Choosing a sample size for a thematic analysis

About a decade ago, Henry Potts and I (Fugard & Potts, 2015) developed a method to help think about choosing a sample size for thematic analyses. The paper explaining the approach has been cited over a thousand times. A small number of those citations report correct uses of the approach. 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)
  • “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’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 [30] 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 [30,31]. 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)
  • “According to the calculation method of Fugard and Potts [28], 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%.)


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.Β & Potts, H. W. W. (2015).Β Supporting thinking on sample sizes for thematic analyses: A quantitative tool.Β International Journal of Social Research Methodology,Β 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., & BerterΓΆ, 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.

Qualitative research

Qualitative research isn’t synonymous with interview or focus group. Here’s an example from neuroscience: Gray’s (1959) classification of the qualitatively different kinds of synapse, using electron microscopy.

Here is Gray’s summary description (p. 430).

“In type 1 synapses a large percentage of the length of the apposed membranes shows increased thickness and density. The post-synaptic thickening is more pronounced than the pre-synaptic thickening. These thickened regions of the membranes lie farther apart than where the apposed membranes are unthickened and in the extracellular region between the thickened membranes an intermediate band of material can be seen.”

“In type 2 synapses the percentage length of thickening is small, the pre- and post-synaptic thickenings are of similar dimensions, the intermediate band is not clearly visible and the membrane spacings at these regions differ little from the non-thickened regions.”

Figure 10 below shows an example of the material Gray was analysing:

Gray, E. G. (1959). Axo-somatic and axo-dendritic synapses of the cerebral cortex. Journal of Anatomy, 93, 420–433.