What is Selection Bias in Qualitative Research?

A brief introduction to Selection Bias

For more best practices see our method overview

Definition of Selection Bias

Selection bias is a type of error that occurs when the sample of participants in a qualitative research study is not representative of the population or phenomenon of interest. This can lead to inaccurate or misleading findings that do not reflect the reality or diversity of the research topic.
Decoration image for selection bias
Example: Let's consider a qualitative research study aiming to explore people's attitudes towards public transportation usage in a city. The researcher decides to conduct interviews at a central transit station during the morning rush hour. However, this selection bias might result in overrepresenting the opinions of commuters who regularly use public transportation, while neglecting the perspectives of those who do not use it as regularly or have alternative commuting arrangements. These neglectec perspectives may have a very different experience at times when the vehicles are less crowded and usually are subject to different ticketing price structures compared to a monthly ticket.

Sources of Selection Bias

Selection bias can occur at different stages of the qualitative research process, such as:
  1. When defining the research question and objectives
  2. When choosing the sampling strategy and criteria
  3. When recruiting and selecting the participants
  4. When collecting and analyzing the data
  5. When interpreting and reporting the results

Some of the common causes and types of selection bias in qualitative research are:
  • Convenience sampling: This is when the researcher selects participants based on their availability, accessibility, or willingness to participate, rather than on their relevance or suitability for the study. This can result in a sample that is biased towards certain groups or characteristics that are more easily reachable or cooperative, and exclude others that are harder to access or less willing to participate.
  • Self-Selection Bias: This is when the participants themselves decide whether to participate in the study or not, based on their own interests, motivations, or preferences. This can result in a sample that is biased towards those who have a strong opinion, experience, or stake in the research topic, and exclude those who are indifferent, unaware, or reluctant to share their views or experiences.
  • Non-response bias: This is when some of the selected participants do not respond or drop out of the study for various reasons, such as lack of time, interest, trust, or incentive. This can result in a sample that is biased towards those who are more responsive, engaged, or motivated to participate, and exclude those who are less so.
  • Attrition bias: This is when some of the participants leave the study before it is completed, due to factors such as illness, death, relocation, or loss of contact. This can result in a sample that is biased towards those who remain in the study, and exclude those who exit prematurely.
  • Interviewer bias: This is when the researcher influences the selection or behavior of the participants, either intentionally or unintentionally, through their actions, words, gestures, expressions, or attitudes. This can result in a sample that is biased towards those who are more compatible, agreeable, or similar to the researcher, and exclude those who are less so.

Strategies to Mitigate Selection Bias

You may want to consider implementing some of the following strategies to address the potential threat to the Trustworthiness of your research caused by selection bias:
  • Define the research question and objectives clearly and precisely
  • Choose a sampling strategy and criteria that are appropriate and relevant for the study
  • Use multiple sources and methods to recruit and select participants
  • Ensure that the sample size and composition are adequate and diverse. What sample size is adequate in your context depends heavily on your research question. You can measure Theoretical Saturation as an indicator for when further data gathering does no longer provide significant new insights for your theory.
  • Monitor the response rate and attrition rate of participants and document this in your Audit Trail
  • Use Reflexivity and triangulation to check and balance the researcher's role and influence. Types of triangulation you should be aware of and consider in your research are:
  • Report the sampling process and limitations transparently and honestly. The documentation of your sampling should be part of your Audit Trail

Conclusion on Selection Bias

Selection bias can have serious implications for the validity and reliability of your qualitative research findings. It can affect the Transferability of the results to other contexts or populations. It can also affect the Credibility and Trustworthiness of the results within the specific context or population of the study.
Therefore, it is important for you as a qualitative researcher to be aware of the potential sources and effects of selection bias, and take steps to prevent or minimize it.

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