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How does NVivo work?

This guide covers the absolute basics for how to decide if NVivo is useful and how to get started with NVivo. What is NVivo?

It is a database system designed to facilitate the process of qualitative data analysis. What does it do?

It lets you upload files (transcripts, documents, audio files, pictures), select parts of the document and label them with particular descriptions. Then you can retrieve everything with a particular label and read them alongside each other

Is that all?

It also produces word frequency counts, charts of the labels you use most often, text searches, and so on. You can organise the labels into categories, make notes about links between labels and concepts, and create relationships between labels and documents.

Is it worth using it? Yes, because it...

• Speeds up retrieval • Allows you to manage more data • Allows you to incorporate more data of different types (video, audio, text) • Encourages you to be more systematic • Helps make connections across lots of different sources

And no, because it... • Takes time to learn • Does NOT speed up analysis or coding • Does NOT do your analysis for you • Does not help with small amounts of data • Can lead you to take extracts or quotes out of context (this is a common criticism of

computer assisted analysis, but there are ways around it – just be aware) So if I use NVivo, do I still need a method of analysis?

Yes. NVivo is not an analytical method. Analytical methods are things like thematic analysis, critical discourse analysis, content analysis, semiotic analysis and so on. NVivo can be used with any one of these systems but it does not replace them. That’s why people refer to ‘CAQDAS’ – computer assisted qualitative data analysis software. The software assists you in the analysis, but that’s all it does. It’s basically a really sophisticated filing system.

Here’s what you need to know – some key vocabulary.

● File = a ‘source’ ● Label = a ‘node’ (a code, in qualitative analysis language) ● Notes = a ‘memo’ ● Retrieve everything = run a report ● Search the text = ‘create a query’

NVivo is particularly suited to thematic analysis, and you can refer to the CfAP Guide here. However, it can also be used with other analytical methods. If you’re not familiar with qualitative data analysis, you will find it easier to learn about this in general first and then come back to this guide later.

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Should I use NVivo to analyse my qualitative data?

1. How much data are you collecting? a) Fewer than 10 interviews/ texts / images etc b) More than 10-15 interviews/ texts / images etc

2. How big / long are your pieces of data (interviews/ texts / images)? a) Interview under 30 minutes / text under 2-3 pages each / small simple images b) Interviews at least 45 minutes or an hour each / text over 5 pages each / large complex

images

3. Are you collecting different types of data? a) No – just interviews OR texts b) Yes – e.g. video, observation notes, and interviews

4. How rich or complex is the data? a) Each interview / text / image has one or two key themes or points for my analysis b) Each interview/ text / image has loads of key themes and interesting points I want to pick

up on

5. Will your analysis need to link different parts of the data together? a) No, I can just deal with each question / topic separately b) Yes, different themes will probably come up under different questions / topics

If your answers are mostly a)s, NVivo will probably not add to your project. In the time it takes you to learn the software, you could be finished with your analysis. If your answers are mostly b)s, NVivo could be useful to you. It might help you to organise a large volume of information and make sure you are really systematic in your approach. Disclaimer: these numbers are rough estimates only – the key point is that NVivo is great for dealing with a big quantity of rich complex data, but learning new software takes time. So you need to decide if it is worth it.

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Getting started

Step 1: Create a ‘project’, i.e. a new database. Open NVivo and you will see this screen.

Click on ‘create new project’, and then afterwards you can click on your project name under ‘Recent projects’. For practice, we recommend you use the ‘Environmental Change Down East’ practice project. Step 2: Add some data. Once you’ve got your project (or the practice project) open, click on ‘External Data’ on the main toolbar. Then choose the right file type for your data, click the icon and browse to find your documents.

Top tip: Read all your documents first, twice, BEFORE you get stuck into NVIVO. This will give you an idea what your main themes and concepts are likely to be and speeds up your inputting.

You can add and code all sorts of multimedia using NVivo. The rest of the guide refers to text for ease, but the methods are the same for audio, image and other file types. However, the process of transcription, while time consuming and tedious, helps you to become familiar with your data and you will start to see emerging patterns as you do this. Coding directly from audio or video may cause you to miss key points. So don’t skip transcription because it’s difficult; embrace it!

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Top tip: give your documents names with the key things you need to know about them (e.g. 3rd interview w Pat 23 Feb re identity). This will show up in your code report and help you keep a sense of context. Be consistent in your naming patterns and consider what information you will need when you start to write about the data.

To keep sources organised, create folders. Right click on the Internals folder under Sources on the left, and select ‘New folder’. This creates a subfolder so you can find your sources more easily. This is what you will end up with:

Question: are you going to code everything in your sources? How are you going to decide what to code and what not to code? E.g. small talk at the beginning of an interview might not be coded, but what is your criteria for excluding something? Be consistent in applying this criteria and state in your data analysis section in the methodology.

Step 3: code your data. If you’re working from a text document, click on the folder, then the source that you want to work with (see above). Now select a relevant portion of text, e.g. a

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sentence. Decide what you want to label it (note that if you are using a deductive coding approach, you should already know what your codes are).

Question: what are you actually looking for? Actions, emotions, ideas, reactions, solutions, experiences? Having a clear idea before you start coding will help you to stay focused.

In this example from the practice project, a choice of existing codes is available from the drop down menu called the ‘quick coding window’.

