Recording of Critical Appraisal Webinar Series Part 3: Qualitative Analysis



This on demand teaching session, relevant to medical professionals, will cover qualitative analysis with expert speaker Dr Heather Morgan - a multidisciplinary primary social scientist with research interest in digital health and methodological expertise. Doctor Morgan will provide an introduction to qualitative research, showing how it works and how it is used in a medical context. She will explain how the method involves gathering qualitative data through a range of sources, such as interviews, observations, documents, social media, etc., and will demonstrate practical steps on how to analyse it. The Q&A session at the end will allow Dr Morgan to respond to participants' questions, after which a certificate of attendance will be generated.
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AMSA Scotland is organising a webinar series on critical appraisal to provide students with the basic skills required for critical appraisal of academic papers.

The target audience is medical students, but the events are free and available to everyone.

The series consists of multiple talks, and the 3rd talk (Qualitative Analysis) of the series will be held:

Date: 9th November 2022

Time: 14:00 - 15:00 (UTC+0)

Speaker: Dr Heather May Morgan

Learning outcomes:

  • Recognise qualitative (health) research principles and practises
  • Distinguish between qualitative and quantitative approaches to (health) research study ethos and design
  • Outline how qualitative health research can be undertaken
  • Recognise general approaches to qualitative data analysis and steps involved
  • Have been introduced to the NVivo software platform for qualitative data analysis management
  • Appreciate how qualitative research is written up and can be presented as findings

Tentative schedule for AMSA Scotland Critical Appraisal Webinar series:

1. 12/10 (Wed) 1830-1930 (UK Time): Study designs (Speaker: Agi Jothi)

2. 22/10 (Sat) 1130-1230 (UK Time): Critical appraisal of a quantitative paper (Speaker: Agi Jothi)

3. 9/11 (Wed) 1400-1500 (UK Time): Qualitative Analysis - (Speaker: Dr Heather May Morgan)

4. 16/11 (Wed) 1700-1800 (UK Time): Academic writing (Speaker: Professor Phyo Myint)

5. 24/11 (Thur) 1730-1830 (UK Time): Systematic Reviews (Speaker: Dr Amudha Poobalan)

6. 29/11 (Fri) 1615-1700 (UK Time): Formulation of Research Questions - (Speaker: Professor Stephen Turner)

Learning objectives

Learning Objectives: 1. Understand the basics of Qualitative Research 2. Differentiate between Qualitative Research and Quantitative Research 3. Identify the primary methods of data collection in Qualitative Research 4. Identify the steps of Qualitative Research analysis 5. Discuss the benefits and challenges of Qualitative Research.
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Computer generated transcript

The following transcript was generated automatically from the content and has not been checked or corrected manually.

