Doing Evidence-based Analysis: Applying Grounded Theory Methods to Your Research

Robust, involved research projects can still yield little to no research impact. That’s because your qualitative studies are only as effective as the insights you surface in analysis.

A solid understanding of grounded theory can help ensure you’re getting as much out of your studies as you and your team put into them. We’ll walk through a case study that will demonstrate hands on, effective qual analysis, and leave you with pointers for teaching these tactics to your team.

You’ll learn how to:

• Apply methods that promote users and their diversity of thought
• Develop a “codebook” or a set of thematic areas to systematically “code” or tag across all data
• Analyze, track, and code data into insights
• Identify recurring patterns across data and develop evidence-based opportunities

Transcript:

Ben Wiedmaier:
Hello, everyone. It is 12:00 noon Central here in Chicagoland. I am Ben from People Nerds. Thank you so much for spending a bit of your time with us. We are so very excited to welcome Dr. Rachel Ceasar of the Culture of Health and Technology.

Ben Wiedmaier:
Rachel is someone who is living in, thinking about and working on the areas of health, technology and design. I'll give her some time and some space to talk more about the work she's doing.

Dr. Rachel Ceasar:
Thanks, Ben. And so, thank you everyone for participating with the chat. Well, let's go ahead and get started a bit. This is just a little breakdown for the next hour. Some housekeeping, just where things are materials and a little introduction about how my path into UX research.

Dr. Rachel Ceasar:
And then, we'll just go through what grounded theory is and the different steps. So, this is a shorter version. This is a one-hour presentation, webinar workshop-ish. I'll definitely provide you the materials, so you can follow along. The EPIC workshop is a three hour one.

Dr. Rachel Ceasar:
And even then, I feel like it could really be four. I think EPIC is sold out. But I do these once a month as well, so I can definitely share a link for that. In terms of housekeeping, I mean, this is a shorter version, but I'll put here in the chat where you can get resources, so you can follow along with the transcript.

Dr. Rachel Ceasar:
I'm just using ATLAS.ti. You can use a million software's, they're all in my experience pretty terrible. The only reason I use ATLAS.ti in these workshops because it's free to download and there's no like end date to it. So, it's nice for practicing.

Dr. Rachel Ceasar:
They've gotten some updates. The cloud version is actually really nice. So, please feel free to follow along. And then, I've just shared all the resources that you can use, there's a couple of transcripts in there, there's a codebook template. So, we'll be using those during this our time together.

Dr. Rachel Ceasar:
One thing I want you to add in the chat, and I can see the chat, but please feel free to use Q&A as well. But if you could tell me just so I have idea of the audience. I'm seeing a lot of you from all over the world and our UX researchers, I want to get a feel like where you stand with analysis and working with user data. Is as user data your bread and butter?

Dr. Rachel Ceasar:
Oh, people are saying they need access to the resources. I see. Okay. Well, I will make sure to share that with everybody. Maybe I can even add in the chat. I see what needs to happen. So, let me make these available. I'll just change that. That's going to take me two seconds to change [crosstalk 00:02:55].

Ben Wiedmaier:
Again. Yeah. Well, while Rachel is doing that, this is Ben from People Nerds. For anyone who's just joining, the slides and the recording will hit your inboxes next week. So, the various resources that Rachel is showing, you can certainly access those.

Ben Wiedmaier:
The specific slides will have a PDF version of those next week to you as well as this full recording, just in case we go over an hour answering questions and you have to jump, we will make sure that you get all of that. Thank you, Rachel.

Dr. Rachel Ceasar:
So, I'll change it so anybody can view this who is not a buddy of mine. Anyone with the link can have it now. So, I'll share a new link to the whole folder. And then hopefully, everyone can get it that way. All right. So, I'll do that. So now, okay.

Dr. Rachel Ceasar:
So then, you should have access to it. If you don't, well, mostly because it's only about an hour week. Oh, you can access the resources, awesome. Great. So, just to go back. Just get a feel where everybody is, is user data your bread and butter?

Dr. Rachel Ceasar:
This is something you're doing every day. Maybe when you're approaching user data, it's a little bit new for you, it feels like it's too much data. Or maybe you're more coming like Ben has mentioned, coming more from the quant side and now coming into work with quality data.

