Qual and quant are too broad and blended – time to look at specific goals of the research
The tensions between qualitative and quantitative work often seem more tribal than reflecting methodological differences. The debate between Loder et al.  and Greenhalgh et al. , like that of many commentators, is framed as a binary opposition between quantitative and qualitative. However, qualitative research, as quantitative research, is a vast heterogeneous bucket of different approaches, many of which are still evolving. Loder and colleagues’ characterisation that “qualitative studies are usually exploratory by their very nature and do not provide generalisable answers”  is outdated. Qualitative studies can be definitive and change clinical practice, and the distinction between what is qualitative and what is quantitative is fuzzy.
Some qualitative research develops what looks like a taxonomy of experiences or phenomena. Much of this isn’t even framed as qualitative. Take for example Gray’s highly-cited work classifying type 1 and type 2 synapses . His labelled photos of cortex slices illustrate beautifully the role of subjectivity in qualitative analysis and there are clear questions about generalisability. Some qualitative analyses use statistical models of quantitative data, for example latent class analyses showing the different patterns of change in psychological therapies . Yet other qualitative research attempts to explain the mechanisms underlying some phenomena, for example through observation, as with the highly-cited work one of us did with Trish Greenhalgh on the adoption of electronic healthcare records .
We think that once you look at the more specific goals of research, the qualitative versus quantitative debate thaws. And looking beyond this crude distinction makes it easier to come up with creative approaches to new research.
Researchers wishing to construct themes, for examples of experiences of care, have the same quantitative problem as researchers designing clinical trials: determining how many participants are required. We recently published a heuristic to help researchers think about this for thematic analysis . Our approach begins with the basic idea that thematic analyses are searching for examples of phenomena. The more participants you interviewed, observed, or whatever, the more themes you will find, so you have to decide when enough is enough. Suppose you just wish to examine the most common themes, then not many participants would be required as those themes would turn up quickly. However, for rarer themes, more participants are needed. Making some simplifying assumptions, we developed a simple model for predicting the number of people you would need, drawing on a quantitative, probability distribution.
1 Loder E, Groves T, Schroter S, et al. Qualitative research and The BMJ. BMJ 2016;352:i641. doi:10.1136/bmj.i641
2 Greenhalgh T, Annandale E, Ashcroft R, et al. An open letter to The BMJ editors on qualitative research. BMJ 2016;563:i563. doi:10.1136/bmj.i563
3 Gray EG. Axo-somatic and axo-dendritic synapses of the cerebral cortex. J Anat 1959;93:420–33. doi:10.1038/1831592a0
4 Owen J, Adelson J, Budge S, et al. Trajectories of Change in Psychotherapy. J Clin Psychol 2015;71:817–27. doi:10.1002/jclp.22191
5 Greenhalgh T, Stramer K, Bratan T, et al. Adoption and non-adoption of a shared electronic summary record in England: a mixed-method case study. BMJ 2010;340:c3111. doi:10.1136/bmj.c3111
6 Fugard AJB, Potts HWW. Supporting thinking on sample sizes for thematic analyses: a quantitative tool. Int J Soc Res Methodol 2015;18:669–84. doi:10.1080/13645579.2015.1005453
Competing interests: No competing interests