Mhm. Hello, everyone. My name is Raymond More. I'm the national research director for Scotland this year. Welcome to the third part of our critical appraisal weaponize series. Today's topic will be qualitative analysis, and we are very happy to have Dr Heather Morgan as our speaker today. Doctor Morgan is a multidisciplinary primary social scientist with research interest in digital health and methodological expertise in designing, leading and delivering qualitative and mixed methods Studies in Applied Health Sciences. She's also the lead author or co author of over 20 peer review papers and two edited collections. Please feel free to having any questions you have anytime during the presentation, Doctor Morgan will be addressing your questions in the Q and a session at the end after the Q and A. It'll be great if you could fill out the feedback form, which I will send out the lane later in the chat, a certificate of attendance will be automatically generated for you after completing the form. So without further a do, let's have Doctor Morgan to share her presentation on qualitative analysis. Thank you very much. Thank you very much. Um, and thanks for having me and for inviting me. I've not used this platform before, so hopefully, um, you can see my slides. Is that is that right? Yeah. Yeah. Perfect. OK, that's great. And I'm gonna It's come up on my second screens. I'm going to see if I can also see the chat. So, as Raymond said, if you want to post any questions or any comments along the way, anything that's unclear, please, please do. And I'll follow the chat as well on this screen as we go along. Um, So, um, as we said, we're going to be looking at qualitative analysis today. This just to get a sense from from participants. If you can put yes or no. If you have any experience of qualitative research or any prior knowledge of it at all, um, into the chat, that would be helpful. Um, just that it might be new to too many of you. Um, not really. Okay, Yeah, that's and that's what you tend to find in sort of. I work in, um, Applied Health Sciences and within the medical school, um, and you tend to find among among clinical colleagues and, um, other health scientists that most people are familiar in in research terms with doing quantitative research, um, using statistics and, um, outcome measures and surveys and things like this, whereas qualitative research is really quite different. Um, so qualitative research, um, is really focused on, uh, yeah, communication, human community communication. Um, the mains of methods, primary methods of data collection and qualitative research tend to be interviews with people or focus groups with with groups of people. So you might have 1 to 1 interviews, or you might bring groups of people together based on a shared experience or a common factor. So perhaps you want to look at, um, people with diabetes who are aged over 60. So you might bring together groups of people who have some something in common that you're interested in exploring with them. Um, and you know, in terms of health and health topics, some things can be quite sensitive, So you might want to do individual interviews. Some things can be, You know, if you're trying to look at service redesign or what's working well, you might want to get groups of people together to understand from multiple perspectives. Um, what's going on? And these tend to be quite small. Sample sizes. So if we think about clinical trials, we can think of hundreds of people that we need to include in studies to test drugs. For example, um, in qualitative research where we work with a lot smaller numbers. Um, and there's no kind of set sample size calculator like there might be in in the statistics, um, for power, but we normally kind of decide Well, how many people do we need? Need to talk to other kinds of data collection methods might include case studies. So you might have a very small number of people that you follow up and do interviews with over a period of time over a period of years. You might speak to them, um, to follow their journey through a particular health condition. Um, or you might also do use of observations as well. So, um, some of my work and I refer to later has involved actually going and sitting in settings and watching what's going on in context. So one example of that is, um, uh, electronic records, an ambulance project. And to understand it was mixed methods. But to understand how the use of electronic records worked in ambulances, I did 12 hour shifts in ambulances across Scotland to try to understand and see how they were used in practice. So it's quite physical. Yeah, an endeavor as well. And what we tend to do is is go out of the university, um, and actually meet people in their own setting. So it's not like sending out a survey and getting it back or asking people to come to appointments here so we can collect data from them. It's actually involves a lot of, um, offsite working, um, other communities and groups and so on and and so doing this kind of work. Um, having conversations, we tend to audio record, Um, sometimes video record, Um, and this from these we can generate transcripts so you can see examples here for, um, you know, the conversation is typed out verbatim. So exactly what was said? Um, if videos sometimes they can be captioned for body language as well. And observational notes can be notes that jotted down by hand when you're maybe on site so that the ambulance work I mentioned I would have a piece of paper and a pencil and write down as much as I could about what I was seeing, um, to be able to write that up later, but you can also use things like emails, documents. We use Web pages. We use discussion forums quite often as a source of qualitative data. So if we're interested in a particular health topic, we might go to like Mum's. Net is a really good resource where you can go and look at what topics are interesting to people. What people are saying about particular topic, so we can do secondary analysis of that. And we can also use things like photos, diaries, videos, films. And it depends what discipline really you're working in because qualitative research sort of comes from the social sciences, um, and has been used, really, and anthropology, sociology, political science. Um, we use it in Applied Health Sciences and have done for a number of years now, but it's really, I suppose, gained traction over the last 10 years where I've worked in health sciences. Um, and now it's certainly something that's really firmly embedded in most projects that there's at least some qualitative research that happens, Um, involves lots of words, um, and lots of interpretation. Um, and it can respond to new technologies as well. So things like social media we use quite often as well. Um, and of course, that will change in the future. We'll have new, uh, forms of data. So it's it's responsive to to really, um, just about anything. And it's really, I suppose, a bit like detective work and that you whatever source that you need to find whatever evidence you need to find you kind of go to find based on your research question. Um, so because it's it is very wording very much about words. Um, it's It can be quite unstructured. There can be a lot of data. Um, it's, uh, you know, you're interested in the details, so you have to go through things of, um, very fine detail, line by line. Almost. Um, it's it's very much focused on the little details. Um, but the idea is that you're kind of digging and delving into the different, uh, supposed sources of data, um, to try and get the bigger picture. Um, so you're But you're