NVivo, Word or good old-fashioned pen & paper: pros and cons of three qualitative data analysis tools

This post is similar to the previous one in that it is my attempt to pass on nuggets of wisdom gained through trial and error. The last post was very broad whereas this one will only be of interest to those qualitative researchers weighing up which tool to use in their analysis. The idea for this post came to me following a conversation with a research assistant. Over the summer I’ve been fortunate enough to have an internally funded RA working for 8 weeks full time on the OPEN project. Like many qualitative researchers (including myself post-PhD study 2), my RA was transcription weary after many days spent in headphones in front of ExpressScribe ready to get stuck into the nitty gritty of data analysis. “Which method should I use?” and “Can I use NVivo?” were two questions of hers and also mine a few years earlier. The discussion we had around my thoughts on the pros and cons of different data analysis tools was very much informed by my experience, having trialled all three of the methods mentioned in the title of this post. Here are some pros and cons of each, based on my experiences.


Pro: NVivo is an amazing program and you can do (what feels like) millions of different things with your data!

nvivo 10

I’m by no means an NVivo expert. I’ve used the programme on three different projects to date, two of my own and one I was employed on as a research associate. The range of functions available on NVivo is massive. I’ve likely only used 10% of the program’s functionality. NVivo is not restricted to textual data, you can analyse images, videos, web sources and (probably) much more. In terms of the nitty gritty of data analysis, NVivo is great because your codes are clearly labelled, code labels can be edited, codes can be moved around, deleted or merged into others, whilst at the same time NVivo recorded the date, time and author of any new codes so that you can re-trace steps. Within seconds you can produce ‘reports’ of data relating to particular codes or themes, frequency counts, word clouds or coverage statistics.

For me to provide a review of everything possible on NVivo would probably mean an entire new blog in itself. The point I’m trying to make here, I guess, is that NVivo is a flexible and sophisticated tool for handling and analysing qualitative data.

Pro: NVivo is fantastic for collaborative work


Collaboration is easy with NVivo for a number of reasons. Because NVivo records the author, time and data of any changes to codes and coding, collaborators can easily trace their steps (and others). Because data sources are neatly stored together in project folders, collaboration can be a simple as ensuring that the NVivo file being worked on is the most recent one.

Con: NVivo is inaccurately heralded as the best way to analyse qualitative data

NVivo meme

With its many functions and means of presenting data and coding, NVivo is often heralded as the best tool for conducting qualitative data analysis. Often this is seen more implicitly in methods sections of papers with only one or two sentences about data analysis which include “All data was analysed using NVivo 10” with little other detail. Of course this may be due to restrictive word limits particularly for qualitative empirical articles. My interpretation is that this is also due to the illusion that NVivo is doing more than it actually is. NVivo is ultimately a data management tool. It provides a means to store, code and report data. It does not analyse data – well not in a qualitative way at least. Stating that NVivo was used to analyse data is no more informative than stating that Sony recording devices were used to record your interviews. So for those of you who haven’t yet dabbled in NVivo – don’t worry, it is one way of analysing data but certainly not the only or the best way.

Con: NVivo makes it too easy to disengage from your data


Those new to qualitative data analysis often mistakenly imagine NVivo is a qualitative equivalent to SPSS. Enter the data. Click. Click. Check box. Select ‘options’. Check another box. Check one more box. “Run”. And in seconds, data is analysed and ready for interpretation. As stated, when it comes to qualitative data, NVivo is predominately a data management tool. The danger with NVivo, particularly for novice researchers, is in it’s ability to disengage the analyst from the meanings within the data. Coloured sections of text are compelling and coding can easily become like a game of Tetris, an attempt to keep clicking until you get to the bottom of a transcript. This tendency can be combated and indeed most researchers do use NVivo to enhance their data management and analysis.

Con: NVivo takes a little time to learn to use

Time for that

Like any unfamiliar program, a first look at NVivo can be overwhelming. Even navigating the basic functions can take some time. Many people, myself included, have attended training courses. There are also tons of useful videos on YouTube and lots of other guidance across the web. Getting your head around the language of NVivo is all part and parcel of your induction. Learning about parent, child and sibling nodes, external and internal sources and reports. All of this takes additional time that you wouldn’t have to invest if you were using a data analysis tool that you were already familiar with.

