Jess Hagman’s Illinois Experts Profile
Have you ever thought about what really goes into supporting digital scholarship? Well, some may say it takes a village but here at the University of Illinois it’s bigger than that. It Takes a Campus. The Scholarly Commons will be interviewing experts across campus about all the new and exciting things that are happening to support digital scholarship. We will sit down with a specialist to learn about what they do, how they do it and why they got started working in their field. Hear what we mean when we say it takes a campus to do what we do.
Ben Ostermeier: Hello and welcome back to another episode of “It Takes a Campus.” My name is Ben, and I am currently a graduate assistant at the Scholarly Commons. And today I am joined by Jess Hagman, who is a librarian at the Social Sciences, Health, and Education Library here at the University of Illinois, and she does a lot of work with qualitative data analysis. So, Jess, welcome to the podcast and thank you for taking the time to talk to me today.
Jess Hagman: Yeah, thanks for having me.
Ben: So, let’s get started, um, basic, well, I guess basic stuff, which is, can you define qualitative data analysis for our listeners?
Jess: Sure, it’s actually really difficult to define qualitative data because we tend to think of data as numbers, and really qualitative data could be anything non-numeric, so you could be using images, video, text is of course very common. So, any sort of analysis of any non-numeric data I think would be considered qualitative data analysis, which is why it can be tough to learn and teach because there is so much variety of methods of analysis and types of data out there.
Ben: Great, yeah. And then, so how did you get started working with qualitative data analysis in your career?
Jess: So, I was, I came here in 2019, before that I was at Ohio University, and I did some research there that was qualitative of my own, with some colleagues. So, we did some basic qualitative analysis, and it was something I was interested in. And then part of what I was hired here to do was to teach the workshops on qualitative data analysis software tools for the Savvy Researcher, because my predecessor had done that, and it seemed like there was a need for that. So, since I’ve come here, I have continued to learn about a wide variety of tools and strategies for data analysis, qualitative data analysis, as I could. Because working with people here and doing my own research, it’s really made it clear how challenging it can be, but also how, there’s just a lot of different ways you can work with qualitative data that are really exciting, and there’s never one tool that will work for every situation, so by having a very broad base of options for folks, we can help them figure out what would be best for them to do for their research.
Ben: Great, and so when exactly was it that you started here at the University of Illinois?
Jess: I started in August 2019, so that’s what, two full years? Three? [laughs]
Ben: Three years this August.
Jess: Yeah, it’s strange to have started so close to the pandemic, it makes the time feel different, but I’ve been working here for, coming up on three years now.
Ben: And you were at Ohio State?
Jess: Ohio University
Ben: Okay, not the same [laughs]
Jess: Yes, exactly, and they feel very strong about that
Jess: They were around first before Ohio State, but yeah, it was in southern Ohio, it was a large research library but not nearly as large as here, so, it’s been a very exciting place, to move here and get to work with all the resources we have here.
Ben: Mm-hmm. So, what are the various ways you support qualitative data analysis, or QDA, here at the library?
Jess: One of the biggest things is doing the workshops through the Scholarly Commons. In about an hour, I’m actually going to teach one about free tools for qualitative data analysis. I do, I think like five different workshops. The two are more general, planning qualitative data analysis and free tools, and then I also do specific workshops on NVIVO, ATLAS.ti, MAXQDA, which are licensed software that people might use. ATLAS and NVIVO are in the Scholarly Commons, so they get used quite a bit, but MAXQDA also has, I think, a growing audience on campus. So, in addition to those workshops, I have a research guide where I have workshop recordings and slides and just, information that I gathered over my time here. Part of that I draw on, I co-teach a course in educational psychology on the use of software in qualitative data analysis, which has really helped me work closely with a larger group of graduate students for a long period of time, so I have the opportunity to see the many different ways work with their qualitative data over the course of a project. And then I also just do individual consultations with people who make appointments through my appointment schedule or who contact the Scholarly Commons to talk about how, specifically how the tools we have available can be used for their project, which is always a lot of fun.