also trying to retain the individual stories, the individual experiences of people, um, the concerns that they might have the the So what Each person is saying, um uh, any given time point, but also to paint this kind of bigger picture of what that might mean. Um, so I mentioned the ambulance, Um, electronic records and ambulances. So we were looking, for example, for that. Looking at that, um, what managers thought what paramedics thought, what patients' thought. So all of these people have different perspectives, and paramedics were not all, like, really positive about it. They had lots of different comments and different experiences and examples of where things work, well, where things don't work. Well. And the task of that project was to kind of bring together that overall picture of Well, you know, what do we think about electronic records and ambulances? How do they actually work? Sorry, I had a cough there. Um, so in terms of analysis and this, this session is really focusing on analysis rather than data collection itself. Um, it's really time consuming doing the analysis. Um, not least because the data are not necessarily structured in the same way. So just thinking back if you've got pictures, if you've got text, you've got links and things like that. Um, it can be you're working with different, different things. Sorry, I'm gonna have to have a coughing fit to apologize. No, I can't find the mic. Fine. Switch off the that's rather inconveniently timed. Um, so if you're working with different types of data, if you're working with, like a spreadsheet, for example, where you've already set up a survey and respondents click and they enter all the data and then it's tabulated, it's all quite organized. You might have to go in and clean it and tidy and check. It's It's all right, but in general it's It's in a set format, but with qualitative. You've got all these different types of data that you're trying to bring together. Um, and so there is an element of being sort of creatives. Um, but you're also trying to be scientific and systematic. So there's this kind of tension or trade off of working with unstructured data, trying to bring structure to it, but also not trying to destroy it from its original source. To try and keep kind of retain the context of the data. And that might sound a bit vague and a bit wooly. Um, very often you know, it'll involve reading lots of text, um, looking through things, writing notes and sort of layering notes and building up. And I will take you through the stages of of analysis, um, shortly. But yeah, it's, um, it's sometimes it's really easy to do digitally we've got I'll show you the software and viva that we use and you can digitize everything. Sometimes it really does. Involve sitting on the floor with cut cut up bits of paper and moving things around and crawling crawling on the floor, most with Post it notes. And I use tracing paper quite a lot. You'll see, um, and it's quite a sort of its most physical, not only in going out to collect the data, but when you're actually doing the analysis. It can be quite, um, sort of trying to map things to make sense of them. So you you kind of, uh, I suppose that, Yeah, it is very, um, hands on. Um, so, as I said, that sounds quite wooly. And there is often a lot of mystery around how analysis is done. And if you ever read any qualitative papers, excuse me, I'll just take a drink. Um, you see a lot about the data collection processes. So people will say how many interviews they did, what the questions were, what the sample was, how long it took all of that kind of detail. But they say very, very little about the actual analytical process. Um, how they actually have approached the analysis. You normally get a sentence or two, maybe, Um, and quite often they'll refer to thematic analysis, Um, particularly in health sciences. And I don't know whether it's a a thing about sort of journal restrictions on word counts, Um or sort of less is more and being kind of quite direct around the methods, um, and kind of being quite concise and then having more space for the results or findings. But, yeah, you find very little of the actual unpacking of the analysis itself less so in student work, I have to say, like if we have student projects, um, quite often students are very good at writing it, you know, in a dissertation about the detail of what they did. Um, I think a lot of that is sort of makes the cutting room floor, as it were in the actual publication process, so it can be quite difficult for for people who are not familiar with qualitative research to actually get a lot from papers in terms of, you know how how people approach analysis. Um, and there isn't a standard approach, so there's almost like a toolkit, I suppose. Um, and of course there is in statistics as well. But in statistics you would name which tests you're going to do. Or you would consult with the statistician to know which was appropriate test for the type and quantity of data that you have in qualitative. We don't have that kind of reference point. Um, it's kind of almost like, Well, you work with the data once you've got the data, which is challenging to maybe outline in terms of ethics applications. Exactly what you're going to do when you you don't actually know. Um, but as I said, it's kind of about detective work, and your your main sort of goal is to define, categorize, theorize, explain these kinds of words. Um, what, you're actually investigating? Um, it really does depend on the question what you're actually trying to do, But there's a few examples here, so maybe you're trying to understand how people conceptualize something from a different perspective or how people see things. Or maybe you're looking at ranges of experience, for example, So, you know, are there patterns to the way people, um, see or do things? Are there types of attitudes that can be grouped together? Are types of behaviors so quite often interested in health behavior? So, for example, a lot of my work has been in, um, trying to understand smoking cessation and why, where we come to the kind of sticking point with some people who still smoke. But you know, how do we design services that might appeal to them? Um, or might help or support behavior change? How do we approach? So are the types of people who we need to create different types of approaches. For, um, that's not stereotyping. It's more like typology. And it might not be based on characteristics that are obvious either. It might not, you know, it might not be. The age is actually the defining factor. It might be something else, um, or it might be a shared attitude, for example, so we might try to explain. People who have this attitude find it difficult to engage with this service because and and then we can say Okay, so what can we do instead? So, um, it is like, yeah, finding associations between things or trying to explain things. Um, sometimes it can be about new ideas as well. So, for example, some work that I've been doing recently has been I'm working with some engineers and we're, uh, robotic engineers who would like to develop a wearable band belt. Um, that could be wandering pregnancy That would have, um, sensors in it. That could provide, um, haptik light and audio feedback on the baby's movement during pregnancy. Um, so at the moment, we've got a prototype mock up of of an image. We don't even have a product at this point. And we're doing focus groups with people with pregnant women, people who are women who have miscarried with partners to understand whether they would be interested in this kind of a device and