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.


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

  1. Thank you so much for this fantastic article. I am an evaluation consultant wondering whether to invest in NViVo and this answers many of my questions. Thanks again, it’s a great resource I’m sharing with others now.
    I’d love to know if you have experience of Kobo Toolkit software though this is more useful for field research in remote contexts.

  2. I am a qualitative researcher and it is very often clear when a published article is based on use of nvivo: there’s no analysis! There’s a tendency to ram-raid the data for appropriate quotations, which are divested of all necessary context. Nvivo is poor at collecting anecdotes and processes which take extended description. My preference is to use a combination of word and excel. I create a standardised form in word for each respondent based on the interview schedule to order data that I enter from transcriptions (which allows for summary and the entry of ‘no data’ or non-response) and is time for deep engagement with the material, and then transfer that material to separate excel spreadsheet sorted by theme (in columns), with a line for each respondent. I use the formatting tool then to highlight each cell in whatever way is helpful. Generally I retain verbatim quotations in italics. I used this method for a 60+ interview research project recently, which was remarkably effective in establishing how this respondent group could be broken down and drawing out the play of themes between and within groups. Easy to print out the spreadsheets but actually I found I didn’t need to – making the screen smaller meant that it was easy to see how the colour coding was panning out over a large sheet.

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