The Eternal Question in Qualitative Research: How Many Interviews Are Enough?

 

If you've ever designed a qualitative study, you've faced the inevitable and tricky question: "What should my sample size be?" If you asked a methodologist, they'd likely say, "It depends." If you ask an ethics committee, they demand a specific number. And if you are working on a master's thesis, you likely have to navigate the various opinions of your advisor.

As a statistics professor, I tend to encourage large samples. But I know that in qualitative research, especially in studies involving personal interviews, the statistician's voice carries less weight. Anyway, I have faced many anxious students, trying to get my opinion. In an attempt to shed some light on the discussion, I will outline some knowledge that should help those who are searching at this crucial moment in their research.

This dilemma is as relevant today as it was a decade ago. While the core principle remains—depth over breadth—our understanding of how to justify the "right" number has become more sophisticated. Let's explore the current answers to this perennial question.

1. The Classic Compass: Pragmatic and Experiential Advice

Some timeless advice still holds immense value for early-career researchers, especially for dissertations.

        ·        Uwe Flick reminds us that design must balance what we want to know with the time needed, access to participants, and, crucially, the budget.

        ·        Rola Ajjawi points to the trade-off between depth and breadth. A deep phenomenological study might need only 6-12 participants, while a study seeking broader perspectives might require more.

       ·        Adler & Adler offered famously practical advice for students: interview about twelve people for a master's thesis. This number is manageable and provides a rich learning experience in interviewing and analysis. For a doctoral thesis, they suggested around 30.

This pragmatic guidance is a safe harbor for many. But the prevailing wind in qualitative research has long been blowing toward a different concept: saturation.

2. The Gold Standard? The Evolution of "Saturation"

For years, saturation has been the go-to justification for sample size. It’s the point where you stop collecting data because new interviews are no longer yielding new insights; you're hearing the same themes repeated.

However, this common definition has faced valid criticism:

        ·        Is it just a "feeling" the researcher has?

        ·        What if the next person would have provided a radically different story?

        ·        Are we stopping at "descriptive" repetition, or have we reached a deeper, "theoretical" understanding?

How the Concept Has Matured:

Research by scholars like O'Reilly & Parker pushed the field to refine its thinking. We now often distinguish between:

       ·        Code Saturation (or Descriptive Saturation): When no new codes or themes are emerging from the data. You've "heard it all."

       ·        Meaning Saturation (or Theoretical Saturation): A deeper level where you fully understand the nuances, relationships, and contours of your themes. You're not just hearing new codes, but you've developed a robust theoretical model that explains the data.

This distinction is crucial. A study might achieve code saturation after 15 interviews, but may need 5 more to truly flesh out the meaning and relationships between those codes, reaching meaning saturation.

3. The Modern Toolkit: New Ways to Justify Your Number

So, how do you navigate this in practice today, especially when you need to propose a sample size to an ethics committee before you start?

Here are two powerful contemporary approaches:

A) The "Information Power" Model:
This model suggests that the more information power your sample holds, the fewer participants you need. Your sample size is adequate when your data is rich enough to answer your research question. This depends on:

       ·        Aim of the Study (broad vs. narrow)

       ·        Sample Specificity (highly specific, experienced participants vs. a general group)

       ·        Use of Established Theory (are you building new theory or testing an existing one?)

       ·        Quality of Dialogue (the depth of the interviews)

       ·        Analysis Strategy (a detailed, nuanced analysis requires less data)

B) The "Saturation Model":
This approach, gaining traction in health research, involves specifying a "stopping rule" for data collection. You can pre-define:

     ·        A base size (e.g., an initial 10 interviews).

     ·        A run length (e.g., 3 additional interviews).

     ·        A new information threshold (e.g., less than 10% new information).

You would analyze the base size, then conduct the "run" of new interviews. If the new data falls below your threshold for new information, you stop. If not, you do another run. This provides a transparent, empirical justification for your final sample size.

Conclusion: From a Single Number to a Strategic Plan

So, what is the sample size for qualitative research in 2024?

The answer is no longer a single magic number. It's a strategic, justified decision.

     1.     Start with a Pragmatic Estimate: Use rules of thumb (like Adler & Adler's) or the Information Power model to propose a plausible range (e.g., 15-25 participants) for your ethics proposal.

     2.     Plan for an Iterative Process: Clearly state that this is an initial estimate and that data collection will continue until saturation is achieved. Specify which type of saturation you are seeking (e.g., code saturation).

     3.     Be Transparent in Reporting: When you publish, don't just say "saturation was reached." Describe how you assessed it. Did you use a saturation grid? Track cumulative themes? This transparency is the new standard of rigor.

The goal remains to tell a compelling, valid, and deep story about your data. The methods for getting there have simply become more transparent, defensible, and nuanced. And I hope you develop a sensitivity for the method and start accumulating experience—even if it's hard-won—to confidently throw yourself into new research.

References

         1.     Flick, Uwe in Baker, S. E. e Edwards, R How many qualitative interviews is enough. (2012)  http://eprints.ncrm.ac.uk/2273/4/how_many_interviews.  

  2. Ajjawi, R. Sample size in qualitative research Medical Education Research Network. blogs.cmdn.dundee.ac.uk/meded.../tag/sample-size/

        3.     Adler, P.A. e Adler, P. in Baker, S. E. e Edwards, R How many qualitative interviews is enough. (2012)  http://eprints.ncrm.ac.uk/2273/4/how_many_interviews. 

      4.     Mason, M Sample Size and Saturation in PhD Studies Using Qualitative Interviews. FQS.  Volume 11, Nº 3, Art. 8 – September 2010.

     5.     O’Reilly, M., & Parker, N. ‘Unsatisfactory Saturation’: a critical exploration of the notion of saturated sample sizes in qualitative research. Qualitative Research, 13(2), 190-197. (2013).

     6.     Keen, A. Saturation in qualitative research: distinguishing between descriptive and theoretical saturation www.rcn.org.uk/

     7.     Look for: How many interviews are needed in a qualitative research ...www.researchgate.net/.../How_many_interviews_are

Acknowledgements

This post was updated with the assistance of DeepSeek's AI research assistant in synthesizing recent developments in qualitative methodology.