Uncovering the types of bias in research: Identification, prevention and examples
Have you ever heard the saying "to err is human"? Well, when it comes to research, this statement couldn't be truer. Researchers aren't immune to biases that can seep into their studies, despite their best intentions. And to clarify, they can affect both qualitative and quantitative research studies. These biases can be unintentional or subconscious, but the impact can be significant.
That's why understanding these biases is essential for producing high-quality research. If you want to keep bias to a minimum, read on!
Addressing bias in your research is critical. In online market research, for example, bias can manifest in several ways. This includes self-selection bias, question order bias and confirmation bias. All of which can lead to inaccurate results that don't reflect the population you’re studying.
When a market research survey has a self-selection bias, participants who have a stronger interest in the product will choose to take part.
Question order bias could inadvertently influence participants' responses. The way you order questions in a survey can affect how people answer later questions.
Confirmation bias could cause researchers to move away from a neutral analysis. Instead, they would interpret the results in a way that affirms their expectations.
We saw three types of bias in the previous example, but there are many more. To help you understand how they may affect your own research, here's how they can creep up:
Before you embark upon your next research project, be sure to look out for these signs that could show the presence of bias in a questionnaire:
Leading questions: Queries that suggest a particular answer can lead participants to respond in a certain way. For example, "Don’t you agree that [X issue] is a serious problem in our society?” assumes that the participant agrees with the statement and is encouraging them to confirm their agreement.
Vague survey questions: If you ask a vague question, respondents may give inconsistent or unreliable answers. "What are your thoughts on our software?" is an example of a broad question that could lead to different interpretations. These depend on the person's mood, as well as their needs and expectations at the time. A better approach would be to ask about a more specific element or feature.
Double-barreled questions: These questions ask two things at once. This makes them difficult for participants to answer. For example, "How satisfied are you with our website's design and functionality?" This question combines two separate concepts into one question. In the end, this may lead respondents to answer based on their opinion of either design or functionality, rather than both.
Absolute survey questions: These questions ask for a clear and certain answer from the participants. However, these questions can be problematic because they assume that the participants have all the necessary information to give a definite answer. For instance, the question "Do you always recycle?" assumes that the participant never fails to recycle, which may not be entirely accurate.
Acquiescence bias questions: These biased questions contain phrasing that encourages participants to agree. For example, "Did our product work?". This question may lead a respondent to say yes, even if the product didn't work in their specific situation — just because it did technically work.
After conducting your quantitative or qualitative research, look for signs of potential bias:
As a researcher, you have a responsibility to remain objective and unbiased during all stages of your research. But again, as a human being, you’re prone to biases. It’s important to be aware of this and continually check for bias throughout the process of research.
During planning, you may set out to explore a certain topic and not be open to other approaches. This could lead to bias in your research design. But if you keep your mind open and explore similar research projects, other alternatives may come to light.
Now, data collection and analysis can be tricky. That's because, again, you might be so invested in your initial research proposal that you don't see other possibilities. To avoid this, try to be as objective as possible when looking at the data and consider alternative explanations for what you see.
Online research can be tricky, and minimizing bias is just one of the many considerations. To get the full story on how to conduct reliable and accurate research online, check out our best practice guide.
This comprehensive guide covers:
With Prolific, you can complete your research projects more efficiently and collaborate with your team in a shared workspace. This fosters much-needed transparency and accountability throughout the research process.
In addition, we ensure diverse, high-quality study participants by:
And, if you need a niche audience, you can run a quick survey to recruit your participants.
Sign up today to get started.
Fresh out of YC's Summer 2019 batch, we want to share some of our most interesting learnings. If you're a startup founder or enthusiast and want to learn about product-market fit, growth experimentation and culture setting, you're in the right place!
Today Prolific is turning 5 years old – Happy Birthday to us! 🥳 It's been a remarkable journey so far. 3000+ researchers from science and industry have used Prolific last year, we have 45,000 quarterly active participants, and we've seen 200% year-on-year growth. But we're only getting started. In this post, I'll tell you a little bit about our journey, give credit where it's due!, and tell you about our exciting plans for the future.