In qualitative research, the term “data saturation” is used to describe the point at which the integration of new participants doesn’t produce new findings. When saturation is achieved, enough information has been gathered to replicate the study and additional coding isn’t needed. Saturation helps indicate the robustness of a study, demonstrating the researcher has exhaustively studied a phenomenon within the bounds of a study. As Fusch and Ness (2015) explained, the failure to achieve saturation can undermine content validity.

Saturation is directly tied to two things: sample size and the depth of data. Let’s look at how both of these factors can influence saturation.

Sample size for data saturation

Sample size has the greatest influence on saturation in qualitative research. Generally speaking, larger samples are more likely to achieve saturation because a greater volume of data is produced. However, there is no one-size-fits-all approach to sample size. Also, a big sample does not guarantee data saturation will be reached. There are no published guidelines to determine sample sizes needed to reach saturation because requirements vary from study to study. Usually, studies with a smaller scope and more modest claims can achieve saturation more quickly that those with broader claims that span multiple disciplines (Mason, 2010). In addition, more homogenous samples (that is, those with narrowly defined inclusion criteria) are more likely to hit saturation at an earlier point.

Sample size recommendations from notable qualitative researchers

While the exact sample size required to achieve saturation can’t be known until data analysis begins, several qualitative researchers have provided recommendations that you can use to support the sample size you decide to start with.

-Guest et al. (2006) said samples a small as 6 may be adequate for qualitative researchers who are conducting interviews. Guest et al. also stated that often, 88%-92% of codes emerge after 12 interviews.

-Morse (1994) also argued for a minimum sample size of 6.

-Creswell (1998) suggested 5 to 25 participants for phenomenology.

-Bertaux (1981) recommended minimum samples of 15 in any type of qualitative investigation.

-Green and Thorogood (2009) recommended around 20 participants for qualitative research involving interviews.

Grounded theory and ethnography generally require larger samples; Morse and Creswell both recommended 30-50 interviews (so you might want to shy away from these designs unless you have a LOT of time to dedicate to data collection and analysis!).

Depth of data

As important as sample size is, having a beefy sample doesn’t guarantee saturation. More is not always better when it comes to sample size in qualitative research. It is usually better to have a smaller sample, but long, in depth interviews with each participant, than it is to have a larger sample with short, superficial interviews. For this reason, the design of your interview protocol (or questionnaires, focus group protocols, etc.) is really important. Make sure you’re asking clear, specific questions, and are using follow-up and probing prompts to dig a little deeper when appropriate.

Outside coders in qualitative research

Another way to help ensure data saturation is through an outside coder. Brod et al. (2009) suggested secondary coders to ensure data saturation has been reached. A secondary coder can provide a fresh set of eyes to make sure all codes have been recognized and assigned, as well. If you need assistance with primary or secondary coding, contact me. I have helped hundreds of doctoral candidates with their qualitative research.

What if data saturation isn’t indicated?

Keep collecting data. If you don’t hit saturation with your initial sample size, collect data from additional participants until saturation is achieved. You increase your chances of achieving data saturation if you: (a) have clear and narrowly defined inclusion criteria, (b) ask focused questions and follow up with probing prompts, when appropriate, and (c) have clear study boundaries that limit the scope of your investigation.

Happy writing!


Bertaux, D. (1981). From the life-history approach to the transformation of sociological practice. In D. Bertaux (Ed.), Biography and society: The life history approach in the social sciences (pp.29-45). Sage.

Brod, M., Tesler, L. E., & Christensen, T. L. (2009). Qualitative research and content validity: Developing best practices based on science and experience. Quality of Life Research, 18(9), 1263–1278. doi:10.1007/s11136-009-9540-9

Creswell, John (1998). Qualitative inquiry and research design: Choosing among five traditions. Sage.

Guest, G., Bunce, A., & Johnson, L. (2006). How Many Interviews Are Enough? Field Methods, 18(1), 59–82. doi:10.1177/1525822×05279903

Green, J., & Thorogood, N. (2009). Qualitative methods for health research (2nd ed.). Sage.

Fusch, P., & Ness, L. (2015). Are we there yet? Data saturation in qualitative research. The Qualitative Report. doi:10.46743/2160-3715/2015.2281

Mason, M. (2010,). Sample size and saturation in PhD studies using qualitative interviews. Forum: Qualitative Social Research, 11(3).

Morse, J. M. (1994). Designing funded qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (pp. 220-235). Sage.

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