Speaker 1: What is coding of qualitative data? Well, coding is a method used to analyze data by identifying themes or codes that appear in our qualitative data and then assigning sections of data to those codes. Miles and Huberman state that codes are tags or labels for assigning units of meaning. Codes are usually attached to chunks of varying size. Those might be words, phrases, sentences or even whole paragraphs. The process is to break up very long and detailed qualitative data sources like interviews, focus groups and documents into common themes. We can then read across different sources to compare what people are saying on one particular issue or topic. For example, we might have a code called healthy eating and we can compare what different groups of people have said about healthy eating. So, for example, we might see whether older people were more into healthy eating than younger people or what different ideas people had about what healthy eating is. Coding is like putting things into categories. Once we've created our categories and sorted things into them, it's easier to find things later and see how items in one category vary. It's partly a method for analysis, but also a way of managing your data. Once you've been through and coded your data, it can be a lot easier to do the writing up later because it makes it easier to quickly find a quote to support something that you want to say. So let's do a little bit of coding here and see how that works. So we've got an example data source of someone being interviewed about what they eat for breakfast. And they say, I'm a single mom with an eight-year-old toddler and breakfast is mayhem. The baby has porridge, I microwave up some ready breakfast whole milk for him. So I've created a couple of codes here of things I think might come up. You see, we've already got one here for porridge. So I'm going to select this text about being about porridge and drag and drop it onto the porridge theme. So we've coded that section of text now to that theme. We could have chosen the whole paragraph or a larger piece of text or just this section here about porridge, but we chose the whole sentence. And we can also say that this whole sentence is also about milk. So we can drag that onto the milk bubble here as well. And we've now coded this text to be about milk and about porridge. And that's the basics of coding. So going through, creating codes, coding sections of text to those codes. Now, the kind of coding we're doing here is very basic and descriptive. For example, here, I'm a single mom with an eight-year-old and toddler and breakfast is mayhem. Well, let's put this onto the children's bubble here, because actually, I think that's about children. But you see, we've got some other themes here. So we could say that this is also about family structure. It's about wider issues, about social breakdown. It's also about time. So these are things that might come up. And we also want to do some kind of in vivo coding, questionably about mayhem. And we may want to even create a theme here, which is called mayhem. So there are various different ways that we can code up the data. And you see, even this very simple sentence, these couple of sentences here, we've managed to code in a whole bunch of different ways. So it can be a very long process of going through and doing coding. But this is the basics of how it looks. Broad and Clark describe codes as being a pithy label identifying what's of interest in the data. While themes are an idea or concept making a common recurring pattern across a data set clustered around a central organizing concept. So in our example here, low fat yogurt might be a code and healthy eating would be something that would be more like a theme. So there are many different types of coding. The two big kind of types are grounded theory or emergent coding. And that's pretty much where you start with just a blank sheet. So you have no preconceptions of what you're going to find in the data. You're completely open to having the data speak to you. And just as you go through on the fly, reading through the data, defining the codes and themes that the data is suggesting to you. The other method is called framework analysis or structured coding, where you actually have a framework. So you've got your list of codes and themes beforehand. And what you're trying to do is match the data to the codes that you've already identified. So the topics of interest you already want to look for in the data. Now, in practice, what most people do is something which is a little bit of a flexible combination between both. They have some kind of idea of the things they want to find in the data, but they're also open to new things. So if there's something surprising and unexpected coming out of the data, they can create new codes and themes to capture that. There are also lots of different types of coding which we can use. The most common is called descriptive coding. And it's about literally coding what's being discussed in a very literal basis. So if somebody says, I like yogurt, you code yogurt and like. There's also thematic coding, where you create themes and codes. We talked about a little bit already. You can do line-by-line coding. And that's where you assign each line its own code, kind of describing what's going on in that one line or sentence. There's also IPA, interpretive phenomenological analysis. And that's about looking to see how participants experience and make meaning of things that are happening in their world. And that's often combined with a line-by-line coding approach. There's also in vivo coding. And that's where you use actually the words and terms that participants in the data are using to develop code. So if somebody says, I really love breakfast. It's my favorite meal of the day. Love would be the word that you pull out there. So you are using their own words to categorize what they're saying. There's also discourse analysis, where we look at how people express themselves. What types of words do people use? How do different people express themselves? And there's many more different ways that we can analyze and explore data. In fact, there's a whole textbook by Johnny Saldana, which is really excellent to read on coding qualitative data. I think in it, there's 34 different ways that you can code data. And it can really help if you're kind of stuck. You've tried a coding approach, and it's not quite working for you. Go back and try a completely different approach. Do it from a kind of action viewpoint or a process viewpoint. It's important to remember that coding is nearly always an iterative, cyclical process. You go back and do it again and again, over and over. So you're reading it through multiple times. You're doing different levels, different types of coding, and also going through and coding your coding. So trying to put your codes and themes into different groups, different structures, different ways to pull together the different themes which are coming out from the data. Coding is an important part of analysis, but it also requires further interpretation when it's complete. You still need to say, well, what does my coded data say? You need to go through and read it. And all these different methods are really just different ways of getting to know your data better. They're not going to do the analysis and interpretation for you, but they're an important step in the process. It's also important to bear in mind that you don't have to do any coding. A lot of people don't like doing coding. A lot of practitioners say that it abstracts people from the data. It's a very reductive process. You're taking very long, very detailed, complex, nuanced data and extracting it at a very kind of low level, a kind of very base level of analysis. So some people just advocate just reading the data over and over until it makes sense in your head. Now, there are lots of software tools like Quercus that will help you code qualitative data, but you don't have to use any special software. A lot of people use spreadsheets. We use something like Excel to go through and do line-by-line coding and put different themes for those. Or in word processors, in Word, just use the comments feature. A lot of people also just use pen and paper. So they'll print out the transcripts, use different colored highlighters to identify different themes. And some people even cut out the sentences or paragraphs, put them into different envelopes or folders and group them together by the different themes. These are all ways that you can use to get to know your data better and start to sort and organize it. But the dedicated software does make it a lot easier when you come through to write up to find those quotes, especially compared to doing it on paper, and know where they came from. So if you want to see everything you coded on healthy eating on all the qualitative software packages, that's just a click away. Quercus is just one of the ways that you can use to analyze your data. It's very visual, very intuitive and easy to use. And you can go to our website and download a complete free trial and see how it works for you.