Themes, variables, and the limits to calculating sample size in qualitative research: a response to Fugard and Potts
By N. Emmel. 2015
52 citations recorded by the source corpus.
Andrew Fugard and Henry Potts’ paper joins a small number of papers that seek to justify sample size through counting themes. Unlike the other two papers I can think of, Guest and colleagues and Fr...
Subfields: Applied Psychology, Social Psychology, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/69266c8ed7696f69c1b3a5c1edda0d525b91f434
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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Applied Psychology
In Social Psychology
In Sociology and Political Science
- Victoria Clarke
- Virginia Braun
- F. Ruffin
- S. A. Mthuli
- Nikita Singh
- B. Kaiser
- M. Hennink
- Favourate Sebele Mpofu
Citations in the corpus
- ‘Define, Explain, Justify, Apply’ (DEJA): An analytic tool for guiding qualitative research sample size (2021, background): “For instance, Fugard and Potts (2015a) article about using a quantitative tool to support thinking of sample sizes for thematic analyses generated debate (Braun & Clarke, 2016; Byrne, 2015; Emmel, 2015; Fugard & Potts, 2015b, 2016).”
- (Mis)conceptualising themes, thematic analysis, and other problems with Fugard and Potts’ (2015) sample-size tool for thematic analysis (2016, background): “…to conceptualise themes as ontologically real, discrete things, out there in the world (or the data), identifiable by researchers – like diamonds scattered in the sand, waiting to plucked-up by a lucky passer-by (though within their paper, there are varied definitions of ‘a theme’; Emmel, 2015).”
- Code Saturation Versus Meaning Saturation: How Many Interviews Are Enough? (2016, background): “…or frequency of an issue that contributes to saturation but more importantly the richness of data derived from an issue that contributes to understanding of it (Emmel, 2015; Morse, 1995): [A] mistaken idea about saturation is that data become saturated when the researcher has “heard it all” . . .”