Microsoft Word

Pro: You don’t have to spend hours learning to use a new program

On the flip side to the above, using Microsoft word to analyse data has the benefit that you don’t have to translate NVivo’s terms. You don’t have to locate functions and spend hours generally learning how to use a program. Word’s review tab allows data analysts to perform many of the functions available on NVivo – coding, coloured highlights, text search, author identification. With just the use of the comments function and text highlight, Word offers an accessible way of analysing data electronically.

Con: Word lacks the sophistication of NVivo

microsoft office paperclip

If you’re going to analyse data electronically then NVivo offers a far greater range of options for storing, coding, locating and presenting data. You may however, not need all of that sophistication and functionality. If you have a small data set, are working solo, have text only data or are conducting a semantic level thematic analysis or something similar, Word may do the trick just fine. You may just find that you need to reassess your comments or colour coding intermittently and would probably want to use a different document, program or pen and paper when it comes to searching for themes in your data.

Good old-fashioned pen and paper

Pro: Pen and paper ‘feels’ more engaged

I hate reading articles from a screen. During my undergraduate degree I started using a Kindle to store and read articles, which helped with printing costs too! Having your data in front of you in hard copy, just feels different. It feels more engaged, more like you yourself are doing the analysis. Reading through the data physically and coding physically with highlighters and hand-written comments, feels satisfying and methodical. You can’t see word counts, node counts or the percentage of data coded but you can see and feel the dog-eared pages of data and your hands dotted with fluorescent ink. And I don’t think I’m just being nostalgic. When analysing data by hand you can flip back to a code you noted a couple of transcripts ago with ease, rather than fumbling over files or waiting for your PC to open a document. The hand written thematic diagrams and lists that accompany your colourful pages of data serve to ice the data cake.

Con: Pen & paper based analysis is not flexible

bad hand writing

The satisfaction that comes with the methodical nature of analysing data by hand can be disrupted when codes need to be merged or re-coded. When themes don’t quite fit or the input from a second researcher sheds doubt on your interpretation, pen and paper is more problematic. Most forms of qualitative data analysis are iterative, they require the researcher to move back and forth between steps in analysis, refining and re-interpreting as they go. All of this can be done electronically with ease but when your pages of data are already crammed with annotations, ink smudges and post-its, your analysis begins to look messy. On top of that, if your handwriting isn’t the best, your colleagues/supervisors patience may wear thin when attempting to give you input.

So, which tool should I use?

I use NVivo now and probably will from now on. This is because I’ve invested time in learning how to use it and I like the range of functions on offer. For me, good old-fashioned pen and paper has just a few too many cons. Word is an excellent best of both worlds for those who can’t or don’t want to navigate a new program in order to analyse their data.


Doing a PhD: Six things I’ve learned

A few months ago I gave a keynote talk at my department’s 4th annual postgraduate psychology conference. This was a real pleasure (and a little surreal) as it was Jenny Taylor and I who set up the first of those annual events four years earlier. 

I was asked to do a talk on ‘my PhD journey’. This was terrifying daunting for two reasons.

Reason 1: Up until then I’d given lots of talk on lots of different aspects of my work but never given a talk about myself. Even where I’d talked of research challenges and even reflexivity, the presentation was always about my work and not about me.

Reason 2: My prospective audience was to include not only current postgraduates but prospective PhD students, current colleagues, mentors and my PhD supervisors; a consequence of returning to work in the department where I did my PhD.

Though apprehensive about talking about myself and doing so in front of my peers, I decided to embrace the challenge. I prefaced my talk with an explanation of how it would be an open, honest account of my experience rather than a sales pitch to attract PhD applicants. I also sourced some anonymous quotes from friends and colleagues about their experiences. The following is taken from the content of my talk titled “Doing a PhD: Six things I’ve learned” in the hope that others might learn from it.

Lesson 1 – Get involved in everything and anything*


I remember being told once by a senior academic that a PhD is more of “an apprenticeship in academia” than anything else. Though my PhD was full-time, I tried to squeeze in as many different academic experiences as possible (partly because I had difficulty saying no to opportunities). These included:

  • Research assistant opportunities
  • Volunteer opportunities
  • Organising and chairing conferences
  • Open days and community days
  • Research networks
  • School initiatives
  • Teaching

*but remember to prioritise your PhD (not always easy!)

