NVivo is qualitative data analysis software, designed to assist you in your research. Unfortunately, it won’t find the answers for you, but it does a cracking job of organising your data and helping you to interrogate it. You can link your data internally and externally, use demographic variables, and explore relationships between participants and ideas.
In NVivo, an analytical project is broken down into two main parts: sources (the data) and nodes (containers for the coding of ideas or categories). Sources can be created in NVivo or imported to the project as documents of various types, such as interviews, field notes, project journals, images, or audio files. Nodes are created for any topic or category relevant to the project, and can be connected in ways to allow you to visualise and discover new connections. Sources or nodes can be explored either by browsing or by running queries. The ‘Externals’ folder of NVivo is used to link to documents held elsewhere that can be associated with your project. You can open the file in its native program and then record information on it in NVivo. The ‘Memo’ function allows you to add your thoughts and ideas on the project.
It’s quite difficult to grasp the potential of NVivo without having a particular project in mind. As a researcher in English Literature, it’s not an obvious tool I would expect to use. However, I was soon able to see how it would help me in my own work. For example, I’m currently working on an edited collection of interviews with late-Victorian women writers. In NVivo I can import the documents and then code the content to identify patterns. If I’m looking to compare their working environments, I could go through each interview to find where they mention this subject and then code it as a “working environment” node. In future, I could quickly generate a document collating all those references, either as just a list of citations, or as contextual paragraphs. The real power of NVivo is being able to quickly grab data that refers to a single theme or concept.
‘Cases’ are used for grouping together all data concerning a particular participant, and attributes can be assigned to them, eg gender, nationality, age, marital status. Once the data was in place for my project, I could use the search tool to find all instances of unmarried Scottish women writers talking about their working environment. The data can be generated as mind maps, showing the relationships and structure, and the results can be pasted into Word as an image.
I’ve only really scratched the surface of NVivo’s potential and there’s all sorts of other good stuff, such as the ability to analyse and annotate sound and video files. The main disadvantage is that it’s not a particularly intuitive package and requires a certain amount of commitment to overcome the initial brain pain. My preferred learning style is a chunky manual and a large cup of tea, but NVivo really needs a workshop. Fortunately, Technical Skills for Researchers (formerly SciPS) will be running sessions throughout the coming academic year. We’re also organising a surgery for existing NVivo users so they can get help with their own data.
NVivo has much to offer those whose research involves interviews or case studies. Although not vital in my particular field, it is nevertheless a useful tool and one that I shall continue using.