![]() Pro: NVivo is fantastic for collaborative workĬollaboration is easy with NVivo for a number of reasons. 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. Within seconds you can produce ‘reports’ of data relating to particular codes or themes, frequency counts, word clouds or coverage statistics.įor me to provide a review of everything possible on NVivo would probably mean an entire new blog in itself. 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. NVivo is not restricted to textual data, you can analyse images, videos, web sources and (probably) much more. I’ve likely only used 10% of the program’s functionality. The range of functions available on NVivo is massive. 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. NVivo Pro: NVivo is an amazing program and you can do (what feels like) millions of different things with your data! Here are some pros and cons of each, based on my experiences. ![]() 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. “Which method should I use?” and “Can I use NVivo?” were two questions of hers and also mine a few years earlier. 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. ![]() Over the summer I’ve been fortunate enough to have an internally funded RA working for 8 weeks full time on the OPEN project. The idea for this post came to me following a conversation with a research assistant. 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. 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. ![]()
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