Supporting thinking on sample sizes for thematic analyses: a quantitative tool

By Andrew J. B. Fugard, Henry W. W. Potts. 2015

772 citations recorded by the source corpus.

Thematic analysis is frequently used to analyse qualitative data in psychology, healthcare, social research and beyond. An important stage in planning a study is determining how large a sample size may be required, however current guidelines for thematic analysis are varied, ranging from around 2 to over 400 and it is unclear how to choose a value from the space in between. Some guidance can also not be applied prospectively. This paper introduces a tool to help users think about what would be a useful sample size for their particular context when investigating patterns across participants. The calculation depends on (a) the expected population theme prevalence of the least prevalent theme, derived either from prior knowledge or based on the prevalence of the rarest themes considered worth uncovering, e.g. 1 in 10, 1 in 100; (b) the number of desired instances of the theme; and (c) the power of the study. An adequately powered study will have a high likelihood of finding sufficient themes of the desired prevalence. This calculation can then be used alongside other considerations. We illustrate how to use the method to calculate sample size before starting a study and achieved power given a sample size, providing tables of answers and code for use in the free software, R. Sample sizes are comparable to those found in the literature, for example to have 80% power to detect two instances of a theme with 10% prevalence, 29 participants are required. Increasing power, increasing the number of instances or decreasing prevalence increases the sample size needed. We do not propose this as a ritualistic requirement for study design, but rather as a pragmatic supporting tool to help plan studies using thematic analysis.

Subfields: Education, General Health Professions, Social Psychology

View Paper: https://www.semanticscholar.org/paper/1a502adc41b6a62040c2b0aa54eb8421751f4c1e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Education

In General Health Professions

In Social Psychology

Citations in the corpus

(Mis)conceptualising themes, thematic analysis, and other problems with Fugard and Potts’ (2015) sample-size tool for thematic analysis (2016, background): “VOL. 19, NO. 6, 739–743 http://dx.doi.org/10.1080/13645579.2016.1195588 (Mis)conceptualising themes, thematic analysis, and other problems with Fugard and Potts’ (2015) sample-size tool for thematic analysis”
‘Define, Explain, Justify, Apply’ (DEJA): An analytic tool for guiding qualitative research sample size (2021, methodology): “, the analysis technique used (Fugard & Potts, 2015a), aim/s of the study (Charmaz, 2006), etc.”
A simple method to assess and report thematic saturation in qualitative research (2020, methodology): “Employing this same logic, Fugard and Potts [21] developed a quantitative tool to estimate sample sizes needed for thematic analyses of qualitative data.”
How Many Focus Groups Are Enough? Building an Evidence Base for Nonprobability Sample Sizes (2017, methodology): “Employing this same logic, Fugard and Potts (2015) developed a quantitative tool to estimate sample sizes for thematic analyses of qualitative data.”
Saturation controversy in qualitative research: Complexities and underlying assumptions. A literature review (2020, methodology): “Further emphasizing the controversy, Braun and Clarke (2016) submits that contrary to the conceptualisation of themes by Fugard and Potts (2015) as ontologically, clear and discrete things that are in the littered in the data, just like “diamonds” waiting to be picked, themes are determined and…”
Sample Size Justification (2021, positive): “FugardandPotts(2015)show how to move towards a more informed justification for the sample size in qualitative research based on 1) the number of codes that exist in the population (e.g., the number of reasons people have pets), 2) the probability a code can be observed in a single information…”