No matter how much you think you know about your users, conducting user research is crucial to uncovering valuable insights and aligning your product or service to your user’s needs and goals. As an organisation you need to ensure that your user research is unbiased and reflective of how your users or customers truly behave and feel. It would be naive to think that research can be 100% free of bias, however, there are ways to reduce and avoid bias as much as possible. We’ve pulled together our top tips for minimising research bias and maximising objectivity:
An unbiased approach to shaping your methodology
- Be purposeful in defining hypotheses. This is key in allowing you to be explicit about your assumptions from the start and to consciously look for overt bias. Question what your hypotheses were based on – for example, was it customer data, or word of mouth?
- Identify and avoid sample bias. Be mindful of how you recruit your participants. Always refer back to your overall research aims when deciding on which users to focus on. To avoid sample bias, define your target audience to ensure that participants are neither underrepresented nor overrepresented, relative to others in the sample.
- Construct great questions. Structure your questions carefully and avoid wording bias that causes the framing of your questions to suggest a certain answer. Draw upon your team to review your questions and ask them to highlight where questions might inadvertently reveal your own point of view or lead to biased responses. Asking the right questions can be challenging; consider conducting contextual research alongside your research methods to gain a more nuanced understanding of the user in their natural environment.
An unbiased approach to conducting user research
- Recognise your own confirmation bias. As a researcher, pay attention to any responses or behaviours that may disprove your hypothesis, rather than confirming your own assumptions. This will help reduce bias when synthesising your findings.
- Recognise participant bias. Participants may respond in a manner that is socially desirable rather than expressing their true feelings. They may also, unintentionally, modify their behaviour if they know that they are being observed. Make users feel comfortable by creating a safe space that encourages them to express their opinions freely, and state that all results will be anonymised. Preface the session by informing participants that there is no right or wrong way to complete tasks or to respond. State the session objectives but then let the participants do the talking or take actions that you may want to observe. If feedback is required on visual designs, highlight that the researchers did not design the content and therefore will not be offended by open and honest feedback.
An unbiased approach to analysing your findings
- Collaborative analysis. The most crucial step in conducting unbiased research is analysing your findings in an objective way. It can be beneficial to start by gathering together a team of people to generate themes from your findings before diving into the detail. The nature of qualitative data can make it difficult for the person doing the analysis to separate themselves from the data. Use multiple people to organise the data into cohesive themes and collaboratively look for alternative explanations that can be accounted for or ruled-out to strengthen your interpretations. It can be difficult to recognise your own bias, therefore, asking colleagues to analyse your conclusions will help to identify if there is consistency between your interpretations and that of your colleagues.
- Identify and eliminate participant bias. Pay close attention to any findings that are not reflective of how the participant would normally behave or feel. For instance, in some user research there is an expectation to receive some sort of retribution after completion. Respondents who are solely motivated by money can also skew your findings, therefore, control the quality of responses by eliminating responses that appear biased – for example, respondents who consistently answered too fast, or selected the same option in every question.
- Agreeing a threshold. It can be difficult to know what standards of evidence your team will use for something to qualify it as an actionable insight. If you find one user responding in a certain way is that enough to take action? What if you see it in five users? To reduce bias, we recommend that you determine a process in advance for analysing results and collectively agree on a threshold for an insight that is actionable and, when developing questions, have the conversation on how you might interpret them.
Conclusion
There are many more biases that manifest within user research and it is best not to get too caught up on contemplating them all. Ultimately, recognising bias is present in both the methodology and analysis, and then taking action to reduce it, will allow you to obtain truthful insights and design, keeping the users at the forefront of your user research.