If you’re familiar with the basics of NVivo, the collection of tips below is designed to help ensure your next project is a successful one. For those who are newer to the software, you might find some of our previous blog posts helpful – Why use NVivo for qualitative research and New to NVivo? Some advice for beginners.

Tip # 1: Be Organised

Qualitative research is messy! One of our favourite reasons for using NVivo for qualitative analysis is that it helps us to organise our research data. Keep the following tips in mind for your next project:

• Organise your data using folders – you can create folders for different data types, locations, time periods or anything else relevant to your project.
• Name your files and folders simply, clearly and consistently – this will pay off later down the track when you start your analysis.
• Create a memo in NVivo to use as a research journal file – record everything here, even if you think you’ll remember it. NVivo also has a useful ‘Insert Date & Time’ function to help you keep records in your memos.
• Be clear about where your project is saved (you specify this when you create the project) and ensure it is regularly backed up.

Tip #2: Prepare Your Data

When it comes to data analysis, it’s very much a case of “garbage in, garbage out”! Ensure all data has been checked carefully for errors before importing it into NVivo. You should also check whether the data you’re importing needs to be in a specific format. For example, if you want to use the ‘auto coding’ function in NVivo, your transcripts will need to be formatted in a specific way. Likewise, Excel worksheets need to be structured in a particular way for NVivo to read them correctly.

Tip #3: Learn About Qualitative Data Analysis

NVivo doesn’t analyse your data for you – that’s your job as the researcher! If you’ve never analysed qualitative data before, it would be worth reading up on the subject before starting to code in NVivo. Otherwise, the process of coding may seem a little bewildering! Some of our favourite books on the subject are:

• Bazeley, P. (2020). Qualitative data analysis: Practical strategies (2nd ed.). SAGE.
• Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. SAGE.
• Gibbs, G. R. (2018). Analyzing qualitative data (2nd ed.). SAGE.

Tip #4: Stay on Top of Your Coding

The following tips will help avoid your coding framework getting away from you!

• Ensure each of your codes has a clear label/name and enter a description – this will be useful if you export a codebook from NVivo to keep you on track.
• Avoid creating too many codes for your analysis – there’s no ‘magic number’ of codes that you should have, but as a general rule, avoid venturing into the hundreds (unless your data analysis approach requires this).
• It’s normal for data to belong to multiple codes – qualitative data is rich and complex. If you’re always coding to the same two or three codes, have a look to see if these codes need to be combined.
• Review and revise your codes regularly – qualitative data analysis is a complex, cyclical process and your NVivo coding should reflect this.

Keep in mind that the above tips are generic advice – the appropriateness of these to your research will depend on the qualitative data analysis approach you’re taking.

Tip #5: Learn What Else is Possible in NVivo

While there’s nothing wrong with only using NVivo for basic coding, there’s a lot more to the software than meets the eye! Consider checking out some of the following functions:

• Word frequency and text search queries can be useful as data familiarisation techniques and to see if you’ve missed any coding. The word frequency query can also be used when you’re coming up with ideas for your codes.
• Coding, matrix coding and crosstab queries are useful for sub-group comparisons, looking for overlaps in coding and many other things.
• Mind maps are a helpful brainstorming tool when you’re developing your codes. Project maps are a useful way to display your coding structure. Concept maps can be helpful when you’re thinking through the findings, relationships, patterns etc. from your analysis.
• Explore diagrams, comparison diagrams and cluster analysis can be useful for looking for patterns in your coding.

If you’re interested in learning more about NVivo and some of the functionality discussed in this blog post, consider joining our Research Accelerator membership. The membership has over 60 hours of self-paced videos on NVivo and qualitative data analysis, along with regular live sessions where you can ask specific questions about your research.