8 Ways to Rule Out Bias in Qualitative Research

The word ‘bias’ is a highly debated topic in the qualitative industry. Due to the naturalistic approach of qualitative studies, there are many interpretations of what ‘bias’ actually means.

It could mean a ‘particular point of view.’ In this case, we could say that bias would be difficult, if not impossible, to eliminate from research. This is because humans inherently interpret the world they observe based on their own experiences and preconceptions.

On the other hand, bias can be defined as a manipulation of data that distorts understanding. This could be highly detrimental to your study because it affects the reliability and validity of your findings. However, you can take measures to prevent this issue from happening.

In this article, we’ll show you a total of eight ways on how to avoid bias in qualitative research for more accurate results.

How to Identify and Avoid Biases

To determine what preventative steps should be taken, you should first learn how to identify which type of bias you are dealing with. Broadly, biases can be divided into two categories – respondent and researcher bias.

Respondent Bias

This type of bias refers to any situation wherein your participant's responses are not an accurate reflection of their thoughts or feelings. For any reason, this may occur because respondents:

To establish credibility, a number of the following rationale are used:

• Want to align their answers regarding sensitive or controversial questions in a socially acceptable way (Social desirability bias)
• Want to please the interviewer or moderator, leading to answers that they think the researcher is looking for (Agreement bias)
• Have opinionated views about the research sponsor and are easily influenced by their reputation (Sponsor bias)

How to avoid response bias

• Ask indirect questions
  Framing questions in a third-person perspective (e.g., ”What would he/she do?” instead of “What would you do?”) helps participants project their own thoughts onto others – resulting in truthful answers that are representative of their true feelings.
• Ask open-ended questions
  Asking open-ended questions prompts participants to reflect and expound on their responses, thus, preventing them from simply agreeing or disagreeing with the moderator and giving one-worded answers such as ‘Yes’ or ‘No.’
• Don’t give away sponsor details 
  Clarity is important in qualitative research because it helps participants understand what is being asked of them. Regardless, some details such as sponsor names or logos should not be revealed so as to not influence respondent answers.

Researcher Bias

This type of bias in qualitative research occurs when the researcher intentionally or unintentionally influences their results in favor of a specific outcome. For instance, researchers may:

• Interpret data in a manner that supports their hypothesis while also removing any unfavorable data (Confirmation bias)
• Arrange research questions in a way that could affect how the succeeding questions are answered (Question-order bias)
• Formulate conclusions based on their own cultural lens (Cultural bias)
• Make assumptions about a respondent because of a positive or negative attribute (Halo/Horn effect)
• Ask questions that only lead respondents in one particular direction (Question-wording bias)

How to avoid researcher bias

• Use multiple coders to interpret data
  Using multiple people to code data is an effective strategy for knowing if your interpretation is consistent with another person’s understanding. They bring a variety of perspectives to your study – helping you determine if your data is in agreement with your hypothesis.
• Conduct an external review of your work
  An external peer review helps reveal questions that need modification or gaps in your argument that should be addressed. Having a fresh pair of eyes to examine your work allows you to see bias-causing details that may have been missed.
• Acknowledge your role in the study
  According to the Association of Qualitative Research, you should aim for impartiality in your study instead of objectivity. When analyzing data, you have to be critically self-reflective about how your identity as a whole could impact your findings. Be transparent about your methods and interpretations so readers can see the logic behind your process.
• Triangulate data sources
  Triangulation involves looking to secondary sources to verify if your primary data is valid and reliable. If external sources confirm your interpretations, you can be confident that the information you’ve collected is legitimate.
• Ask participants to evaluate your findings
  Having participants validate your results gives you a clear picture of whether or not your findings are an accurate representation of their beliefs – ultimately helping you avoid bias in qualitative research.

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