I’m continuing my commitment to do a bit more academic reading, and I must say that I am enjoying it. I am being quite deliberate in the books that I choose to read – trying to vary that which I normally encounter, as well as address some gaps in my own development as a researcher of which I am aware. One of the areas that I really wanted to make sure I was getting the most out of was NVIVO, which is a kid of qualitative data analysis tool that is in common use in the humanities and social sciences. I’ve used NVIVO for a while, but it was all very much self-taught, and I was conscious that I wasn’t getting everything from the software. I’ve watched a few videos, but I decided that I would bite the bullet, so to speak, and put this book, written by Jackson and Bazeley. It’s widely regarded as one of the best introductions to the software.
It’s good, and very helpful, and I left feeling much more confident that the next time I fire up NVIVO I will be better equipped to make use of it. The terminology, for a start, is much clearer to me – although it’s a source of mystery to me that every tool seems to feel the need to change the most commonly used terms into another term that’s just applicable in their setting – and confusingly, this can be a term that’s used for other things in other settings, too. In this case, both nodes and cases are used to mean quite specific things in NVIVO. However, what I wasn’t expected – but was very pleased to read – was the thoughtful and nuanced consideration of qualitative methodologies. With hindsight, it makes perfect sense – this isn’t a manual, but rather a consideration of how computers might aid that most human act of interpretation – but I wasn’t expecting it.
Here’s the ChatGPT version:
Qualitative Data Analysis with NVivo by Kristi Jackson and Pat Bazeley is a comprehensive and user-friendly guide that bridges the often-perceived divide between qualitative research methodology and the technical skills required to use NVivo software effectively. Now in its third edition, the book remains a leading resource for novice and experienced qualitative researchers alike, particularly those seeking to integrate rigorous qualitative analysis with practical digital tools.
Jackson and Bazeley bring a rare combination of methodological depth and practical expertise to the text. The book is organized in a logical and accessible manner, progressing from foundational principles of qualitative research to more advanced NVivo functionalities such as visualisations, coding queries, and working with mixed methods or team-based projects. What sets this guide apart is its emphasis on reflexivity and transparency in the analytical process, encouraging researchers not to let the software dictate their approach, but rather to use NVivo as a tool in service of thoughtful, theory-informed analysis.
Each chapter integrates conceptual discussions with step-by-step instructions and real-world examples, making complex procedures feel manageable. Screenshots, case studies, and reflective prompts support learners at multiple levels. The inclusion of updated content reflecting changes in NVivo’s interface also ensures the book’s continued relevance.
Importantly, the authors critique “button-pushing” approaches to software training and instead advocate for methodological intentionality. Their consistent emphasis on aligning research questions, epistemology, and NVivo use makes this not just a technical manual, but a pedagogically rich resource for research training programs.
Overall, Qualitative Data Analysis with NVivo is essential reading for anyone seeking to deepen their qualitative analytic skills while making the most of NVivo’s capabilities. It exemplifies best practices in both qualitative research and digital tool integration, and remains a gold standard in the field.