Ben: Yeah, and we, this semester we started having the drop in consultation option…
Ben: …in 220, and I don’t know that that’s seen a ton of use yet, but, um.
Jess: We’ve only had the, we had the snow day, there was snow last week… [laughs]
Jess: …that got in the way of the, so yeah, I haven’t seen a lot of folks, but it is good to know that that’s there, thank you for reminding me, I almost…
Ben: Yeah, it’s okay we just…
Jess: …forgot about it.
Jess: But yeah, it’s a great way to let people know that there is help available, especially if they’re already in the Scholarly Commons where you have access to so many resources there, it’s good to be located there as well.
Ben: Yeah, yeah, we just started this semester and, this semester is still pretty early, and at least as of this recording. Podcast probably won’t come out for a few weeks, but this is part getting the word out to patrons who have an interest in QDA can definitely stop by the Scholarly Commons, that’s Wednesdays at one?
Jess: Thursdays, from one to three.
Ben: That’s right, Thursdays one to three, in the Scholarly Commons, that’s Room 220 of the Main Library. So, what are some uses for QDA that people may not realize who might be doing research with qualitative data but they may not know it, or that they might want to do with it?
Jess: Mm-hmm, one of the big things is people often want to code qualitative data, so that’s where they take some chunk of the text or a part of an image or even a media file and apply a label to it. And that helps people to, in addition to kind of looking at each document, to look at it by some sort of topical thing that you’ve applied. It might be a code system you get from previous research, or it might be more codes you develop from reading into the data, inductive approach is probably the technical term there, so coding can be done in Word documents, people do that. They do it in Excel documents, so that is totally, if that works for each person, that’s what they should do. But coding using software gives you the ability to test out coding approaches in different ways without having to go back and reprint your documents or losing the work that you’ve already done. So you could code in one way and then code in an entirely different way and keep all of that, I call it structuring, like adding structure to the data, new ways to look at it, so you can keep all that without, they’ll lose it, or you can edit as you develop, a lot of times people will have category development, or they’ll have small codes that they can kind of develop into bigger codes and categories and themes in their data, and so you can merge things, merge codes together so it’s just, it facilitates that coding process in an efficient sort of way. And then after you’ve coded, the licensed softwares have more advanced features for kind of looking at it in different ways, like getting, basically making of table of coded data, maybe for each participant, or for groups of participants, so you can really try out different ways of looking at the data. And I realize you can’t see me, but I’m like
Jess: I have this constant need to describe as like I’m holding my hands up and rotating like a cube or some sort of, cause I just sort of imagine it like just looking at your data in different configurations, in different ways to develop an interpretation, or to test, like maybe you think you see a difference between one group of participants, so you really need to look at the data for each group, and it’s much easier to do that in a system like that, so yeah, coding I think is much facilitated, and what I call retrieval where you look back at the data in different configurations. It can be really powerful, and in some ways more efficient for folks, especially once they get past working with a couple of documents, it can be, there’s a lot of data, and we usually want to look at it in a lot of different ways.
Ben: Yeah, are there particular disciplines that you work with more than others, or do you see people from all across the spectrum?
Jess: Mm-hmm. I would say education and social sciences are the big ones, which is maybe not surprising given that that’s the library I work in.
Jess: There are a lot of folks doing really amazing research in education, so I talk to them a lot. Anthropology, sociology, but I can talk to anyone who’s doing qualitative research, which happens in most fields, I think, and sometimes those fields aren’t, there’s not as much history of qualitative data analysis, so there’s still graduate students for example may have to figure out how to do qualitative analysis without as many courses as they might have in a discipline where it’s a more common thing. So, I can meet with anyone doing any sort of qualitative research, but it tends to be social sciences and education are definitely the big one, and social sciences of course is a huge category, but
Ben: Yeah, I, last semester I was in Museum Informatics and one of our readings was someone who did qualitative data analysis of a survey they did of museums visitors and how they used museum websites.
