Reaching saturation point in qualitative research

A common question from newcomers to qualitative research is, what’s the right sample size? How many people do I need to have in my project to get a good answer for my research questions? For research based on quantitative data, there is usually a definitive answer: you can decide ahead of time what sample size is needed to gain a significant result for a particular test or method. In qualitative research, there is no neat measure of significance, so getting a good sample size is more difficult.

The literature often talks about reaching ‘saturation point’ - a term taken from physical science to represent a moment during the analysis of the data where the same themes are recurring, and no new insights are given by additional sources of data. Saturation is, for example, when no more water can be absorbed by a sponge, but it’s not always the case in research that too much is a bad thing.

What is saturation in qualitative research?

Saturation in qualitative research is usually defined as the point in a qualitative research project when there is enough data to ensure the research questions can be answered. However, it is a difficult concept to completely define, according to Bowen (2008). As with all aspects of qualitative research, the depth of the data is often more important than the numbers (Burmeister & Aitken, 2012). A small number of rich interviews or sources, especially as part of a ethnography, can have the importance of dozens of shorter interviews.

For Fusch and Ness (2015):

“The easiest way to differentiate between rich and thick data is to think of rich as quality and thick as quantity. Thick data is a lot of data; rich data is many - layered, intricate, detailed, nuanced, and more. One can have a lot of thick data that is not rich; conversely, one can have rich data but not a lot of it. The trick, if you will, is to have both.”

So the quantity of the data is only one part of the story. The researcher needs to engage with it at an early level to ensure “all data [has] equal consideration in the analytic coding procedures. Frequency of occurrence of any specific incident should be ignored. Saturation involves eliciting all forms of types of occurrences, valuing variation over quantity” (Morse 1995).

How do I know if I've reached data saturation?

For a qualitative project, you can tell if you are approaching data saturation by these signs:

• The amount of variation in the data is levelling off.
• New perspectives and explanations are no longer coming from the data.
• There are no new perspectives on the research question.

Brod et al. (2009) recommend constructing a ‘saturation grid’ listing the major topics or research questions against interviews or other sources, and ensuring all bases have been covered.

How many participants or sources should I have for a qualitative research project?

This depends significantly on your methodology and approach for data collection. Your intended research output can also have an impact, if you are writing for journals or audiences which are unfamiliar with qualitative research and its generally lower sample sizes. Many papers have attempted to find the ideal number of sources for a qualitative project, and as could be expected, the results vary greatly.

Mason (2010) looked at the average number of respondents in PhD theses using qualitative research. They found that an average of 30 sources were used, but the lowest was 1 source, the highest was 95 and there was a standard deviation of 18.5! It is interesting to look at their data tables, as they show succinctly the differences in sample size expected for different methodological approaches, such as case study, ethnography, narrative enquiry, or semi-structured interviews.

While 30 in-depth interviews may seem high (especially for what is practical in a PhD study), others work with much less: a retrospective examination from a qualitative project by Guest et al. (2006) found that even though they conducted 60 interviews, they had saturation after 12, with most of the themes emergent after just 6. On the other hand, if students have supervisors with more of a mixed-method or quantitative background, they will often struggle to justify the low number of participants suggested for methods of qualitative inquiry.

It is nearly impossible for a researcher to know when they have reached saturation point unless they are analysing the data as it is collected. This exposes saturation's ties to grounded theory, which requires an iterative approach to data collection and analysis. Instead of setting a fixed number of interviews or focus groups to conduct at the start of the project, the investigator should be continuously going through cycles of collection and analysis until nothing new is being revealed.

Do I need to worry about saturation in qualitative research?

Saturation can be a difficult notion to work with, especially when ethics committees or institutional review boards, limited time or funds place a practical upper limit on the quantity of data collection. Indeed Morse et al. (2014) found that in most dissertations they examined, the sample size was chosen for often practical reasons, not because a claim of saturation was made.

You should also be aware that many take umbrage at the idea of saturation. O’Reilly and Parker (2013) notes that since the concept of saturation comes out of grounded theory, it’s not always appropriate to apply to all qualitative research projects, and the term has become overused in the literature. It’s also not a good indicator of the quality of qualitative research, and might be too much of a positivistic concept for qualitative studies.

Further reading on saturation

For more on these issues, I would recommend any of the articles referenced in our bibliography below, as well as discussion with supervisors, peers and colleagues. There is also more on sampling considerations in qualitative research in our previous blog post article.

Finally, don’t forget that Quirkos can help you take an iterative approach to analysis and data collection, allowing you to quickly analyse your qualitative data as you go through your project, helping you visualise your path to saturation (if you so choose this approach!). Try our free trial for yourself, or take a closer look at the rest of the features the software offers.

Bibliography

Bowen, G. A. (2008). Naturalistic inquiry and the saturation concept: a research note. Qualitative Research, 8(1), 137-152.

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, 1263–1278.

Burmeister, E. and Aitken, L. (2012). Sample size: How many is enough? Australian Critical Care, 25, 271-274

Fusch, P. I. and Ness, L. R. (2015). Are We There Yet? Data Saturation in Qualitative Research. The Qualitative Report, 20(9), 1408-1416.

Guest, G., Bunce, A. and Johnson, L. (2006). How Many Interviews Are Enough?: An Experiment with Data Saturation and Variability. Field Methods, 18(1), 59-82.

Mason, M. (2010). Sample Size and Saturation in PhD Studies Using Qualitative Interviews. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 11(3).

Morse, J. M. (1995). The Significance of Saturation. Qualitative Health Research, 5(2), 147-149.

Morse, W. C., Lowery, D. R., & Steury, T. (2014). Exploring Saturation of Themes and Spatial Locations in Qualitative Public Participation Geographic Information Systems Research. Society & Natural Resources, 27(5), 557–571.

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