Validity and Reliability in Qualitative Research

Post prepared and written by Joe Tise, PhD, Senior Education Researcher

In this series we have discovered the many ways in which evidence of validity can be produced and ways in which reliable data can be produced. To be sure, the bulk of this series was focused on quantitative research, but any mixed-methods or qualitative researcher will tell you that quantitative research only tells us one piece of the puzzle.

Qualitative research is needed to answer questions not suited for quantitative research, and validity and reliability need to be considered in qualitative research too. Qualitative research includes numerous methodological approaches, such as individual and focus group interviews, naturalistic observations, artifact analysis, and even open-ended survey questions. Unlike quantitative research–which utilizes forms, surveys, tests, institutional data, etc.–in qualitative research, the researcher often is the data collection mechanism and the analysis mechanism.

Researchers usually don’t run a statistical analysis on qualitative data; instead, a researcher typically analyzes the qualitative data, extracts meaning from it, and answers a research question from that meaning. Though this is similar to quantitative research, some of the analysis methods can be viewed as more subjective.

So, how can we know that results obtained from a qualitative analysis reflect some truth, and not the researcher’s personal biases, experiences, or lenses?

Reliability and validity are equally important to consider in qualitative research. Ways to enhance validity in qualitative research include:

  • Use multiple analysts
  • Create/maintain audit trails
  • Conduct member checks
  • Include positionality statements
  • Solicit peer review of analytical approach
  • Triangulate findings via multiple data sources
  • Search for and discuss negative cases (i.e., those which refute a theme)

Building reliability can include one or more of the following:

  • Clearly define your codes and criteria for applying them
  • Use detailed transcriptions which include things like pauses, crosstalk, and non-word verbal expressions
  • Train coders on a common set of data
  • Ensure coders are consistent with each other before coding the reset of the data
  • Periodically reassess interrater agreement/reliability
  • Use high-quality recording devices

The most well-known measure of qualitative reliability in education research is inter-rater reliability and consensus coding. I want to make a distinction between two common measures of inter-rater reliability: percent agreement and Cohen’s Kappa.

Percent agreement refers to the percentage of coding instances in which two raters assign the same code to a common “piece” of data. Because this is a simple percentage, it’s more intuitive to understand. But it also does not account for chance–in any deductive coding framework (i.e., when all possible codes are already defined), there is a random chance that two coders will apply the same code without actually “seeing” the same thing in the data.

By contrast, Cohen’s Kappa is designed to parse out the influence of chance agreement, and for this reason Cohen’s Kappa will always be smaller than the percent agreement for a given dataset. Many qualitative data analysis software packages (e.g., NVivo) will calculate both percent agreement and Cohen’s Kappa.

In consensus coding, multiple raters code the same data, discuss the codes that may apply, and decide together how to code the data. With consensus coding, the need for inter-rater agreement/reliability metrics is circumvented, because by definition, you will always have 100% agreement/reliability. The major downside of consensus coding is, of course, the time and effort needed to engage it. With large sets of qualitative data, consensus coding may not be feasible.

For a deeper dive into these topics, there are many excellent textbooks that explore the nuances of qualitative validity and reliability. Below, you’ll find a selection of recommended resources, as well as others that provide detailed insights into strengthening qualitative research methods.


Corbin, J., & Strauss, A. (2015). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (4th ed.). Sage Publications.
Creswell, J. W., & Báez, J. C. (2021). 30 Essential Skills for the Qualitative Researcher (2nd ed.). Sage Publications.
Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches. Sage Publications.
Saldaña, J. (2013). An introduction to codes and coding. In The coding manual for qualitative researchers (pp. 1–40). Sage Publications.

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