Setting sample sizes for quantitative studies can be done in an explicit way by means of calculations based on the concept of precision and a specified effect size (ideally based on a loss function). But qualitative studies are vague, based on notions of ‘theoretical saturation’. Problems:
- How much information is needed to satisfy the theory?
- How do you know how much information will be provided in the average encounter with an informant?
A small contribution to ameliorating the latter problem comes from the idea of the expected ‘information content’ of each encounter. The authors identify five factors that determine this quantity:
- The broader the aim, the larger the sample of encounters needed.
- Salient knowledge of people in the sample. The greater their knowledge, the smaller the required sample.
- The extent to which theory is already established. The more developed the theory, the smaller the required sample.
- The quality of likely dialogue. The more articulate the respondents, the smaller the sample size.
- Analysis type. An exploratory cross-case study requires larger samples than an in-depth analysis of a few, well selected, respondents.
Certainly a useful paper and aide mémoire. However, translation into the actual sample size required remains, let’s be honest, informed guesswork.
The CLAHRC WM Director is attracted to an earlier paper by Fugard and Potts, not referenced in the Malterud, et al. paper. The earlier paper proposed a logical calculation based on the three critical determinants:
- The expected prevalence, among respondents, of the least prevalent theme – this should be based on an explicit estimate of the prevalence of the least prevalent theme that the study should be capable of uncovering.
- The number of desired instances of the theme.
- The power of the study – the probability of detecting sufficient themes of the desired prevalence.
For example, to have an 80% power to detect two instances of a theme with a prevalence of 10% among encounters, 29 informants are required. Now that makes sense. This method has the considerable advantage of requiring the researcher to specify the prevalence of the theme that should not be missed in the sample. The CLAHRC WM Director would like to see this quantitative thinking incorporated and routinely used in planning qualitative research. He will write more generally on some of the problems in the way qualitative research is routinely framed in a future post.
— Richard Lilford, CLAHRC WM Director
- Girling AJ, Lilford RJ, Braunholtz DA, Gillett WR. Sample-size calculations for trials that inform individual treatment decisions: a ‘true-choice’ approach. Clin Trials. 2007; 4(1): 15-24.
- Malterud K, Siersma VD, Guassora AD. Sample Size in Qualitative Interview Studies: Guided by Information Power. Qual Health Res. [ePub].
- Fugard AJB, & Potts HWW. Supporting Thinking on Sample Sizes for Thematic Analyses: a Quantitative Tool. Int J Soc Res Methodol. 2015; 18(6).