Paid studies help businesses and medical professionals make informed decisions based on real-world data and feedback. If that sounds broad-- it's because as a category, paid studies vary widely, referring to 5 minute questionnaires or surveys to rigorous, long-term observational and experimental studies.
Paid studies fit into three main categories:
➡️ Descriptive Studies: (Qualitative surveys, questionnaires, and focus groups)
➡️ Observational Studies: (Longer-term observational studies on eating, exercise, or smoking habits etc.)
➡️ Experimental Studies: (New medicines, vaccines, treatment methods, or diagnostic tests etc. )
But what incentives do institutions have to spend extreme amounts of money on recruiting volunteers for their studies. Is qualitative data from a survey really that valuable to a business? Aren't there enough sick people in need of medicines and treatment? Why would anyone pay money to recruit healthy volunteers to try a new medicine or treatment?
The answers to these questions are all grounded in the predictive power of a well-designed, sound use of statistics.
How Does Research Work? The Basics.
Consider that every new medicine, diagnostic test, or treatment method at one point begins as a question– is a vaccine booster shot safe? Is 1 hour/day of exercise effective in combating depression? Do iPhone Users prefer a metal or plastic backing? It’s from questions like these that researchers design experiments to test their assumptions and then make predictive conclusions about how something will perform.
For example, think back to when you were young and claiming to be sick to stay home from school. Your parent(s) were then faced with a simpler question:
❓Question: Is my child really sick?
They then might feel your forehead for warmth, or ask you questions about how you feel. Based on their initial impression– they form an assumption, sometimes fancily called a hypothesis, that you either are or are not sick. Perhaps your parents are skeptical:
🤔 Hypothesis: My child's forehead is very warm, and thus is probably sick.
Your parents may then test their assumption by taking your temperature with a thermometer– if your internal temperature is above/below regular 98.6 temperature they will make a conclusion:
✅ Result: The temperature is 99.6 F, my kid has a fever.
From the result of their test , your parents will make the informed decision, or conclusion to let you stay home from school:
🏁 Conclusion: My child should stay home from school today.
When the same, simple procedure of rigorously testing a question is applied at scale on multiple participants– the informed decisions and conclusions become exponentially more powerful and predictive.
One sick child who runs a fever and happens to be sick tells us little about whether or not that is an effective diagnostic tool. On the other hand, imagine that a school nurse ran a study on 100 students who ran a fever above 98.6 F and that 90 of those students tested positive for some illness. They might conclude that having a fever is 90% effective in predicting sickness.
Similarly, medical professionals can use statistics to derive confidence intervals around the safety and efficacy of new treatments. Perhaps the FDA claims a vaccine is 97% effective. Essentially, that means that if you were to treat 100 random people, at most, 3 people would not be protected by the treatment.
And while these examples are a gross simplification of the analysis researchers do-- they provide a clear example of the utility of large-scale studies and trials. And as the sample size increases-- the more accurate and precise the predictions.
Who stands to gain?
So getting back to our initial question of why these paid studies exist– what reasons do institutions have to pay participants volunteering in these studies? How could these paid opportunities possibly be legitimate?
In the case of paid research studies-- the economic saying “One incentive leads to another” is especially true. Clinicians and researchers are aware there is an immense financial and societal incentives to develop new medicines.
If a new treatment passes the rigorous phases of tests required by governments, the researchers could receive a large financial return when they go to market-- sometimes millions of dollars. Although, the success rate is rather low and this process can take years to play out.
Due to the perceived risks of trying new medicines and scarcity of the average person’s time, it can be difficult or challenging for researchers to recruit willing volunteers. For both of these reasons, researchers tend to offer cash incentives to participants and reduce dropout rate.
The amount of money researchers offer in compensation is relative to the time commitment required and difficulty in recruiting participants. This amount is often planned out during the experimental design phase and can be set based on budgetary constraints. Compensation amounts can range from as much as $15 - $100 per hour depending on the study.
It’s important not to conflate the compensation amount with a level of risk. Every study comes with its unique set of risks and rewards. Understanding these is a minimum requirement to participate in all studies, for more information read about Informed Consent. It is always important to understand the gravity and significance of informed consent when considering participating in a paid study.
Participating in paid studies has never been easier, see our comprehensive list of studies going on in your area. Or sign up for notifications when a new study is launched in your area.