The Association for Qualitative Research
The Hub of Qualitative Thinking

Never the twain shall meet?

One of the hot topics at AQR's recent conference was the cross-over or even convergence of qual and quant research. In Brief looks at where this might be heading.

It’s a topic that keeps rearing its head, witness Riki Neill’s piece in the last In Depth, in which described how he used Chaos Theory and mathematics to make sense of Big Data and predict shopper behaviour.

Freeze frame

So since In Brief’s brief is to monitor trends, and change, we decided to ask a selection of researchers where they stood on convergence. The answers make for a fascinating snapshot of an industry in flux. Digital is, it seems, blurring the lines — at least superficially — when it comes to client interface, with quant debriefs peppered with verbatim quotes, videos and pictures. Use of the net for data collection means they are getting closer together in the online space, too. But what does this mean in real terms?

“In some ways, the pressure is on for qualitative research as an industry,” says Chris Barnham. “I can’t see qual becoming more like quant, but I can see quant becoming more like qual — without the analysis. The way forward is for qual to up its game, and re-establish its credentials.”

It’s easy to forget, though, that ‘qual’ and ‘quant’ have always been part of a spectrum of research information. Consider grounded theory, where experiments demonstrated that talking to 30 or so people will identify most of the main issues, and that thereafter researchers are in the business of attempting to measure those issues. Consider, too, the fact that 20 years ago or so quant — like qual — was carried out face to face, by interviewers in the street or going door to door.

“Personally,” says Andy Dexter, “I’ve always been of the view that all data is qualitative, and that we’re in the business of pattern recognition, not absolute accuracy (particularly true now). The difference today is that with tools such as easyaccess multivariate data reduction techniques, coupled with methods of analysing unstructured data via, for example, text analytics, ‘quant’ can genuinely be treated as ‘qual at scale’.”

At AQR’s recent conference, his session on Brexit won the award for best contribution. He had asked people to tell him in their own words what Brexit meant to them. In the safe space of an anonymous online survey, some of the responses were, in his words, ‘astonishing’. “When subjected to text analytics, we were then able to form vocabulary clusters that said more about the participants’ value systems than any attitude scale,” he says. “Admittedly, much of this convergence happens, or is facilitated, online — it’s a natural platform for this kind of convergence.”

If convergence is a done deal, at least for certain projects, there could be a need for a new descriptor, too. Are we talking about qual-quant, quantl or something completely different? Or are we behind the times anyway? Happy Thinking People has been working in this space for over 15 years with eTrack, an agile pretesting tool combining the sensitivity and exploratory advantages of qual with the robustness and validity of quant — all in the space of a day, face to face.

“Online communities do play a role in easing the ease of interplay between qual and quant,” says the company’s Edward Appleton, “but that’s not what drove our quant-qual offering. The tool is a ‘digitally enhanced’ face-to-face method, meaning that for the quant part participants are invited to a central location, engage in (for example) a gallery walk of different concepts or products, each armed with an iPhone, so that they can give their individual responses. Clients can observe in real time: actual facial reactions, body posture, as well as the build of response patterns appearing on their dashboard. A preselected group then stays on for a focus group to delve deeper into concepts performing well, explore ways of making them even better.”

Maybe there is a call for a workshop to discuss how far convergence has gone, and what quant methodologies can be used to make qual more effective. There again, could it be that this convergence is happening more readily online than offline? If so, the emphasis on online might be distracting the research community from any convergence that’s happening offline. “I’d say that, in my world, (I come from a qual background but run a lot of online communities), I certainly apply more quant principles to my research online than I do offline,” says Felicity Adkins of LWR Tonic. “We are, though, beginning to do the latter more (e.g. pulse responses in workshops).

Need for a fix?

“I think the techniques used in traditional face-to-face ‘offline’ research have been around for such a long time, we’ve had less of a need to find convergence between quant and qual. I’m not saying we haven’t innovated in offline qual, but why fix something that’s not broken? Whereas with online communities, we’ve had more ‘permission’ to blend techniques and experiment. Because they’re a ‘newer’ methodology, there’s almost an expectation for us to not be traditional in that sense.”

In the end, the future make up of the industry is probably in the hands of clients. There is a perception that — over and above the pressure they put on researchers to deliver ever faster and more cost effectively — that they need to be entertained. Attention spans are shorter, they want the visual fix that digital can provide. Where does this leave qualitative research? Hopefully
banging its drum.


Louella Miles
Copyright © Association for Qualitative Research, 2017