Jess: Oh interesting.
Ben: So, it was a very specific use-case, and perhaps a field that people would typically think of with QDA, but it can really be used in all sorts of ways it seems like.
Jess: Yeah, what did, do you remember what the, like what the results, did they present themes? Or like counts of how often people said different things? Like do you remember what the results looked like in that case?
Ben: I’m trying to remember because it’s been a while since I…
Jess: [Laughing] Maybe not a fair question
Ben: No, that’s okay, um, definitely I can link to it in the transcript, um, but I believe that, as I recall, a lot of it was more, I mean there was an element of quantitative data…
Ben: …as well in terms of like, what percentage of museums visitors go to a website for the museum after they visit versus before they visit and what do they use on the museum [website], but there was analysis of what like, textual survey responses…
Ben: …that I don’t remember the exact details of.
Ben: That was going on as well. And I remember they used NVivo, I think.
Jess: Oh, okay. It’s pretty common. NVivo is pretty good for surveys. That’s another thing, it can facilitate survey analysis, and you can use the quantitative data that you often get with a survey in combination with qualitative data that you code to look at it, to structure. And the reason why I asked you about the outcome is that’s something I talk to people a lot about is they, well obviously they don’t know the answer to their research question, they should have a sense of, it helps if you have a sense of what that outcome might look like, so sometimes people want to code and say, you know, ten people said this, forty people said this, and that means, you know, whatever for our topic. But there’s also, you know, you might identify discourses and describe those, or themes, or narrative stories that people tell, and having a sense of what format that might look like I think can help people figure out what they should do with the data, and whether they need to code or need to use any of those other features, because some of the software can be difficult to learn, so it needs to be worth your time to invest either the funds or the time to learn it, so that’s something that I recommend do a lot, is think about, and to look at articles in their field, so if someone who is interested in museum research and how qualitative research is presented could look at that and see like, what kind of, how do they present their analysis, how do they describe the process of analysis, and then would you want to model on that, or maybe do it differently. It can be a really valuable step before people jump in to doing their analysis.
Ben: Right, so along those lines, do you have any particular favorite QDA projects, or projects you find particularly interesting, that either you know about or you actually, like, consulted with patrons about, or if, you don’t necessarily have to definitely pick some, but if you have some in mind?
Jess: I’ve been working with some people who have very large-scale survey data, which may actually be too large scale for qualitative software, I think they might need tools for text analysis, but it’s been really interesting to talk to them about how they might take this very large data that has lots of variables and also lots of qualitative data, and I know two students who are working with the same data set and very different questions, and it’s really interesting to see how they are doing that. And I don’t have a specific example, but I do think it’s interesting there is more effort to share qualitative data, like we have our repository, IDEALS. People could share qualitative data there, but there is qualitative data, the qualitative data repository, out of Syracuse, is just qualitative data. And I think ICPSR has qualitative data, it can be a little tricky to find, but I think it’s exciting to see what people do with that, cause it’s, qualitative data analysis often assumes that you’re very familiar with the data, or you were there when people were interviewed, but I’m curious to see whether more people will try to re-use existing data even if it was not the context in which, they weren’t there for the data collection, so I think it will be interesting to see how that goes in the next couple years.
Ben: Right, um, yeah so, one I think big challenge with QDA is that, and I know you probably talk about this a lot with your Savvy Reseachers is that, there’s a lot of different software options available, and some of them are proprietary and very expensive for individuals to use, and we have some of them available through the Scholarly Commons, which is great, but, you know, not everyone necessarily has that option. And there are some free options as well, but, because they’re free they’re probably not as fancy, so what your recommendations for how to handle that if you have any quick answers, I mean, broadly…
Ben: …I suggest you go to a Savvy Researcher if you’re interested in this, but…
Jess: Yeah, um, so, going back to that question of what do you need to do with your data, there’s Taguette, which is like the word baguette but with a T.
