New Book: Making Qualitative Research Happen

I’ve been thinking and talking about the role of tools in qualitative data analysis my whole career, with a focus on dedicated digital tools designed for the purpose (aka Computer-Assisted Qualitative Data AnalysiS (CAQDAS) packages. So as soon as OpenAI released ChatGPT and research communities began debating the capabilities and consequences of LLMs and Generative AI tools, I experimented with the plethora of new tools, reflected on their implications for QDA practice and knowledge production, and incorporated critical discussion of them into my awareness-raising and capacity-building remit.

Much of that research and thinking has been shared via blog posts, webinars, keynotes, podcasts, panel discussions and training events. For example, the series of posts of which this is a part, and my YouTube Playlist on Qualitative AI.  In addition, in my role as Director of the CAQDAS Networking Project, is the CAQDAS blog, which includes posts by myself and guest authors on aspects of the use of AI in qualitative analysis, and the QualAI pages where we are collating resources on the topics.  

So, I’m excited that the first of several more formal pieces I’ve written on the topic is now published, in a book that, as its title suggests, focuses on making qualitative research happen.

The book includes 21 chapters, taking the reader through the qualitative research journey in six parts:

1.      Epistemology and meta issues

2.      Design and plan

3.      Gather data

4.      Manage and handle data

5.      Analyse and transform data

6.      Write and report

Many chapters are written by J.T. Weisser, Cathy Gibbons, and Daniel Turner, who have written dozens of posts on aspects of qualitative research and analysis on the Quirkos blog.

Others, including mine, are invited chapters authored by qualitative researchers and methodologists. It’s great to be in the company of authors including Mónica Sánchez-Hernández, Helen Kara, Anuja Cabraal as invited authors.  

Actually, I was initially a bit surprised to be invited to write a chapter on the use of Generative AI for this book because I know that Daniel Turner, lead developer and Founder at Quirkos, is sceptical about the use of GenAI for qualitative analysis. Indeed, as he writes in the chapter preceding mine, “there are lots of reasons to avoid this practice” (p221).

However, openness to discuss the relationship between technology and methodology on its broadest sense that the invitation represents, is characteristic of the Quirkos' approach, and indeed that of most developers of CAQDAS packages that have their roots within academic/applied contexts and the goal of enabling high-quality analysis at their heart.

My chapter in this book is a relatively short one - it will cost you only about 20 minutes of your time to read! In it I cover the following:

• A little bit of historical context, with reminders that although Generative AI is new in the QDA space, other forms of AI are not - see also here

• A description of the core genres of GenAI tools being used for QDA, which I first wrote about here

• A high-level overview of GenAI capabilities that are being harnessed for QDA

• A visualisation of the intersection of GenAI capabilities and broad stages of the qualitative workflow

• The risk of “qualitative deepfake” and why it matters, also initially written about here

• A discussion of the most important matters: ethics

• Some reflections on what the future might hold

If you follow my work, you’ll know I take a pragmatic and balanced approach to the question of how technological capabilities relate to methodological imperatives. And you’ll see that in this chapter too. Indeed, knowing that my pedagogical approach is anchored in enabling researchers to make informed choices about tool use, rather than to promote particular tools or methods, is perhaps a reason I was invited to write a chapter on the topic for this book.

Because, there is no one-size-fits-all answer to whether or how qualitative researchers “should” or “shouldn’t” use GenAI. Why? Because every project is different and therefore what is appropriate in one project might not be for another. Hence why if you take those 20 minutes to read my chapter, what you’ll get is an overview of the landscape and some insight into some key questions to consider, to take away and consider for your needs, not a recipe for what you should be doing.

I don’t advocate the use of Generative AI for qualitative research and analysis, but I do advocate being aware of what it can actually do – and what it can’t – and most importantly, that researchers make informed decisions about whether and how it may or may not be appropriate for each individual project.

This book is an excellent resource for qualitative researchers, whatever tools they choose to use. So if you're not keen on GenAI, don't be put off from reading this book because of my chapter - there's so, so much more in the book.

As the back cover states, this reference book covers:

• choosing a good research question

• exploring social theory

• designing research with intention

• collecting valuable data

• analysing data whatever tools you choose to use

• presenting findings to different audiences

• writing up

If you are interested in GenAI and QDA watch out for other writings penned by me on coming soon in a number of additional publications. And in the meantime, check out the series of posts I’ve written previously on the QDAS blog and the CAQDAS blog.

To find out more about the book, Making Qualitative Research Happen, go here...