what their concerns have been, what other technologies have used and so on. So that's about really developing a new idea. And that's really huh. That qualitative research is helping to shape some of the things that even from the literature and from our team being interdisciplinary and the kinds of things that we've been looking at we've missed because when we start to talk to people, they give us feedback and, um, new ideas and so on, and that's that's really helpful and that it will change the course of the next step so you can do it before. So rather than design a product and then try to use it, um and then because I really don't like that because you can you can do the work beforehand to get the design right. Um or at least you know, um, to inform it. So I said there are different approaches to analysis. And, um, there are three kind of images here that I think are quite useful in the context of health research and the one to the left. There is this sort of thematic framework analysis, um, that is generally used in applied health sciences. So basically, you get your data, and it could be transcripts. It says there. Um, but essentially what you're doing is going through each of your data sources and just to familiarize yourself with it. And that does take time. Um, and you you take a lot of notes And you think, and you might talk to members of your team and so on, Um and then what you want to build from there is a thematic framework. So you start to look at how things, um uh, in the day. So if there are concepts or if they're common things that you in that they're sometimes called themes concepts, um, and there are There are other like codes, nodes. There's lots of different things that people call, uh, this part of it, which is really confusing if you read textbooks. So I said in papers, it's difficult to see what happens in in textbooks. The terminology is used is different, but sometimes it's referring to the same thing, so it can be quite confusing essentially what you're trying to do. If if you could imagine reading a transcript so you've got 30 page transcript and you might have, um created some post it notes with sort of high level things, concepts, ideas, labels that you might think being talked about within one interview, you might then go through the second transcript and do the same. The third transcription do the same and then from there, you've got a pile of post it notes, which you would ideally have, like different colors or something per per transcripts. You can relate them back to the data, but then you would kind of move them and say, Oh, this this thing seems to be coming out a lot. That thing is only important to that person. Everyone's mentioned this, but they all disagree. And you can start to kind of map. And this is why it's quite physical. You can do this digitally and some of you during the pandemic. We'll probably use Miro. There's a really great online platform that can mimic these things, but it is kind of visualizing the relationships between things and then starting to develop. Um, sort of, yeah, those key concepts in their relationships to each other. And then once you've got that, you go back to the data and u code them individually. Um, based on the ideas that you've had or the concept you've identified, and you go and label them back thoroughly. So you go line by line or, um, if it's images or websites and things, you can tag and you can do this digitally, um, in in Vivo where you can put these codes on, then group together information that relates to the same thing. So it keeps its integrity within the original source. But you can then say, Okay, let's pull out everything that refers to, um, privacy in relation to technology. And then you can say, Okay, here's all the quotes on privacy, and then you've got all your participants and you might look at Okay, well, it seems that all of the people who live in Aberdeen said this, but all of the people who live in Aberdeen just say that I'm just making things up now. But you can start to look for those patterns, um, and then use that to sort of chart the data and arrange into the themes. So that's when you start to kind of group it together, um, and write it up and then you start to try to explain, um, so you you write out what you found, and then you try to look at the reasons for this. So that's the kind of process um, that is generally used, but you can see it on the right hand side. Here in the in the top quite often in applied health research. We use, um, is frame work again? We use this process to to do it, but we we actually have a framework, and you can see it looks almost like an Excel spreadsheet with cells. Um, and the idea is that you already know from the literature what what the themes are going to be, because you it's very focused. So if if it's a topic where a lot is already known. But there's what, like one new question, or there's like lots of literature on the topic say, smoking cessation loads of what has been done on that you might have things that you want to specifically ask the participants about. And so the interview is quite structured and you've got definitely got comments on each of these things from each participant. So you would put each of your participants in here and you would say what they said about each thing, and then you could look for you know, most people say this or you, and then you can even use color coding, then to see patterns and gaps and things, and it's quite a neat way, particularly you're working with clinicians to actually do qualitative research. That can be useful. A quick kind of a glance. Remember, some years ago? I did some work, um, with some clinicians in, um, the Children's hospital. And, you know, if I had done everything in in vivo and and had done it with post it notes and everything, it would have taken a long time, and it wouldn't have been very useful to them. They wanted a very quick snapshot. And so the work was very focused. The questions were quite narrow, and I was able to tabulate the data and share it by Excel spreadsheet. Essentially. So that worked for everybody. Um, if you're doing more in depth work about something that's less known about, you might do something like grounded theory. So this is really about gathering your data and you do that first so you can see here the review of the literature comes after, whereas for this one, you've already done the literature review and identified the main things that you want to look at this You might just gather data in the sense of well, you know, the rough topic area and and and then you Once you've gathered the data, you code it and start writing memos, which, and classify which kind of mimic these stages here. And then you develop your kind of theory, which is like your interpretation, and then you review the literature. But it's almost like the research hasn't been biased by what's in the literature. You're kind of trying to find new things. So, for example, the work with the pregnancy belt Um, that's really we've We've got some ideas about some things that were interested in, uh, like, for example, are people worried about senses and and contact with the skin and the and the bump. Um, we thought that was something charging how long you would wear the thing for. But actually, lots of other things came up that we hadn't really thought about around integration into healthcare, around cost around sustainability of the materials with which the band is made so that lots of things came out that we weren't, you know. Now we're going to have to go and do the literature of you to to look for those things. If there's