Jess: Which is a really great simple tool, you upload your text, you highlight and you code, and you can export that to an Excel file, which a lot of people do, they actually copy and paste data into Excel, and then they manipulate it that way, so that could save people some time. So I think people who are doing pretty basic text analysis, small amount of data, not super complicated coding, or if they’re working with another person, cause it’s actually either on their server or you can have it on your own server, you can install it, it’s open source and, so you can actually work collaboratively with people, which is much harder to do with a licensed software. You often have to, like if you and I were working together, I might have the original file and I would give you a copy, and you would code it, and then I would have to merge them back together, which makes me a little nervous to be honest.
Ben: Yeah, that sounds terrifying.
Jess: Yeah, it does. And so I always encourage people to do a test of that, so I would say, a lot of people that I talk to, they could work with something like Taguette, or it would help them with their process where they would normally use Excel, could facilitate that a little bit. There is a tool called QualCoder, which I’m going to talk about in the workshop today, which is more complicated. But it does have some of those features like you would get in NVivo, like the ability to automatically code data or to give you like statistics on your data, or even to compare, like if we both coded we could compare how we coded, so like an inter-rater reliability, so it has more of those features, it just is a little, it’s a bit more complicated to me, as someone who, it’s like based on python, and it’s beyond most of my skill level to get the, the installation was a bit of a challenge, and that’s just someone’s like, personal project, both of them are personal projects, so there are people who work on them in their free time. So we’re really fortunate to have them, but that is always kind of the debate, so I would say for whoever, if you’re deciding, the big things would be: how much data, do you need to collaborate, and then do you need those more advanced features like automatic coding, reporting out, and more ways, and in that case looking at either QualCoder or one of the proprietary options, thinking about licenses. There are usually licenses for graduate students that are much cheaper than the full license, so that’s something to look into. But yeah, so I would really think about what you need to with the data, and see whether something simpler would work for you, and then kind of move up to something if you reach the limits of those tools.
Ben: Yeah one challenge that I often have dealt with, not with QDA so much, but other, using free tools versus more advanced tools is oftentimes, more advanced, in some cases more expensive tools can actually be easier to use just because they have better user experience design going on, does that happen with QDA software or is it because it’s simpler, it’s easier to use, or does it vary?
Jess: Uh, I think both, I think Taguette is just much simpler to use, like right now, it’s purpose is really just you highlight text and you apply code and then you can export that data, so it’s a very, it’s not meant to do anything more complicated than that, so that is helpful, in that it’s very simple, I recommend it to people all the time, or people who maybe they have full software, but they wanna work with maybe a student who doesn’t have access to that software, they can start maybe developing a coding scheme, you know, working together with a smaller amount of data in the Taguette. So, it definitely is simpler, so and like Qualcoder, as I was saying, has a lot of the same functions or tools as like MAXQDA or NVivo, but the interface is like, I’m constantly like scrolling over the buttons to remember which does which thing, cause I don’t use it enough in my day-to-day life to have that memorized. And there’s some features that I have not quite figured out, so without, as you were saying, the full force of, you know, a professional, a team doing user design, and the companies behind MAXQDA, Atlas, and NVivo are pretty big companies that can devote the resources into the sort of product development, but then you have software that costs hundreds of dollars if you don’t have like a student license or if you are not getting it through campus or having someone else pay for it, so it’s definitely a trade-off, and there’s also version issues as well, like NVivo looks different on Mac and PC. That’s actually one of the reasons why I like MAXQDA a lot, is that it looks the same in the class that I teach that’s what we’re using is because we’ll help folks using PCs and Macs, so if we’re using MAXQDA it’s all the same interface, and Atlas is different as well, so that’s something to think about too. So, I think maybe I’ve gotten a bit away from your question, but yeah…
Ben: That’s okay
Jess: …I think, the gist is that the companies that charge a lot of money, they have the funds to do that and to put out a new version every year, it seems like, so yeah, it’s one of those things where you can have it, you know, quickly, you can spend a lot of money, or it can be easy, and you can really only have so many of those things, it’s not going to be quick, cheap, and easy at the same time…
Jess: …it’s always a tradeoff.
