Tools for Analysing Data from User Research

Weighing up the different tools we can use for making sense of data

User research generates a lot of data. If you’re running user testing, interviews, ethnography, or anything where you’re capturing qualitative information, you’ll probably have hours of footage or recordings to revisit and analyse.

An often overlooked and undervalued skill for a user researcher is the ability to analyse and condense all of this data into something meaningful, useful, and representative of the views and behaviour of users.

The methods which can be used for analysing qualitative data can vary depending on the data and objectives of the research. Thematic Analysis is the most common approach – where a researcher sifts through the entire data set, searching for themes and patterns from which the findings of the research emerge.

So what tools can we utilise for conducting this type of analysis? Before delving into any of them, it’s important to point out that no tool will actually analyse the data for you – the responsibility to engage and understand qualitative data always lies at the door of the researcher – these tools can simply assist with sorting, coding and arranging.

Here, we look at software, apps and more traditional methods which can help you get to grips with a qualitative data set… 

 

1. The Heavy Duty Tool – NVivo

NVivo is widely regarded as the tool for analysing qualitative data. It’s purpose built for getting to grips with large qualitative data sets, and is packed full of features for importing, coding, connecting, visualising and representing data.

For many, this will be total overkill. It’s a fairly intimidating and overwhelming piece of software and you can usually achieve everything you need to using something much simpler.

2. The Traditional Method – Good old Post-It Notes

Personally, this will always be my preferred method. There’s something about the physical space that you can use with Post-It notes which makes the data so much easier to understand and visualise.

A clear downside to this method is the need to write out each post it note by hand. This can take hours. However, in my own personal experience this is often the best possible way to properly immerse yourself in the data. By writing out key points onto post it notes, your brain starts to join the dots and spot emerging patterns and trends as you go.

A Post-It wall can also be a fantastic way to collaborate and share your ideas with the rest of your team, allowing you to move notes and rearrange things based on your discussions.

3. The Digital Alternative – Post It Notes Plus

The Post It Notes Plus app has SO much potential, but has sadly barely been updated and improved since it’s original release in 2014.

The app allows you to create a digital post-it wall – you can either type or write out onto post-it notes and then arrange and group them on a board. This does work relatively well, particularly if you have an iPad.

However, a few things seriously let it down. Firstly, it’s buggy and prone to crashing, meaning you can end up losing your entire dataset with no way of recovering it. This happened to me on one occasion after over 5 hours of analysis and I lost everything (Yes, I’m still bitter).

Secondly, the app offers several ways of exporting your work, including to an Excel spreadsheet. However, it unfortunately exports every Post-It as an image! For a researcher this is far from ideal, as you’d really want it to be able to export everything in a textual format, so you have a usable database of your themes, quotes, comments etc.

Finally, the app doesn’t offer any form of collaboration. You can’t work with a fellow researcher on the same board, meaning its no where near as effective for collaborating on research findings as a real-world post-it wall is.

Verdict?

These are just 3 options, plenty of others are available but they all fall into similar categories to the tools mentioned above.

Ultimately, the tool that’s right for you may depend on how your mind visualises data and how deep you want to dive.

If your data set is so huge that post-its simply would not be feasible, a heavy duty tool such as NVivo may be the way to go. However, if time allows, my personal preference would always be to write out everything by hand and use paper post-it notes. Not only do I find this the best way to understand the data and visualise my findings, I also think it’s the best way of encouraging you to continually refine your thoughts. Whilst you can close down an application and put your research to the back of your mind, having a post-it wall physically present in your office can be a great way of keeping your data fresh in your head.

Which tool do you find works best for you?

Dr Sam Howard
Meet the author:
Co-Founder and Director of Research at Userfy
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