anything else on that in digital tech development, So there's this kind of very sort of linear process. On the left hand side, there's this sort of tabulation process at the top there on the right. And then there's more cyclical process, um, at the bottom right there. So these are the kind of general approaches that you can take. Um so framework has been really developed for applied research. As I said, it's it's most used, and that's because I suppose it's most sort of transparent. It takes the mystery away and makes it the the systematic nature. There's like clinicians and so on. Um, and the objectives usually clear. You've got the literature reviews done, and it can be really focused so that the timescales are shorter and it can be done more efficiently, I suppose, and also within the context of like applying for ethics approval from the NHS. Um, you know, being really transparent about what your go going to do is really important, whereas maybe in social sciences, I'm not saying that ethical issues are less important, but I'm saying you can be a bit more creatively in the way that you approach the work. Um, and it's not written up necessarily in the scientific way that we tend to think of in medicine and healthcare. So there's a bit more licensed there to do things. So this tends to be the kind of default. And also, when explaining to funders what you're going to do, it can be nice to have this very, um, organized way of approaching things, uh, and again transparent for the funder to know. Okay, what is it that you're actually going to do? Um, so these are some of the kind of words or the the descriptions or, you know, the the features that, um, easy retrieval of data accessible to others. So it's It's quite, you know, because you can share it in that quite visual way and that tabulated way. It's a bit more familiar and does look much more organized than you know, pages and pages of sort of not created writing but, uh, stories about what people have said so that that's a good reason to use it. Um, but you've got the stage is still so you always go back to those stages. Um, as I've said that are quite linear. But it's not even though you've got those stages in that structure to it isn't mechanical. It is Still, there's a lot of creatives and, um, conceptual ability that's required of the research. Because the research is the tool. There's no software that you can press or test that you can run that will tell you what's in the data. There are increasingly in software, um, things like word frequencies and and that kind of thing. But I would always really caution against that. They can be useful to giving, um, snapshots overviews, But you have to be really careful because, um, it might have been that the interviewer had used a particular word a lot, um, in every interview, because it's part of the questions. But actually, it wasn't the participants saying it at all. Um, so it's really, you know, you might have something coming up, but actually, it wasn't really salient. And and it wasn't of concern to the participants. Um, so you have to think about those things. And if you're gonna use those kinds of tools, think about that critically. Um, I can't seem to move my mouth out. I can see there's a question that's come up a bit. Oh, yeah. Okay. Okay. Or something. Particularly framework you prefer using. I actually don't prefer framework personally. Um, just because I tend to do more exploratory work at the early stages of I sort of ideas. So my own research is really concerned, concerned with digital health. And, um, a lot of that's quite early stages and news. It's about ideas, generation and the more grounded type stuff. But in the past, when I was more junior and was working for other people on their projects and they wanted to very specific questions answered, um, framework approach was really using the linear process there, and then tabulating data was really helpful for being able to to do that. So that's the approach that I've used on projects and that, generally is what clinicians will want because it does. It answers the question quickly and in a way that you can act upon whereas some of the stuff that I do that's kind of earlier stage a more grounded theory takes longer and is is the kind of it's not so focused. It's a bit more about exploring concepts and things. Can you use a framework approach for quantitative research, too? Um, I don't know. And, uh, the last time I did any quantitative research myself, I have to really confess is 2006. I'm just thinking about going back to those stages. I mean, I my understanding is that yes, you run tests once you've got them in a spreadsheet. So I suppose in some senses you are. But But I don't think I suppose I wouldn't say it's not creatives because I I guess statisticians have to make decisions about how they approach data and they might have to change things, Um, as well. But I think that that qualitative is much more creativelive in the, uh, the thinking. You're you know, you're trying to interpret things and think about what the meaning of them is, So it involves that level. Whereas I suppose if it's like is drug a better than drug be in a In a quantitative study wave, you've given drug aid 250 people and rugby to 250 people, and you want to know who had the better outcomes. I suppose there would be a created thing, and that that may be a was better in some way. But be was better in another way. Like there might be nuances. I'm not saying it's that clear cut. Um, so they might need to be creativity. There may be, actually, um, a was better because more people took it because pill be was too big and people couldn't swallow it, so it didn't have any effect at all, and they put it in the bin. I mean, there might be there might be I'm just being silly, but, um, I suppose there might be things within quantitative studies that require that sort of critical thinking and creatives. I've awareness, but with qualitative, it's very much more sharply and focused that you have to really? You know, the research is heavily involved in interpreting. Um, just because of this, you know, this final stage here is really about How are you then? Mapping back the data that you've got to the research question and and the objective. So all of this kind of part, Although you've got the objectives of the study at heart, it is just really kind of immersing yourself in it. And it is very I've got some images at the end, is very physical if you approach it the way I do, um, and the way I show students to approach it, Um, but it's, you know, it's about the the final stages. And about you saying, Well, okay, so we've got all this stuff and all these people's words and images and things. What does it mean? What does it tell us? And it's it's that bridge of what does all this data tell us about the research question? Um, so that just in a bit more detail to make it a bit more transparent around what you kind of do. Um, as I mentioned, immersion is really what you're doing. Um, so you might be reading transcripts, studying the notes, um, looking through the pictures. And you probably if you're doing all the data collection yourself when you're interviewing people or you're collecting data from online, you're starting to get a sense of what's going on. So you might have a bit of a hunch, but the idea and and words like hunches and feel for material. I mean, that's not objective. It's very subjective. And it is about that kind of doing that, and it within applied health sciences and a more clinical type related research. You tend to work in teams, um, where the researcher doesn't do this