Ben: Yes, pick what’s most important.
Jess: Exactly, yeah, right.
Ben: Yeah, that’s why you don’t just recommend one software…
Ben: …for everybody
Jess: Yeah, for sure. That was an important thing to me to make sure we had options for people who couldn’t come to the Scholarly Commons, because it’s, you know, it’s an amazing space, but it’s only, it limits the access, and then, you know, people who just, you know, aren’t able to, you know, for whatever reason, don’t have access to that software, it’s complicated. Access is a huge challenge, and some of those resources are often, they are used by people outside of the university as well. Like I wrote a review of different software options that’s been shared beyond here, so trying to contribute to the general knowledge about qualitative research, because there’s people doing research all over who don’t access to the software, you know, and that’s unfortunate. Not that you have to have it for most cases, but it definitely, you know, can help you think about data in different ways and make things more efficient.
Ben: Mm-hmm. Yeah, so you mentioned, just real quick, you mentioned that you teach related to QDA, is that just, what is that class?
Jess: It’s a special topics course in Educational Psychology that I co-teach with Dr. Rodney Hopson, who is a professor in Educational Psychology. It’s a delight, honestly, I just, every time I go to class, and we get to be in person this year which is nice.
Jess: So it’s like 11 students who are all, most of them are doing, most of them are in education but we have anthropology, linguistics, counseling I think, so there’s a range of disciplines, and everyone has a research project that they’re working on in some fashion, and so basically we talk about, we use Taguette and we use MAXQDA, so they learn how to use those tools, but we also talk about bigger picture things like how do you report the process of your analysis? Because often in research articles, there’s not a lot of detail, people will say things like, “Themes emerged” without really describing how they got there, so our approach is to kind of ask students to over-document and to over-describe that process. So it can really clearly explain what they did, and then when they publish, you know, you can always back off from that if a publisher asks you to, but or, you know, in your dissertation you could step it back, so we really focus on being really clear about what you’re doing and why, how it relates to kind of bigger discourses in your field about what makes for good qualitative research. Some of the people are pushing back against, those disciplines that I mentioned where qualitative research has been kind of the dominant mode, so they’re often having to explain to people in their discipline why this is valuable, so we get to have all sorts of great conversations about, you know, how you do qualitative research, how you talk about it, what makes it useful and rigorous and, it’s just, it’s a delight if you’re into that as much as I am. It’s really awesome to just sit and talk with that, so, and if anyone’s interested in that, I have like a syllabus I could share if anyone wanted to contact me. I can show you the readings, cause there’s a lot, there’s a lot out there about the role of software and the ways we do qualitative research that are connected to bigger picture stuff and research and higher education, so if anyone else, maybe not, but if anyone is into that as I am, I would be happy to share those resources.
Ben: Great, yeah, one last question, at least that I have, is, you touched upon this, but, do you have any thoughts or input about the, as you said, a lot of disciplines have more traditional ways of doing research or approaching a topic. So, my background is in history primarily, and the traditional mode of doing things is you read primary sources and you analyze it directly based off of what the sources say.
Ben: And I do know that history has started to move to other forms of analysis like economic analysis or, to a certain extent, quantitative, but I suspect that there is a bit of a, with any sort of academic approach, oftentimes the traditional way of doing things takes a while to make room for the new ways of doing things, so I don’t know if you had any other thoughts about ways to broaden the field I guess.
