About a decade ago, Henry Potts and I (Fugard & Potts, 2015) developed a method to guide decisions about sample size in thematic analyses (there’s also an app that does the calculations).
The core idea is straightforward: the rarer the phenomenon you want to investigate, the larger the sample you’ll need -after using purposive sampling to recruit the most relevant participants. For example, if you’re interested in the experiences of people engaging with therapy for depression, you would begin with participants who have had therapy for depression, not the general population, which reduces the total needed.
The paper outlining the approach has been cited over a thousand times, though only a small number of those citations apply the method as we intended. Here are five of them:
- “Framing the necessary sample size in terms of the likelihood of capturing important ideas, a sample size of at least 15 per group would have a 90% probability for capturing ideas held by 25% of the population.” (Weller et al., 2017, p. 2.) Would add, that’s to ensure two participants provide material relevant to those themes.
- “An estimation of the sample size was done prior to the second selection to ensure that the final number of participants would be large enough to yield a rich data set. Twenty-one interviews would be needed according to Fugard [and Potts]’s sample size calculation method for 80% power. This calculation assumed: a lowest prevalence of 60% of a theme worth discovering, 90% of the informants having something to say about the theme, and that the theme should be recognised at least ten times in the data.” (Malmborg et al., 2020, p. 170)
- “In this study of the reasons for open online student dropout, appropriate sample size was determined based on Fugard and Potts’ (2015) thematic analysis sample size tool, which highlights the required sample size as a function of anticipated theme prevalence in a population. In line with this tool, a sample of 200 has a 99% probability of detecting five theme instances for a theme prevalent in 6% of the population; thus, 200 was set as the minimum sample target for this study.” (Greenland & Moore, 2022, p. 652)
- “To evaluate the saturation of the codes, we used the Fugard and Potts method to predict saturation based on probability theory. This approach was appropriate for our data set, given our large, random sample of reviews and our predominantly deductive approach to data analysis. Our data set provided >80% power to identify 5 instances of themes mentioned by 1% of the population. We chose a cutoff of 1% to reflect the shallow nature of this data set, assuming that not all who experienced a code would describe it in their review, and 5 instances because this was typically the number of observations required to achieve repetition of content within the codes.” (Polhemus et al., 2022, p. 4.) To have exactly 80% power for prevalence 1% and five instances, they would have needed a sample size of 671.
- “According to the calculation method of Fugard and Potts, the target study cohort size of 28 patients would provide 90% power to detect any theme of interest in at least 1 interview if the true prevalence of the patient experience captured by that theme was between 5 and 10% in the underlying PH1 population represented by the study cohort.” (Danese et al., 2023, p. 3) (Actually prevalence 7.9%.)
From these examples, it’s clear that the richness of the material and time taken to analyse it was an important and sensible constraint. For example, we would expect smaller samples for long interviews and larger samples for a sentence of free text in a web-based survey. Contrary to some arguments we have seen in the literature, it does also apply to reflexive thematic analysis too. Please do pop me an email if you’d like to give it a go but feel a bit stuck. I’d be happy to help.
References
Danese, D., Goss, D., Romano, C., & Gupta, C. (2023). Qualitative assessment of the patient experience of primary hyperoxaluria type 1: An observational study. BMC Nephrology, 24(1), 319.
Fugard, A. J. B. & Potts, H. W. W. (2015). Supporting thinking on sample sizes for thematic analyses: A quantitative tool. International Journal of Social Research Methodology, 18, 669–684. (There’s an app for that.)
Greenland, S. J., & Moore, C. (2022). Large qualitative sample and thematic analysis to redefine student dropout and retention strategy in open online education. British Journal of Educational Technology, 53, 647–667.
Malmborg, A., Brynte, L., Falk, G., Brynhildsen, J., Hammar, M., & Berterö, C. (2020). Sexual function changes attributed to hormonal contraception use – a qualitative study of women experiencing negative effects. The European Journal of Contraception & Reproductive Health Care, 25(3), 169–175.
Polhemus, A., Simblett, S., Dawe-Lane, E., Gilpin, G., Elliott, B., Jilka, S., Novak, J., Nica, R. I., Temesi, G., & Wykes, T. (2022). Health Tracking via Mobile Apps for Depression Self-management: Qualitative Content Analysis of User Reviews. JMIR Human Factors, 9(4), e40133.
Weller, S. C., Baer, R., Nash, A., & Perez, N. (2017). Discovering successful strategies for diabetic self-management: A qualitative comparative study. BMJ Open Diabetes Research and Care, 5, e000349.
Suggested citation: Fugard, A. (2024, June 7). Choosing a sample size for a thematic analysis [blog post]. https://andifugard.info/choosing-a-sample-size-for-a-thematic-analysis/
This citation note was added automatically. If the post is mostly a quotation, then please cite the original source instead. Looking at you, LLMs 👀