alone, so there's less chance of bias of just like one person influencing you have the the interpretation you have. You normally have more kind of discussion around it within a team. Um, so it's not just like my hunch is about things I might share and then say, Well, do you think that's what that's saying as well? Or you might have research teams at different sites who collect data, and then they come together and, uh, bring together. So the ambulance project there were actually four sites. Um, I was the Scotland site, but there was one in Wales. Um, there was one in south of England and I think one in the west of England, Um, as well. So there were four researchers. We collected data. Then we came back together. We co analyzed each other's so that we were not just doing it from our own perspective and just to kind of highlight a perspective that I'm not a paramedic. I have no clinical training whatsoever. I'm a researcher, but two of the researchers who were employed we're actually ambulance clinicians who were doing like a research part of their training, so they were seconded onto the research project to develop research skills. So they, of course, would have seen things from paramedic perspective. I could not possibly have seen things from paramedic perspective. So you know, and sometimes it's good to have members of the team with different backgrounds to be able to see different things, Um, and to do this kind of Familiarization process identifying key issues, um, and then thinking about what was in the literature, what's come up and what's new, any patterns and things that within data. And it might be a geographical thing because you might have found a study from somewhere and then thought, That's interesting. I wonder if that is the same locally, um, or, you know. And it might be just a case of, like, almost repeating work and then looking for what's happening locally. Um, so you indexing is really then about kind of. It's not just a filing convention, but you're kind of, you know, labeling up coding up whatever your data is, Um, but it is. There's a huge amount of work that's been done, Um, as you're doing this because it's it's quite systematic. You're going through it line by line and um, getting it all into a state of, um, or being organized and being, um, sort of them ready to be accessed in terms of its the codes themselves. So it's quite and it's important to do this thoroughly, not to rush, so you don't miss anything. Um, And to make sure that everything has been really thoroughly considered. And it is Yeah, it is systematic. So everything's red and annotated. Um, and it is subjective, though, you know, And it it is good to normally we do like a subset to say, if you had 20 interviews, 20 different people, um, you might have five transcript shared with other members of the team. And to check what's you know, the the person who coded all of them is, um, correct. You might do that kind of early stage as well, So you might do the first five together. You might do it independently to begin with and share your notes and then decide what the coding is going to look like. And then the research would apply it to all 20 so it can work like that as well. So you have this kind of systematic process, but this is what I mean, quite often that details missing from right where people are written up exactly what they've done. So once you've then done that, you build a picture. Um, and you you bring out each theme, and you you write, um, about each thing that you found. Um and that can be it might be by typology of people. Um, or it might be by order of importance of issues. Um, it might be that the most salient thing is the brand new issue that nobody had ever thought of. Um, that came out of the data. So you've got to make decisions around how you present the data. And of course, that again is subjective. Um, so that's, you know, thinking about what is most important in relation to research. Question, uh, is important, but it's there's no right or wrong. It's your own, you know, view on what that is. So once you've done all of that and you pull the characteristics of the day to interpret it as a whole, um and hopefully all of that process will have been quite transparent, and you should at least be able to explain to a supervisor what you've done or the colleagues you're working with what you've done, and you should always be able to trace everything back to the original data source. It's really important that all of these processes are followed and documented. Um, just so that you have that, I suppose, accountability and trustworthiness and that any other researcher could then, although we can't share qualitative data because it's quite often contains sensitive confidential information. But the in theory, what you should be able to do is hand over your original data set to another researcher and for them to be able to follow the same process. Um, and even if they disagree with you about what labels or what names you've given to things or how you've interpreted, Um, that's okay, but it is. It is about being transparent so they could see where you're coming from and why you might have done it that way. But it also is open to the idea that other people might have approached it in a different way, and it might just be to do with background. So, for example, if you're working in a multidisciplinary team, I quite often work with work with health psychologists Um, and they we'll put labels on things because it will be like, based on theory, is that they're aware of that. I've never heard of because I'm not a health psychologist. So they might say, Well, that that thing that you're identifying there, Heather, is called this in my language because this is how we see it in the discipline. And then you might have a conversation they might say, Okay, maybe I need to go back. Or maybe I need to adopt their label. Just depends on the project. So that's something to think about and who's involved in the interpretation. Um, And if it's, you know, if you're working with clinicians, it's sometimes it's really important to involve them. Sometimes it might not. It might be better not to to present, because they might try to say, Well, this is how I see it. But you're actually saying no, But this is how it seemed from this perspective, and you you might want to package it all up first and then hand it back to them rather than involve them. It is, and actually patient's are quite often involved in analysis now as well, so you'll have heard of patient and public involvement in research. PPI, Um, that's a major thing now for securing research, funding and making sure that we involved people. Um, all stages of research. And quite often, people like, um, you know, we have committees of stakeholders who might be involved in the interpretation as well. So we ask them, you know, this is how we as researchers, might interpret it. Is that how you would interpret it as people who have similar concerns as the participants? So, um, at that point, you you're not share ing raw data. You you know, it has to be anonymized, and it has to have been written up in a way that preserves the anonymity and confidentiality of the original participants. But it is something that is increasingly done that, um and then this final bit, um, is probably the most difficult to describe. And again, that's why it's likely missing for method sections. But it's it's really about Google's reviewing, comparing, contrasting searching for patterns and then trying to write it up, um, into a coherent story. And it is about storytelling. Um, a lot of the writing is about