Lesson 2 – Engage in your field in different ways

Lesson 2





I separated out lessons one and two for a reason. As important as it is to get involved in activities in your school or the university more widely, it is equally important to get involved in your field. There may only be one or two academics in your department or university that are experts in the same specific thing that you are becoming an expert in. Find others like you. The internet is wonderful for this. Things I did or wish I had done:

  • Attended conferences
  • Published
  • Attended research meetings and events
  • Reviewed articles
  • Joined twitter chats
  • Followed and contributed to blogs
  • Read, read and read some more

Lesson 3 – Talk about your work and your ideas

Lesson 3

Yes, because conference presentations help to build your CV but talking about your work helps in so many ways. Talking about my work helped me to:

  • Make sense of my ideas, especially in the early days
  • Get feedback from others
  • Look at my work from a different perspective
  • Place my work in the context of my discipline
  • Meet other academics

Lesson 4 – Listen to your supervisor*

Lesson 4

Stress does weird and interesting things to us. When stressed, our supervisor’s constructive criticism can feel more cutting and we can take comments too personally or get overly defensive. Remember that your supervisor’s job is to help you grow as an academic and sometimes this means nudging you out of your comfort zone or requesting a fifth re-write of that chapter. Some things I learned:

  • Get feedback on your writing early
  • Expect them to challenge you
  • Communicate and meet regularly
  • Talk through your work and ideas

*but also be proactive and take initiative

Lesson 5 – Look after yourself

Lesson 5







During the course of my PhD I moved house five times, experienced a family bereavement, health issues and other general life stresses. I had to remind myself to:

  • Take breaks
  • Make times for the things I enjoy and the people I care about
  • Speak with other PhD students. They are a great source of support

“Life if what happens when you’re busy doing other things”

Lesson 6 – Enjoy it and celebrate successes – big and small!


Your PhD project is yours and yours alone and that sense of ownership (despite bringing pressure) is an amazing thing! Enjoy it, remind yourself of what you are doing and why and celebrate every little achievement. Things I celebrated included:

  • Finishing a chapter
  • Passing progression
  • Collecting my first piece of data
  • Finishing transcription
  • Getting my progress report
  • A successful meeting with project partners or my supervisor
  • Writing 500 words on a chapter I’d been struggling with
  • Organising my literature
  • Finishing my Appendixes

These were my lessons learned, feel free to share yours in the comments section…

Sun, strawberries, and social representations theory: ISCHP 2017

This week I attended my second International Society of Critical Health Psychology Conference – a good time for a first blog post!

It had been four years since my last ISCHP. Back then I was in the early days of my PhD research and the Bradford conference opened my eyes to a world of passionate critical health psychologists. I was very much looking forward to Loughborough 2017 and it certainly didn’t disappoint. From arriving on a sunny Sunday afternoon to a reception of bangers and mash, and strawberries and cream, to the final (and very inspirational) keynote on the Wednesday by Dave Harper  the whole 3 days were just fantastic. Unlike many other conferences where I feel very much on the margins as a critical social psychologist, I feel at home at ISCHP. My impression of ISCHP is that it is a critical space through and through, embracing scholars from many different theoretical and methodologically orientations and addressing a HUGE range of social and health concerns. There appears to be an understanding across the board that the most popular way of doing things is not necessarily the best or most effective one.

Scanning through the conference programme was not a case of locating where ‘my kind of talks’ were on and when but instead (refreshingly) having to face tricky decisions about what to attend and what to miss out on. Two of this events themes (‘diversity and inclusivity’ and ‘ageing’) summed up much of my research and interests, adding to dilemmas over which talks to attend. I thoroughly enjoyed talks on ageing and issues such as social inclusion, physical activity and sexual health. Many of the diversity and inclusivity talks touched upon the challenges of conducting good quality, ethical co-produced research with ‘disadvantaged’ or marginalised communities – these were most definitely relatable.

Presenting in one of two symposia on the theory of social representations was a personal highlight. For me this was an opportunity to position my work in the context of this fascinating and evolving theoretical framework. Between the two symposia, eight scholars (including colleagues at Keele: Michael Murray and Jenny Taylor) presented research on innovations in theory and methodology. It was inspiring to see SRT used to underpin novel methodologies such as film analysis and also to see people exploring different theoretical combinations to better understand social issues.

I came away from ISCHP 2017 feeling inspired, energised and motivated to crack on with the paper I’m currently working on! On top of that I met many friendly like-minded academics who I hope to cross paths with in the future. Already looking forward to ISCHP 2019!