How to Establish Data Accuracy in Qualitative Research

Louise Principe
Sep 21, 2022

Data Accuracy

In qualitative research, we ask for data that cannot be measured on a scale. While quantitative data is nominal, repeatable, and measurable, qualitative data tends to be varied and socially constructed. As a result, demonstrating the accuracy of your qualitative findings differs significantly from quantitative methods.

To establish accuracy in research, here are four data validation categories that you should address in your qualitative evaluation, as well as some strategies that you could use to operationalize these concepts.

Data Validation Categories


Credibility is arguably the biggest portion of the discussion of trustworthiness or validity. Data is deemed credible if the research findings accurately interpret what participants meant with their responses. This entails phenomena that are richly and accurately described.

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

  • Persistent Observation: Because responses can differ depending on the context, frequent interactions with subjects give you an in-depth understanding of data because you have accounted for the variances in answers.
  • Triangulation: The use of multiple secondary methods or data sources to develop a comprehensive understanding of primary data. There are various types of triangulation strategies that could be implemented for data accuracy, such as:
  • Methodological triangulation (e.g., mixed methods)
  • Data source triangulation (e.g., interview data and survey data)
  • Investigator triangulation (e.g., multiple observers, interviewers, researchers, or analysts)
  • Theory triangulation (e.g., multi-disciplinary theories)
  • Environmental triangulation (e.g., varying locations and settings)


  • Debriefing and Peer Scrutiny: Consult with colleagues (e.g., expert panel, project directors, steering group), so they can look at your data, verify your interpretation, and offer fresh perspectives. Implementing this strategy early on in the process helps analysts uncover possible biases in data.
  • Negative Case Analysis: Refining a hypothesis and comparing outliers or other data points for alternative explanations that oppose the meaning you’ve interpreted from your data. This ensures that constructs account for all instances.
  • Referential Adequacy:  Isolating a portion of the data while analyzing the rest and then comparing the themes identified with the isolated segment to see if they are similar.
  • Member Checking: Ensuring that respondent voices are accurately interpreted through follow-up and clarifying questions as well as a subject review of the findings. 


Confirmability is the aspect of trustworthiness that centers on researcher objectivity. This is established when your research findings are supported by the data collected and not altered drastically due to researcher bias.

But while objectivity or neutrality is hard to achieve, there are ways in which you can demonstrate how your identity can influence your perceptions of certain topics and, in turn, how you interpret results. Some possible strategies include:

  • Reflexivity: Acknowledging and confronting biases in advance.
  • Confirmability Audit: Sharing your findings with colleagues to ensure that observations are not unique to your perceptions.


As the name suggests, transferability is established in data accuracy if the meanings interpreted from your study are applicable (or transferable) to a broader population. The most important means of proving transferability is carefully describing the observed situation.

The richer and more detailed you can recount the phenomena and its scope, the more you can understand the transferability of its meaning in different concepts.

In a study by Shenton, he shared six questions you can use to consider the context of your data:

  • How many organizations took part in the study, and where are they based?
  • Are there any restrictions on the type of people who contributed their data?
  • How many respondents were involved in the fieldwork?
  • What were the data collection methods employed?
  • How many data-gathering sessions were conducted, and how long did they last?
  • Over which period was the data collected?


The category of dependability is somewhat comparable to the concept of reliability in quantitative approaches. It’s defined as being transparent in describing the methods or procedures used so that other researchers can execute a similar study.

To confirm data accuracy, inquiry audits or audit trails should be implemented.

  • Audit Trail: Readers appraise records (e.g., field notes) of how the researcher conducted the qualitative study. This helps them trace the logic behind the research process to determine whether data can be relied upon for further inquiry.
  • Inquiry Audit: Involves having an external researcher examine the data collection and analysis process as well as research findings.


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