storytelling and capturing individual people's voices and representing them. Um, and huge care needs to be taken in that process to ensure that we're not misrepresenting anybody, Um, that were being true to, You know, it's quite humbling that people allow you into their homes, for example, to do an interview, Um, or that they share some really, you know, personal information. And sometimes it can be really painful. Like I'm starting a new project with some lawyers at Robert Gordon University, and we're going to be interviewing parents of, um of Children who are really sick, Um, about end of life care and decision making, um, and shared decision making within hospitals, Um, and how to avoid conflict and conflict resolution. So it's a legal medical legal project. Um, I've been training the interviewer over the last few weeks and, um, talking about you know what if somebody cries or you know what if they get really upset and as a qualitative researcher, you you know, those kind of things can happen. Um, and then you when you come to the writing, how do you then sensitively include that? You know, this was really emo emotional work. It's emotional labor for the researcher. Um uh, for the participants, there's, you know, it's not just about saying, Oh, the quick answer is this. It is very much about making sure you take care of, uh, of what people have told you. Um, so that's just one sort of approach. And it needs to be adapted each time to suit the specific project. Of course. So and this is why again, you know, it's quite it could be this. It could. There are lots of different options this place, so it can be quite overwhelming. But it really just depends on the piece of research. And it's always worthwhile, you know, working within a team and getting feedback on the approach from others. Um, and just in terms of sort of being a bit more transparent about the coding process as well. So you you have different labels, I said in different textbooks. They have different labels. Sometimes they even split out the the coding, and you can see the word story there. So, um, there are these different stages. You might do open coding where I'm talking about, post it notes, then you're sort of organizing the Post it notes, and then you may be you're only choosing which ones to actually talk about in the writing, so that's something to consider as well. And that's not wrong. Um, but it's. And sometimes in big projects you might have from one set of qualitative research or interviews you might have several papers that published based on different things have come out of it. Um, because it can be quite complex. There can be multiple stories that are not necessarily contradictory, but they're talking the stories about different things, and so sometimes what we'll do as teams is carved out. Well, there's actually two papers here, and we need to cross refer, but we will write them up as two separate things. So that's something to consider as well. And this is just to show you the kind of software that we use. Um, Viva. It's very much like Microsoft. Um, and it's, uh, like, you know, there's an updated version of that. That's an old picture, actually, but they have it looks very much like the, uh, sort of Microsoft package, and it works really well. You can code. You can highlight data. You can code data, um, in here so you can, like, highlight and you can right click in the mouse works in the same way as it gives you options. You can then put a label on this. So then, if something somebody else talks about is similar, you can code it in the same way. So this is almost like the if you were using, like, highlighter pens on paper or post it notes. It's a kind of digital version, and I also mentioned mirror as well. But, um, this is, you know, this is always what I have in my office load of pens and pencils, um, and post it notes and tracing paper. And you have transcripts like this. Um, sometimes we might tabulate, and you can see here, Um, this is a student project. We laid it out on the table, but I've had had this kind of thing up on the wall. I've had it on the floor where we've called about on hands and knees and and basically using those colors to look at. Okay, this seems to be similar to this thing. This, you know, and, um then the tracing paper over the top, where you can see just written words around categories, and I don't feel if it's too small for you to see a bit, then circling things with different colors as well. Um, and someone's asked, Is coding an important skill to learn and research? How do you go around learning to do it? Yeah, coding is really important, and it's different from coding like computer coding writing code. Um, because that's another disciplinary difference. So I work with computer science scientists a lot, and we'll talk about coding is quite different things. Um, it is a really important skill. Um, I would say as well, like, even though I'm really experienced in doing this work nearly 20 years, um, I each projects different. So I would say you approach it the same way each time. Even if you're quite experienced in it, you have to work with the data, Um, fine, and identify the best way of approaching the data and organizing it and looking for the codes. It depends on the research question. It depends on the topic. How much is known about it already? Um, so the best way is really, um, I would say to start off with is to read some qualitative research papers on topics that you find interesting. Um, so there are lots of them now in lots of different areas of health research healthcare. It's, I think, important to sort of look at the papers and look at what kinds of codes and things that people have written about in different styles and become familiar with that. And then, if you were to do your own project, you know, do it on something that you're interested in and that you know about so that you can then see how the codes relate to the literature of the code. You know what's new or different, as as a social scientist, I don't really have like areas of it, or I don't have a condition of interest or population of interest. Generally, I'm interested in digital health, but it doesn't matter what population, what condition or so it's a lot of There's a lot of diverse questions and different project types, but it might be that you've got a clinical area that is very interesting to you, and you want to develop so you might do some some really focused work, Um, where the codes are kind of predefined in some ways, and and then that will determine your approach. It's really about the research question and where the state of knowledge is how you approach it. I've put here some, um, projects, Um, that use framework approach so you can follow up up the links, um, and then one that uses casestudy grounded theory type approach so you can see. So this might be a good start to look at how researchers have approached. Um, basically this kind of work. Um, I, a co author on the 3rd and 4th, studies and colleagues Aberdeen on the 1st and 2nd. So it just shows you a few different ways of framing. You can see the topics are different neurological cancer, and, um, our vaccine breastfeeding, smoking cessation and, um, pregnancy, indoor air quality measurements. So different topics and populations. You can see how that hands out in practice. Um, I've put here a couple of links as well. Um, to, um, qsr is the company that make the M v very software their website. Their YouTube channel is really, really good. Their support is excellent. Um, and they really show you how to use, um Viva in really different ways, depending