What next? Making the leap from coding to analysis

leap coding to analysis

 

So you spend weeks or months coding all your qualitative data. Maybe you even did it multiple times, using different frameworks and research paradigms. You've followed our introduction guides and everything is neatly (or fairly neatly) organised and inter-related, and you can generate huge reports of all your coding work. Good job! But what happens now?

 

It's a question asked by lot of qualitative researchers: after all this bruising manual and intellectual labour, you hit a brick wall. After doing the coding, what is the next step? How to move the analysis forward?

 

The important thing to remember is that coding is not really analysis. Coding is often a precursor to analysis, in the same way that a good filing system is a good start for doing your accounts: if everything is in the right place, the final product will come together much easier. But coding is usually a reductive and low-level action, and it doesn't always bring you to the big picture. That's what the analysis has to do: match up your data to the research questions and allow you to bring everything together. In the words of Zhang and Wildemuth you need to look for “meanings, themes and patterns”

 


Match up your coding to your research questions

Now is a good time to revisit the research question(s) you originally had when you started your analysis. It's easy during the coding process to get excited by unexpected but fascinating insights coming from the data. However, you usually need to reel yourself in at this stage, and explore how the coded data is illuminating the quandaries you set out to explore at the start of the project.

 

Look at the coded framework, and see which nodes or topics are going to help you answer each research question. Then you can either group these together, or start reading through the coded text by theme, probably more than once with an eye for one research question each time. Don't forget, you can still tag and code at this stage, so you can have a category for 'Answers research question 1' and tag useful quotes there.

 

One way to do this in Quirkos is the 'Levels' function, which allows you to assign codes/themes to more than one grouping. You might have some coded categories which would be helpful in answering more than one research question: you can have a level for each research question, and  Quirks/categories can belong to multiple appropriate levels. That way, you can quickly bring up all responses relevant to each research question, without your grouping being non-exclusive. 

 


Analyse your coding structure!

It seems strange to effectively be analysing your analysis, but looking at the coding framework itself gets you to a higher meta-level of analysis. You can grouping themes together to identify larger themes and coding. It might also be useful to match your themes with theory, or recode them again into higher level insights. How you have coded (especially when using grounded theory or emergent coding) can reveal a lot about the data, and your clusterings and groupings, even if chosen for practical purposes, might illuminate important patterns in the data.

 

In Quirkos, you can also use the overlap view to show relationships between themes. This illustrates in a graphical chart how many times sections of text 'overlap' - in that a piece of text has been coded with both themes. So if you have simple codes like 'happy' or 'disappointed' you can what themes have been most coded with disappointment. This can sometimes quickly show surprises in the correlations, and lets you quickly explore possible significant relationships between all of your codes. However, remember that all these metrics are quantitative, so are dependent on the number of times a particular theme has been coded. You need to keep reading the qualitative text to get the right context and weight, which is why Quirkos shows you all the correlating text on the right of the screen in this view.

 

side comparison view in Quirkos software

 


Compare and contrast

Another good way to make your explorations more analytical is to try and identify and explain differences: in how people describe key words or experiences, what language they use, or how their opinions are converging or diverging from other respondents. Look back at each of the themes, and see how different people are responding, and most importantly, if you can explain the difference through demographics or difference life experiences.

 

In Quirkos this process can be assisted with the query view, which allows you to see responses from particular groups of sources. So you might want to look at differences between the responses of men and women, as shown below. Quirkos provides a side-by-side view to let you read through the quotes, comparing the different responses. This is possible in other software too, but requires a little more time to get different windows set up for comparison.

 

overlap cluster view in Quirkos software

 

Match and re-explore the literature

It's also a good time to revisit the literature. Look back at the key articles you are drawing from, and see how well your data is supporting or contradicting their theory or assumptions. It's a really good idea to do this (not just at the end) because situating your finding in the literature is the hallmark of a well written article or thesis, and will make clear the contribution your study has made to the field. But always be looking for an extra level of analysis, try and grow a hypothesis of why your research differs or comes to the same conclusions – is there something in the focus or methodology that would explain the patterns?

 


Keep asking 'Why'

Just like an inquisitive 6 year old, keep asking 'Why?'! You should have multiple levels of Why, with explanations in qualitative focus usually explaining individual, then group, and all the way up to societal levels of causation. Think of the maxim 'Who said What, and Why?'. The coding shows the 'What', exploring the detail and experiences of the respondents is the 'Who', the Why needs to explore not just their own reasoning, but how this connects to other actors in the system. Sometimes this causation is obvious to the respondent, especially if articulated because they were always asked 'why' in the interview! However analysis sometimes requires a deeper detective type reading, getting to the motivations as well as actions of the participants.

 


Don't panic!

Your work was not in vain. Even if you end up for some reason scrapping your coding framework and starting again, you will have become so much more engaged with your data by reading it through so closely, and this will be a great help knowing how to take the data forward. Some people even discover that coding data was not the right approach for their project, and use it very little in the final analysis process. Instead they may just be able to pull together important findings in their head, the time taken to code the data having made key findings pop out from the page.

 

And if things still seem stuck, take a break, step back and print out your data and try and read it from a fresh angle. Wherever possible, discuss with others, as a different perspective can come not just from other people's ideas, but just the process of having to verbally articulate what you are seeing in the data.

