What are Dynamic rewards?

Dynamic rewards are a new way for participants to earn rewards based on the number of tasks they successfully complete. Researchers can create a single study that allows different participants to contribute in varying capacities. The rewards you earn will be adjusted to match your level of participation in the study. For example, some participants may have the opportunity to complete more tasks than others, and their compensation will reflect their greater contribution to the research.

This system helps researchers share different types of studies while making sure participants are fairly compensated for their contributions.

Why do only some studies have Dynamic Rewards?

Fair compensation is really important to us. That’s why we’re starting with only a small group of trusted researchers using Dynamic rewards. Not all studies will have them, and we’re closely monitoring this feature to ensure it follows our fair payment guidelines.

What should I expect when taking these studies?

Before you start, you’ll be told the maximum time needed to complete the study, along with the minimum and maximum rewards available. This way, you can decide if the study is right for you.

It’s important to note:

  • In some cases, the data collection might close before the estimated time is up. If this happens, you'll be paid according to the tasks you've completed.
  • Your payment is tied to the number of tasks you complete, as assigned by the researcher.
  • The length of the study might vary, so make sure you have enough time to complete the longest version, as shown in the study card.
  • You’ll only receive payment if you finish all the tasks assigned to you during the study.

Does this mean I can be underpaid for the studies I take?

No, you won’t be underpaid. You’ll always get a reward that reflects your time and effort. The difference with Dynamic rewards is that participants in the same study may have different tasks. Some might be longer, while others might be quicker, but everyone will be paid fairly based on their contribution.

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