Uncovering the types of bias in research: Identification, prevention and examples

George Denison



March 2023

Uncovering the types of bias in research: Identification, prevention and examples

George Denison



March 2023

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!

The importance of addressing bias in research

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.

Understanding the types of bias

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:

  • Sampling bias: Occurs when the study sample doesn’t represent the larger population. This could potentially lead to a misleading conclusion because the participants’ characteristics are too niche compared to those who you’re intending to generalize about.
  • Design bias: Happens due to flawed study design and produces unreliable results. Includes everything in the research design phase. But, in surveys, leading questions are usually the culprit. This may cause participants to answer in a certain way that influences the outcome of the study.
  • Measurement bias: Arises when the measurements used to assess outcomes aren't valid or reliable. For example, you might ask participants how often they exercise. But what does 'exercise' mean? A short jog? An intense workout? If it's not clear, some people might say they exercise more, or less, than they really do.
  • Response bias: Also known as social desirability bias. This occurs when survey respondents answer questions in a way they believe will reflect positively upon them or their group. Conducting research via anonymous online surveys can help reduce response bias. That's because social pressures are less likely to influence respondents in this environment.
  • Reporting bias: Commonly referred to as publication bias. It’s found when researchers publish certain findings over others. This might be for several reasons, but often because they're more relevant to the study's conclusion or fit a particular narrative.
  • Procedural bias: Occurs when participants aren't given sufficient time to complete what is required of them. This poor study design results in inaccurate responses because they're rushed.

Spotting the signs of bias

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:

  • Your results don't match up with existing data or other research on the subject.
  • There are conflicts of interest. For example, financial or personal relationships could affect the study's objectivity.
  • There's pressure or influence from external sources. For example, funding agencies or organizations with a vested interest in the study's outcome.
  • The language used in the research or analysis is loaded with subjective language that could sway how the results are interpreted.
  • There are inconsistencies in the data that could indicate errors or manipulation.
  • A respondent complains about biased questions.
  • The interpretation of the data is one-sided or biased toward a particular conclusion. Other explanations or perspectives haven’t been taken into account.
  • You or members of your research team have personal beliefs or opinions that could influence the outcome of the study, also known as cultural bias.

Bias at different stages of research

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.

Ensuring high-quality research with Prolific

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:

  • What to consider before starting your research.
  • How to design and launch a high-quality study.
  • How to analyze your data under two statistical frameworks.

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:

  • Providing pre-set filters.
  • Following a thorough vetting process for participants. This includes a bank-level check and photo ID verification.
  • Implementing a minimum reward threshold for participants so they're fairly compensated for their time and effort.

And, if you need a niche audience, you can run a quick survey to recruit your participants.

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