socket Layer (SSL) software. MTurk provides the same level of protection of personal data as for all other Amazon.com services, such as protection of credit card information.
If using TurkPrime
This study will be launched from the TurkPrime website. TurkPrime is a Mechanical Turk technology partner which provides additional tools for Mechanical Turk users. Researchers with a TurkPrime account are able to control their MTurk studies directly from their TurkPrime accounts. TurkPrime does not collect any personality identifiable information, and provides the same level of anonymity and data protection as Mechanical Turk.
References
Amazon’s Mechanical Turk : A New Source of Inexpensive, Yet High-Quality, Data? Buhrmester, M., and Gosling, SD. Perspectives on Psychological Science 2011 6:3
Litman, L., Robinson, J., & Rosenzweig, C. (2014). The relationship between motivation, monetary compensation, and data quality among US-and India-based workers on Mechanical Turk. Behavior research methods, 1-10.
