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Analyzing Qualitative Data
July 6, 2015
Engaging participants in the research process can be a valuable and insightful endeavour, leading to researchers addressing the right issues, and asking the right questions. Many funding boards in the UK (especially in health) make engaging with members of the public, or targets of the research a requirement in publicly funded research.
While there are similar obligations to provide dissemination and research outputs that are targeted at ‘lay’ members of the public, the engagement process usually ends in the planning stage. It is rare for researchers to have participants, or even major organisational stakeholders, become part of the analysis process, and use their interpretations to translate the data into meaningful findings.
With surprisingly little training, I believe that anyone can do qualitative analysis, and get engaged in actions like coding and topic discovery in qualitative data sets.
I’ve written about this before but earlier this year we actually had a chance to try this out with Quirkos. It was one of the main reasons we wanted to design new qualitative analysis software; existing solutions were too difficult to learn for non-expert researchers (and quite a lot of experienced experts too).
So when we did our research project on the Scottish Referendum, we invited all of the participants to come along to a series of workshops and try analysing the data themselves. Out of 12, only 3 actually came along, but none of these people had any experience of doing qualitative research before.
And they were great at it!
In a two hour session, respondents were given a quick overview of how to do coding in Quirkos (in just 15 minutes), and a basic framework of codes they could use to analyse the text. They were free to use these topics, or create their own as they wished – all 3 participants chose to add codes to the existing framework.
They were each given transcripts from someone else’s anonymised interview: as these were group sessions, we didn’t want people to be identified while coding their own transcript. Each were 30 minute interviews, around 5000 words in length. In the two hour session, all participants had coded one interview completely, and done most (or all) of the second. One participant was so engrossed in the process, he had to be sent home before he missed his dinner, but took a copy of Quirkos and the data home to keep working on his own computer.
The graph below shows how quickly the participants learnt how to code. The y axis shows the number of seconds between each ‘coding event’: every time someone coded a new piece of text (and numbered sequentially along the x axis). The time taken to code starts off high, with questions and missteps meaning each event takes a minute or more. However, the time between events quickly decreases, and in fact the average time for the respondents was to add a code every 20 seconds. This is after any gaps longer than 3 minutes have been removed – these are assumed to be breaks for tea or debate! Each user made at least 140 tags, assigning text to one or more categories.
So participants can be used as cheap labour to speed up or triangulate the coding process? Well, it can be more than this. The topics they chose to add to the framework (‘love of Scotland’, ‘anti-English feelings’, ‘Scottish Difference’) highlighted their own interpretations of the data, showing their own opinions and variations. It also prompted discussion with other coders, about what they thought about the views of people in the dataset, how they had interpreted the data:
“Suspicion, oh yeah, that’s negative trust. Love of Scotland, oh! I put anti-English feelings which is the opposite! Ours are like inverse pictures of each other’s!”
Yes: obviously we recorded and transcribed the discussions and reflections, and analysed them in Quirkos! And these revealed that people expressed familiar issues with reflexivity, reliability and process that could have come from experienced qualitative researchers:
“My view on what the categories mean or what the person is saying might change before the end, so I could have actually read the whole thing through before doing the comments”
“I started adding in categories, and then thinking, ooh, if I’d added that in earlier I could actually have tied it up to such-and-such comment”
“I thought that bit revealed a lot about her political beliefs, and I could feel my emotions entering into my judgement”
“I also didn’t want to leave any comment unclassified, but we could do, couldn’t we? That to me is about the mechanics of using the computer, ticky box thing.”
This is probably the most useful part of the project to a researcher: the input of participants can be used as stimulus for additional discussion and data collection, or to challenge the way researchers do their own coding. I found myself being challenged about how I had assigned codes to controversial topics, and researchers could use a more formal triangulation process to compare coding between researchers and participants, thus verifying themes, or identifying and challenging significant differences.
Obviously, this is a tiny experimental project, and the experience of 3 well educated, middle-class Scots should not be interpreted as meaning that anyone can (or would want to) do this kind of analysis. But I believe we should do try this kind of approach whenever it is appropriate. For most social research, the experts are the people who are always in the field – the participants who are living these lives every day.
You can download the full report, as well as the transcripts and coded data as a Quirkos file from https://www.quirkos.com/workshops/referendum/