The internet has the reputation of being a place where people can hide in anonymity, and present as being very different people than who they actually are. Is this a problem on Mechanical Turk? Is the self-reported information provided by Mechanical Turk workers reliable? These are important questions which have been addressed with several different methods. Researchers have examined a) consistency of responding to the same questions over time and across studies b) the validity of responses, or the degree to which the items capture responses that represent the truth from participants. It turns out that there are certain situations in which MTurk workers are likely to lie, but they are who they say they are in almost all cases.
Hundreds of academic papers are published each year using data collected through Mechanical Turk. Researchers have gravitated to Mechanical Turk primarily because it provides high quality data quickly and affordably. However, Mechanical Turk has strengths and weaknesses as a platform for data collection. While Mechanical Turk has revolutionized data collection, it is by no means a perfect platform. Some of the major strengths and limitations of MTurk are summarized below.
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You are running a longitudinal study and have identified 1000 workers who you want to allow to take your second phase studies. How do you easily group those workers for easy access.
Or you want to exclude certain workers from taking a number of your studies and wish to group them for easy exclusion in future studies. How can you do that?
Suppose you need to run a group of HITs open only to participants who are women under 50. You previously ran a HIT and know the Worker IDs that you want to reach, but have no way to email them and limit your survey to only them. How can you proceed?