Jess: Yeah, I guess, thinking about it, I have thought about history, and I suspect, my interpretation, I mean I haven’t, I studied history as an undergrad, but that’s the extent of my history knowledge. It feels like to me, history is doing, you know, if you’re reading primary sources and interpreting them, that is a type qualitative data analysis, you know, you’re probably making notes in different categories, so that it’s almost like coding, but it’s just talked about in a different way, so I think one thing we can do is to identify, and I’m sure there’s research out there on this. It would be really interesting to talk to a historian about how they approach primary sources and, you know, someone interested in a similar from sociology and how those methods, the specific things they are doing with the data, how that would compare. And also just to kind of question the assumptions we have, all of us bring to research about what is good research. When we interpret, so like you said, you know, they’re reading into or reading the primary source documents, being reflective and critical about how your positionality is, you know how my position as a white woman who works in higher education is different from someone else who might read that data, so I guess to not think that there’s ever only one way to read data or to work with data, that there are a lot of different ways, and all of those ways together make us more knowledgeable about the thing that we’re studying, so I think it’s, just a sense of, I dunno, humility or just a recognition that our, all of our methods have limitations, qualitative, quantitative, and that we’re better off as a discipline, as a society, the more types of information we have, the more types of data we use, the more ways of doing analysis like critical approaches and using critical theories I think can give us different insights that we can’t get with other approaches. But that takes practice, and I think, you know, some disciplines are more open to it than others, I can’t speak to other disciplines as well, but I think those conversations, I dunno, it’s just everything, our world is so complicated, the social world, and the problems we have to solve are so complex, and to think that there could only ever be one way of understanding it just seems so limiting to me, so yeah, I guess I, sometimes talking with qualitative researchers to, it’s almost to, to get back to like, this research is important. It may be different than how other people have done it, but that has a limit. Yours has a limit, you know, what can they bring together and big picture help us know about this topic, and everyone I’ve talked to has been doing research that I think ultimately can, it sounds so, I dunno, it makes the world better. Like, we’re better for knowing these things, so yeah, I think that’s, it’s an attitude, but then also they need the infrastructure there to do that, so that’s what I try to do in all of our workshops and consultations is, if someone wants to approach a new methodology then they need resources and maybe someone they can talk to about, you know, how would you work with this data, I haven’t had a chance to do that before, so I guess that’s what I see, part of my role is, you know, for people who are in those disciplines is to be a resource, part of this campus-wide infrastructure, because we do have this amazing infrastructure, but to help them figure out how, what contribution they’ll make to this knowledge that we have.
Ben: Well great, well that’s all I had prepared, unless there was anything else you wanted to be sure to share with the listeners?
Jess: Uh no, just to say that, I think this is true of any librarian and any of the folks working in the Scholarly Commons, you don’t have to have a specific question or, even to have your data yet, for qualitative research. Like I sometimes talk to people who are still deciding what data they will use, and I think in most cases you can talk to people at any point throughout the research life cycle, even after you’ve done your analysis and you want to talk about how to, you know, present it or, I think, I guess I probably shouldn’t speak for other people, but my experience is that people will talk to you throughout the research cycle. It’s not just at any one stage, and we generally, in my experience, we also really enjoy that, so, just to encourage people to reach out and let us know what you’re doing and how we can support you.
Ben: Great, and what is your email, just so I can…
Jess: Oh sure.
Ben: …link to it?
Jess: Yeah, it’s firstname.lastname@example.org. I have, I’ll make sure you have this link, but there’s a LibGuide…
Jess: …just like guides.library.illinois.edu/qualitative, that’s where you can find my appointment scheduler, I put the workshop schedule there, yeah. Oh, and I would also mention that there’s other people doing this work on campus too, like the Qualitative Research Initiative out of the Center for Social and Behavioral Sciences, I can send you that link as well. I think there’s kind of a growing effort to bring together people who do different kinds of qualitative research, so they’ve talked about ethnography and kind of working with IRB, in that case, so there are people who are wanting to come together and talk about those things. I think we’re going to have a panel later this semester about teaching qualitative methods, so definitely to get connected to that as well if you’re interested in qualitative research.
Ben: Great, yeah and of course I definitely recommend Jess Hagman’s Savvy Researcher sessions. I’ve gone to one before and it was very useful, and that’s a good place to at least get started. Yeah, well I think that’s everything, so thank you so much Jess for talking to me today, and always good to talk to you.
Jess: Thanks, thanks for having me, I appreciate it.
Ben: Thank you.
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