An analogy to illustrate the importance of strategies driving tactics

Imagine I needed to get from London to Paris, and I needed to get there as quickly as possible (in other words, my strategy – my plan – was to get from London to Paris as quickly as possible). In order to fulfil my plan, I would consider the available tactics – in this situation, the different modes of transport available to me. I might choose to get on an aeroplane as the quickest way to get from London to Paris. But what if my strategy – my plan – wasn’t to get there as quickly as possible, but with the minimum carbon footprint possible. An aeroplane would clearly not be the most appropriate tactic for this strategy. Instead I may get on a train, or even cycle to the coast, swim across the channel and then walk to Paris.

The decisions we make about how to harness the powerful tools ATLAS.ti provides us, are similar.

There are usually several ways we could accomplish any given analytic task within the software, and so we have to make choices. Like our choice of transport, our choice of software tools has implications, which is why it’s so important to ensure the needs of our analysis drive our use of tools, on in the language of Five-Level QDA, that our strategies drive our tactics.

Doesn’t it work both ways?

When Nick and I first presented our pedagogy publicly, at the ICQI in 2016, a few people challenged the direction of the relationship between strategies and tactics we were emphasizing; that strategies drive tactics.

They suggested that the availability of tools affects what’s possible analytically, and thus can shape research design. For example, the ability to effortlessly, speedily and reliably count the occurrence of keywords across vast quantities of textual materials opens up content analysis approaches in ways not practically possible before those tools were available.

The emphasis of the directionality

It’s true that with the development of technology come new possibilities; that’s not something we reject. Indeed, it’s characteristic of technological progress, and one of the things that keeps me interested in the field of computer-assisted qualitative analysis.

Our point is that whilst tactics can inform strategies, strategies should drive tactics.

In other words, researchers absolutely should consider new tools, reflect and experiment with them to determine their potential, to see if they can usefully be incorporated into their practice.

It’s this open-mindedness coupled with practical and methodological reflection that we mean when we say tactics (tools) can inform strategies (methods).

But if that’s not done with careful consideration, if new tools are used because they appear to be a ‘silver bullet’, a magic weapon to solve the challenges of doing qualitative analysis, you can end up in a big mess very quickly, because you’re letting tactics (tools) drive strategies (methods).

I’ve seen the results of this in my workshops on many occasions, particularly when what appear to be ‘cool’ CAQDAS features are misunderstood and used inappropriately. An extreme example I’ve written about previously came from a student who expected they didn’t need to read any articles to do a literature review using qualitative software. Another example related to current technological developments would be if audio/video recordings of interviews, focus-group discussions or other encounters were transcribed using automated ai-driven tools (which have significantly increased in accuracy in recent years and months) without checking the resulting transcript for errors.

New tools, new possibilities, new ethics

Observing the ways generative-ai may change how we can do qualitative analysis is truly fascinating. It will be a while before things settle down and we get to see which of the new tools the qualitative community of practice will adopt into their practice.

At the moment there is much experimentation, and debate. Like when any new technology emerges, there are the early adopters and advocates, and the sceptics.

There are rightly many ethical concerns about the use of these tools. And there will be many continued discussions about them, and guidelines developed around these technologies; not just in terms of how we use them, but also how we report on their use, how we inform research participants about them, how ethics boards and publishers react to research that uses them.

Reaching a balance

But just like it would be inappropriate to use ai transcription tools to automate what is often seen as a tedious, time-consuming process, without checking the resulting transcript for errors, it would be a mistake to have generative-ai tools do their thing with qualitative materials (coding, summarising and so on) and accept the results without checking them or considering whether and how they actually add to our interpretations.

That’s why the difference between informing and driving is so important.

I’ve many times heard researchers say “I’ve done my coding, what next” – this sounds alarm bells for me in terms of research design and analytic planning. It will be all the more alarming if researchers use ai coding tools – like ATLAS.ti’s AI coding or Intentional AI Coding – to quickly “get coding done” and blindly accept it as accurate and meaningful without reviewing, refining and augmenting in relation to the analysis objectives.

Awareness is key

Although the ethical and methodological concerns that are being raised are valid, there are also misunderstandings about how the technology actually works. This is where the teachers of qualitative methods play a fundamental role.

Students will inevitably experiment and utilize these tools, and they will improve over time as the technology continues to develop.

Just like I would want and expect my children to learn about how to use social media platforms appropriately, I would want and expect students of qualitative methods to learn about analysis tools so they can use them appropriately.

This has always been one of my key messages – long before the advent of generative-ai. But it is even more important now. And that can only happen if researchers are aware of the options and the possibilities they afford.

So, experiment with ATLAS.ti’s AI coding, Intentional AI coding, Code-suggestions, Conversational AI, and AI Summaries. Attend webinars and training events that discuss the use of these and related tools in qualitative projects. Discuss the tools with your colleagues, students and advisors.