A lot of qualitative data analysis involves working with text data. There are many ways of working with text data, including using qualitative data software like NVivo, but also using a text editor or processor (e.g. Notepad/TextEdit, Word or Google Docs), a spreadsheet, or a coding language like Python.

Using text editing tools

If you have text data, it makes sense that you might use text editing tools. These can do basic searching for text strings, often given numerical outputs for search terms, and you can use find and replace features to clean up text data.

You might use a text editor like Notepad (on Windows) or TextEdit (on Mac) to work with plain text (that is, text that is not visually formatted). You might have files in a .txt format that have been saved as plain text. These can do find and replace and search files, and can be good to use with coding languages like Python when going further with text data analysis.

You could also use word processing tools like Microsoft Word or Google Docs. These are designed for creating documents, but you can also use them to find and replace text data if needed.

If you're interested in a more advanced example of using find and replace features in text tools, The Programming Historian has a lesson on Understanding Regular Expressions which uses a word processing tool to manipulate a text document using regular expressions, a way of expressing more complicated search patterns for text data. If you want to learn more about using regular expressions, or regex for short, there are many helpful resources out there, for example RegExr.

Using spreadsheets

If you have text data in a structured format like CSV, then spreadsheets can be a useful way to work with this data. Again, you can do searching and use find and replace to look for particular terms, as well as using filtering features to drill even further into text data. You can also transform data in various ways, depending on its structure. The Spreadsheets guide goes over much of what you'll need to get started:

Using coding languages

If you need more customised functionality when working with text data, or want to be able to automate analysis, then coding can be very useful. Python is one example of a coding language often used for working with text data, and our Python guide page has some suggestions for online resources to explore related to text data:

The Programming Historian also has a lesson on Cleaning OCR'd text with Regular Expressions using Python, if you're looking for an example of how Python has been used to work with text data.

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Qualitative data analysis is the analysis of things other than numbers — usually text information. It's mostly a case of just reading stuff, but it can also be advantageous to find ways of quantifying content.

NVivo is a qualitative data, text management and organisational tool which enables an analysis of very rich text-based and/or multimedia information, where deep levels of analysis on small or large volumes of data are required. It is often used for qualitative research and literature review.

Analysing spoken content

NVivo can be used to analyse more than just text-based files: images, audio and video can also be marked up. If you have an audio file of an interview, for example, you can annotate it directly in NVivo, without the need for transcription:

If you do feel the need for a transcription, you're going to have to create it yourself (unless you can persuade somebody else to do it for you!). To save time, you could transcribe directly into NVivo and code as you go; or you could choose to code it up in some other way.

Auto-transcription is possible (though seldom very reliable). NVivo offers a paid transcription service, but there are free alternatives such as the ones we discuss on our Subtitling Skills Guide (if you're conducting an interview over Zoom, you can also enable its built-in auto-transcription). Be aware, though, that even with an automatic transcript, you're going to need to do a lot of work if what you're actually wanting is a perfectly punctuated verbatim text. It may well be nearly as quick to transcribe it manually.

If you create a transcript that you're wanting to analyse in NVivo, be sure to make use of Styles in your document. This will allow you to do some basic auto-coding (i.e. to identify the interviewer and interviewee). You can also sync a transcript to a video though this might require some preparation if you're working from a conventional subtitle file.