Choose one, then click the green arrow next to it. The label is now attached to the text. To un-code it, select it again and press the red cross.

Question: where will you get your codes from? Will they be descriptive, or will you use the same words from the data (i.e. a nice phrase from one of the participants)? Will you apply terminology from the literature? Figure this out before you start coding, and if you’re confused read Johnny Saldana’s Coding Manual for Qualitative Researchers.

Question: what unit are you coding? Individual words, phrases, sentences, a whole response at a time? You don’t need to make up your mind once and for all before you start, but keep it in mind so that you don’t end up with 10 different codes, each for a different word in a sentence. Question: are you going to code some phrases to 2 or more different codes (simultaneous coding)? On one hand, this offers a great insight into the ideas and themes which overlap. On the other hand, it creates a significant volume of new information. NVivo allows you to do this with no problem, but consider setting yourself a maximum for how many codes you apply to the same piece of text to keep things manageable. If you are always applying the same 3-4 codes together, this is a good clue that they may be relating to the same themes and may help you to organise your coding hierarchy (see step 6).

To create a new code, right click on the highlighted text and choose ‘code selection’, then ‘code at new code’. Write the name of the code in the box that pops up, and a description or definition of what it means.

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Top tip: seriously, write the description. You think you know what you mean now, but will you remember in a month? It’s also worth writing down what this label does NOT include.

You can also do this by going to the ‘create’ tab on the top menu toolbar and choosing ‘node’:

Top tip: When you introduce a new code, if you are under way with your coding, check through the sources you’ve already coded to see if it applies there

Step 4: explore your data. (NB: This might happen during step 3, before, after, or really at any point). You can do a simple text search to find out if and where certain words are used. You can find out which words are used most often (word frequency). You can even code these results automatically by storing the results as a new node. But do be careful with this: auto-coding is a blunt instrument, and the point of qualitative research is that humans do it, with their awareness of nuance and subtlety. So there may be instances that require additional interpretation, or are a mis-match and don’t accurately reflect the code. Uncode these manually, or re-name them

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It might be useful to view the extracts in a wider context, which you can do by going to ‘View’, ‘Node’ and selecting ‘broad’ or ‘custom’ context. You can also click on the link at the top and go to the original document. A more advanced tip as your project moves along is to use compound coding. This lets you search for a particular phrase which occurs in a node, or where two nodes are applied together, and is useful for comparison.

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Step 5: check your codes. Go into the ‘Node’ view by clicking on the bottom left hand label. Choose a node to look at from the list that appears. Double click on the node you want to check...

... and you will see a code report, which looks like this. You’ve got all the extracts you labelled to this code, under the heading of the document.

Now you can read it through and make sure that you’ve used the label consistently (i.e. that all these extracts are about the same thing). If something is out of place, decide what it should be instead and code it to that node FIRST, then un-code it at the current node (if you do it the other way round, it disappears from this report and you need to track it down in the original source). You can also export this report into word or PDF to print it off. Make sure you do it in ‘Reference’ view so that you get the complete extracts. Just right click on the report and select export. When you get a flash of insight, and you start to see how things fit together, write a memo. This is just a note to yourself, which you can code to the relevant nodes so that it can be included when you look at that particular labe. Go to the ‘create’ tab and click ‘memo’, then code it to the relevant nodes.

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Step 6: organise and trim your nodes. Most people will get stuck into their coding and, at some point, realise that they’ve created a couple of nodes with basically identical meanings. in addition, they’ve got about 40 or 50 labels, which they are finding hard to remember. At this point, you want to start sorting them into what NVivo calls a hierarchy. This is sometimes called ‘second-order coding’ (Saldana, 2009). Read through your codes. Do any of them seem to fit together under a broader heading? Create a new code, and drag and drop the codes to fit under it. This creates a ‘parent node’ with ‘child nodes’ under it: i.e. main codes and sub-codes. You can then produce reports in the parent node which include all the child nodes by right clicking and choosing the last option ‘aggregate coding from child nodes’. Do any of your codes seem basically the same? You can merge them by right clicking on one and selecting ‘cut’, then selecting the other, right clicking and selecting ‘merge into selected node’. This deletes one and move all content into the other.

Top tip: one of the main criteria of a good methodology is a transparent approach. So when you are doing this stage, it’s important not to lose your original work. Consider creating a backup project at regular intervals, taking regular screenshots of your node hierarchy, and also keeping a research diary where you log your coding decisions and your logic. These can be included in your dissertation as appendices.

Nvivo has a useful tool to help with this, which is called ‘coding stripes’. Go to the ‘View’ tab and select ‘coding stripes’. You can choose whether to see all of your nodes, or just a few of the most relevant. This gives you a visual representation of which codes are used where in the text, and can be seen in both the sources and the code reports.

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This has only covered the very basics, but it should be enough to get you started. Remember that NVivo is not by any stretch of the imagination a short-cut, but it is a good way to deal with a large volume of complex information systematically . Further reading: Bazeley, P. (2013) Qualitative data analysis with NVivo. 2nd ed. London: SAGE Publications. QSR (2015) NVivo10 help. QSR [online]. Available from: http://help-nv10.qsrinternational.com/desktop/welcome/welcome.htm [Accessed 15th June 2015].