 


Also remember to check out Quirkos, a software tool that helps constantly visualise your qualitative analysis, and thus keep your eye on what is emerging from the data. It's simple to learn, affordably priced, and there is a free trial to download for Windows, Mac and Linux so you can see for yourself if it is the right fit for your qualitative analysis journey. Good luck!

 

 

Comparing qualitative software with spreadsheet and word processor software

word and excel for qualitative analysis

An article was recently posted on the excellent Digital Tools for Qualitative Research blog on how you can use standard spreadsheet software like Excel to do qualitative analysis. There are many other articles describing this kind of approach, for example Susan Eliot or Meyer and Avery (2008). However, it’s also possible to use word processing software as well, see for example this presentation from Jean Scandlyn on the pros and cons of common software for analysing qualitative data.

 

For a lot of researchers, using Word or Excel seems like a good step up from doing qualitative analysis with paper and highlighters. It’s much easier to keep your data together, and you can easily correct, undo and do text searches. You also get the advantage of being able to quickly copy and paste sections from your analysis into research articles or a thesis. It’s also tempting because nearly everyone has access to either Microsoft Office products or free equivalents like OpenOffice (http://www.libreoffice.org) or Google Docs and knows how to use them. In contrast, qualitative analysis software can be difficult to get hold of: not all institutions have licences for them, and they can have a steep learning curve or high upfront cost.

 

However, it is very rare that I recommend people use spreadsheets or word processing software for a qualitative research project. Obviously I have a vested interest here, but I would say the same thing even if I didn’t design qualitative analysis software for a living. I just know too many people who have started out without dedicated software and hit a brick wall.

 

 

Spreadsheet cells are not very good ways to store text.


If you are going to use Excel or an equivalent, you will need to store your qualitative text data in it somehow. The most common method I have seen is to keep quotes or paragraphs as a separate cell in a column for the text. I’ve done this in a large project, and it fiddly to copy and paste the text in the right way. You will also find yourself struggling with formatting (hint – get familiar with the different wrap text and auto column width options). It also becomes a chore to separate out paragraphs into smaller sections to code them differently, or merge them together. Also, if you have data in other formats (like audio or video) it’s not really possible to do anything meaningful with them in Excel.

 


You must master Excel to master your analysis

 

As Excel or other spreadsheets are not really designed for qualitative analysis, you need to use a bit of imagination to sort and categorise themes and sources. With separate columns for source names and your themes, this is possible (although can get a little laborious). However, to be able to find particular quotes, themes and results from sources, you will need to properly understand how to use Pivot Tables and filters. This will allow you some ability to manage and sort your coded data.

 

It’s also a good idea to get to grips with some of the keyboard shortcuts for your spreadsheet software, as these will help take away some of the repetitive data entry you will need to do when coding extracts. There is no quick drag-and-drop way to assign text to a code, so coding will almost always be slower than using dedicated software.

 

For these reasons, although it seems like just using software like Excel you already know will be easier, it can quickly become a false economy in terms of the time required to code and learn advanced sorting techniques.

 


Word makes coding many different themes difficult.

 

I see a lot of people (mostly students) who start out doing line-by-line coding in Word, using highlight colours to show different topics. It’s very easy to fall into this: while reading through a transcript, you highlight with colours bits that are obviously about one topic or another, and before you know it there is a lot of text sorted and coded into themes and you don’t want to loose your structure. Unfortunately, you have already lost it! There is no way in Word or other word processing software to look at all the text highlighted in one colour, so to review everything on one topic you have to look through the text yourself.

 

There is also a hard limit of 15 (garish) colours, which limits the number of themes you can code, and it’s not possible to code a section with more than one colour. Comments and shading (in some word-processors) can get around this, but it is still limited: there is no way to create groups or hierarchies of similar themes.

 

I get a lot of requests from people wanting to bring coded work from a word processor into Quirkos (or other qualitative software) but it is just not possible.

 


No reports, or other outputs


Once you have your coded data – how do you share it, summarise it or print it out to read through away from the glow of the computer? In Word or Excel this is difficult. Spreadsheets can produce summaries of quantitative data, but have very few tools that deal with text. Even getting something as simple as a word count is a pain without a lot of playing around with macros. So getting a summary of your coding framework, or seeing differences between different sources is hard.

 

Also, I have done large coding projects in Excel, and printing off huge sheets and long rows and columns is always a struggle. For meetings and team work, you will almost always need to get something out of a spreadsheet to share, and I have not found a way to do this neatly. Suggestions welcome!

 

 


I’m not trying to say that using Word or Excel is always a bad option, indeed Quirkos lets you export coded data to Word or spreadsheet format to read, print and share with people who don’t have qualitative software, and to do more quantitative analysis. However, be aware that if you start your analysis in Word or Excel it is very hard to bring your codes into anything else to work on further.

 

Quirkos tries to make dedicated qualitative software as easy to learn and use as familiar spreadsheet and word processing tools, but with all the dedicated features that make qualitative analysis simple and more enlightening. It’s also one of the most affordable packages on the market, and there is a free trial so you can see for yourself how much you gain by stepping up to real qualitative analysis software!