on the types of data that you have. They've also got a sample project. So, um, as University of Aberdeen, um, I t account holders we all have access to, um and devote to be able to download. I think it's on the computer classroom, Uh, the computers in classrooms, um, as well. But you can get it through the toolkit, so it's worth downloading and having a play with the sample project, because that really gives you quite a good overview of how you might use different data sources and how your code and things like that. But here I just thought this image is quite interesting because it shows you the sort of color coding, the sort of labels and things and and how that kind of physical cutting off of bits of paper, moving them around looks. But envivo does digitize that for you. But but most qualitative research, as I know, um, will use paper at some point. And I'm I went almost exclusively digital maybe about five years ago for everything else. But anytime I've got a data set, I will need to at some point, print, print some of it and do something with it with post inmates um so I think that kind of brings me to an end. I've got another resource there, and I'll ask him and to maybe share the slides if anyone's interested in following up. Um, but any questions just now or happy to discuss or if you're in the future, find yourself wanting to design a qualitative study, and I'm not sure I want some advice or someone to look over it very happy to do that. Um, it's a question. When you have multiple types of data, how would you determine which type of data should have higher significance, the outcome analysis than other types? Yeah, so? So that's a good question. So there's something that we use now quite often called photo voice. Um, and so you have a people. You give people a camera and you or they use their own, and you ask them to take photos of things in relation to questions and then to talk about them. So you've got the photo and the words, um, so you would present those together, perhaps. Or you might remove the images, but use the words and you might mold the words of different participants. So if you said Okay, can you take a photo when you feel happy? Um, each day for the next seven days, then you're gonna have seven images per person. And then but what you might want the people to do is talk about what it is that made them happy. Um, and then the images might be pushed aside because what you're interested in is how they articulate, um, they're at their happiness, Um, or with other things, it might be that the You know, Um, say, if you were interviewing a dementia patient, for example, you might have case study where you're asking the dementia patient to write their name each time you see them and you might be asking them questions about other things, and you might then present you might be looking at deteriorate patient deterioration, for example. Um, and you might then want to show those images alongside, um, you know, talking through the story. And it might be chronological rather than by theme. So sometimes it might be about that and looking at okay, what are the common things that are happening to dementia patient's at this stage at that stage at that stage, but then showing the the images as well might be quite, um, important to include those. So it just really does depend on the project and what you decide to include or exclude and how you do that and quite often because you wouldn't be able to include everything. You might have an appendix with some of the images in, for example. Or, um, it might be that the the words don't count as much because you're more interested in the images. Um, I would say that's more, perhaps in sort of social sciences as well. And that is some of that quick creatively work is is much more prominent in the social sciences than in applied health. But people are using much more creatives approaches now, Um, as long as you can justify why. But even then you know another research, you can come along and say, Well, why would you do it that way? Because you should do it this way. Um, which is fine. But I think as long as within a paper or, you know, a dissertation or something, you justify why you're focusing on one source rather than another. And actually, social media posts can be quite a good example of that, because sometimes we we often use tweets like Twitter tweets, um, analysis. And, um, it's quite important sometimes to retain the integrity of a tweet that if it's got an image and if it's got text keeping that together but then doing higher level analysis of the words you you lose all of the images because you focus on the words because you want to focus on more words. Um, so you might have some exemplar ones that you select as images within a paper, but you have much more around the words. Um, I hope that helps a bit. And how does one organized ethical approval work and grant application? Um, so we have a you know, the university has, um, an ethics review board while the School of Medicine has an ethics review board and the NHS has an ethics review board. Um, and you have to write a protocol, and you have to submit any study documents that any patient information sheet about what you want them to do. Consent forms, Um, what data you want to collect from them, and then they have to be submitted through the university systems or the NHS systems to get approval. So you need to know what you want to find out and who you're going to be asking. Um, why, um that can be really difficult with observational studies. Because you can say you don't know who all the people are necessarily or how many or, you know, the ambulance work that I did. I could say, Well, I'm going to spend about 12 hours, um, in an ambulance several times, But it's not clear I might. There might be seven patient's. There might be 14. There might be, You know, you can't always specify, um, and it you have to negotiate, sometimes in different projects. And if there are partners like Scottish Ambulance service as well, you have to explain how you get access to your sample and who gives access and who gives consent and all of those things. Um, in terms of grant application, you can apply to any funder. Um, and you know, it depends on the level of detail that they require, but they quite often require a good explanation of what you plan to do, and then they can choose to fund it or not. But it's by funder and their own system and applications process. I see where I'm sorry. I realize we're on nearly three o'clock, so I should stop there. But I hope that that has answered questions and has been useful in some way. The Thank you so much, Doctor Morgan, for answering all the questions. Is there any more questions from from the audience? Oh, so if there isn't any, um uh, thank you very much, Doctor Morgan. I've definitely learned a lot. So just before you guys leave, we would really appreciate if you could fill out the feedback home, which I will send out the link now. Yeah, you should see in the chat now, so it'd be great if you could fill up the form to help us improve. It would only take less than three minutes. A certificate of attendance will be generated for you after completing the form. Um, our part four of the Webinar series will be held on the 16th of this month, which is next Wednesday. And we have invited Professor Vo Main to speak on academic academic writing. Please look out for our promotion on our instagram at, uh at Dash Scotland closer to the time. We hope to see you again very soon. Thank you very much again for joining us today and have a good day. Thank you, Doctor Walking. Thank you. Bye bye.