Many participant pools have their own selection biases, each with their own benefits and drawbacks. Consider for instance, the vast majority of published research studies are done using college undergraduate students, who are themselves a biased sample: above average education and affluence, late-teen to early-adolescent demographic, restricted by geography, etc. Similarly, Amazon Mechanical Turk has its own bias; people who are motivated for a variety of reasons to perform micro-tasks in an online environment. However, the tradeoff researchers receive include a wider variations in age, education, income and country of residence.
Articles in this section
- What is the best way to ensure my study is completed quickly and accurately?
- Where can I find information on the demographics of Amazon Mechanical Turk workers?
- Can I measure reaction time?
- What is Amazon Mechanical Turk (AMT) and how does it work?
- Do I have to manage participants from AMT?
- Who protects the personal information of AMT workers?
- Can I discover who my participants are?
- How can I be sure my data is secure? Where is the data stored?
- Can I trust the quality of data from AMT?
- I'm satisfied with the way I do research. Why would I use AMT?