By now, most people have heard of the gig economy and have some idea of how it works. In the gig economy, people perform short-term jobs or tasks to earn money. Gig economy jobs are considered independent or contract work, meaning people who work in the gig economy often trade the benefits and stability of traditional employment for the freedom and flexibility to decide when and how much they work. Some of the most easily identifiable gig economy platforms are Uber, Lyft, AirBnB, and the slightly less mentioned Mechanical Turk or MTurk.
A persistent cause of concern for researchers who conduct studies online is understanding what participants might be doing while completing their study. When participants are outside the lab, they cannot be observed and distracting aspects of the environment cannot be controlled by the research team. As a result, researchers are left to wonder: how much attention are participants giving my survey?
In this blog, we report on one small aspect of this issue by describing the work style adopted by workers on Amazon’s Mechanical Turk.
Academic research is a collaborative endeavor. Faculty members work with post-docs, grad students, and undergrads. Sometimes one lab collaborates with another. During the course of such work, resources sometimes need to be shared or redistributed. At TurkPrime, we have sought to make part of this sharing easier by allowing researchers to transfer funds from one user’s lab balance to another. In this blog, we demonstrate how to use this feature.
One reason Amazon Mechanical Turk has become so popular among researchers is the speed with which data can be collected. Compared to more traditional research methods—lab-based experiments, field studies, ethnographic interviews, etc—MTurk is exceptionally fast, making it possible to collect data for an entire study within a day or sometimes just a few hours. Although MTurk’s speed is nice, there are times when collecting data all at once can actually be a problem. In this blog, we explain how to spread your data collection out across time and why you might want to do so.
Three weeks ago, we published a blog explaining five things you should be doing in your online data collection. In this blog, we follow up with five things you should NOT be doing when collecting data on MTurk.
Researchers are responsible for being an expert, or at least knowledgeable, in several areas. There’s the topic of your research, the methods common within your discipline, best practices for open science, and the mediums used to communicate about your work—just to name a few. For many researchers, online data collection has been revolutionary, helping collect data faster and more affordably than ever before. Yet, with the emergence of online research there is now one more domain to be an expert in. Given the steep learning curve for really learning how to best run online studies, we put together this blog to highlight five practices that if you’re not already doing in your online research, you should be. These practices primarily apply to online research on Amazon’s Mechanical Turk when using TurkPrime’s MTurk Toolkit, but some practices can be applied to other platforms as well.
At TurkPrime, we advocate for requesters to treat workers fairly when posting HITs on Amazon’s Mechanical Turk (MTurk). Workers are, after all, the people who make the research possible. Sometimes situations arise in which an MTurk worker is unable to receive payment, despite having completed a survey. Below are two common scenarios in which a worker may not be paid, despite completing a survey:
- We collected high quality data on MTurk when using TurkPrime’s IP address and Geocode-restricting tools.
- Using a novel format for our anchoring manipulation, we found that Turkers are highly attentive, even under taxing conditions.
- After querying the TurkPrime database, we found that farmer activity has significantly decreased over the last month.
- When used the right way, researchers can be confident they are collecting quality data on MTurk.
- We are continuously monitoring and maintaining data quality on MTurk.
- Starting this month, we will be conducting monthly surveys of data quality on Mechanical Turk.
Last week, the research community was struck with concern that “bots” were contaminating data collection on Amazon’s Mechanical Turk (MTurk). We wrote about the issue and conducted our own preliminary investigation into the problem using the TurkPrime database. In this blog, we introduce two new tools TurkPrime is launching to help researchers combat suspicious activity on MTurk and reiterate some of the important takeaways from this conversation so far.