Dr. Rachel Ceasar:
So, go ahead and put in the chat, is the bread and butter? This is your every day. It's a little bit overwhelming for you. It's just a lot of data or you're coming from a quant as perspective. I see some people, a lot of bread and butter. This is more what they're working with.

Dr. Rachel Ceasar:
A lot of quant folks too. If someone knows a really great quant version of this type of workshop, please share with me. I'm always trying to up my quant game. So, a little bit about me. So, my background. I'm actually an anthropologist. This is usually what people think anthropology is.

Dr. Rachel Ceasar:
This is how I imagined it like going out into the field. Sometimes it is like this in the past life, a lot of my work was on the exhumation of mass graves in Spain after the Civil War and Dictatorship. And so, this felt like a little bit like that. I used to have a symmetrical hairstyle.

Dr. Rachel Ceasar:
But really what a lot of my work is like is this is I'm hanging out with people in their living rooms after a long day of commuting in LA traffic. They've got their dogs. They got their pals. I'm about nine months pregnant here. I told the client I was only six months pregnant.

Dr. Rachel Ceasar:
I've got the recorder bouncing on my tummy. The recorder is picking up the dogs breathing. Yeah. And this is what it is, it's messy. It's confusing. There's no hot sound bites, it's a conversation with three people and this was a project on air taxis. And I'll talk a little bit more about that case study in a moment.

Dr. Rachel Ceasar:
So, my background is in psychology. My PhD was anthropology. And I started moving into UX Research, but really coming from a qualitative mixed method. I see a lot of you are mixed methods as well. So, I love mixed methods, but love qualitative. I like that a lot.

Dr. Rachel Ceasar:
So, I'm coming always as a qual person to quant data. And that's how I landed where I am between UX and service design. And I love grounded theory. I've found a way to really be able to have a systematic way to deal with lots of data. Some of you mentioned like I'm bread and butter, but a lot of it's just too much data. What do I do with this? Or in both, bread and butter and too much data.

Dr. Rachel Ceasar:
So, tell me in the chat, what do you struggle the most when it comes to user data? Where's the most frustrating part for you all? While you all are doing that, I'll walk through a little bit what we'll cover today and what we won't cover. So, I'll give a little bit of background in theory of applications of grounded theory of being a method that's helpful, not just for understanding lots of data, but also for delegating with your team.

Dr. Rachel Ceasar:
That's something I've found as I've done these workshops. I always do a little UX research when I do these workshops. And a lot of feedback I've gotten is how can I do this as a research team of one? Or also, how can I delegate and use some systematic method across my team with people who may not be researchers.

Dr. Rachel Ceasar:
So, I'll talk a little bit about that, too. We'll go through codebook development, coding and analysis. And we'll use a case study of how to use grounded theory and what that looks like in a real project. Sorry all, I'm not going to be able to go into an hour, the entire research process.

Dr. Rachel Ceasar:
I mean, how grounded theory fits definitely determines what your interview questions should look like. How you should set up your research questions, all that goes into it. We won't go into software. All the software is pretty bad and horrible.

Dr. Rachel Ceasar:
We won't go into quant methods, but there are ways to integrate quant methods into the software programs. And we won't go into inter-rater reliability. So, one nice feature of all of this software is you can delegate. And this will be the little symbol for when I highlight when you can delegate certain task to team members to spread out the word.

Dr. Rachel Ceasar:
Because as you all know, analysis takes a while, yeah, plus one decoding without additional people because it can take a lot of time. And so, I'll definitely try to point out places you can delegate and share the workload as well as a training opportunity for more junior colleagues or colleagues who have not have as much research experience.

Dr. Rachel Ceasar:
So, inter-rater reliability is lots of people coding. But then, using a special number or there's a number that does with the software that lets you know, hey, we're coding about the same and that's a good thing to have. So, this is what my research process usually looks like.

Dr. Rachel Ceasar:
And again, we'll just be focusing on analysis. But of course, understanding each step builds up to having great data and being able to do a lot of cool stuff with your analysis. I won't go into it, but it's going to be here, the hyperlinks. This is the feedback I've gotten.

Dr. Rachel Ceasar:
These are the different tools I'm seeing or what people are using in the field. There's always the QDA, the software for more academics, which to me, I feel like is just like ATLAS.ti and MAXQDA, Nvivo, Dedoose, is that can do a lot of stuff with it, but it can be a little overwhelming or it's not as user friendly.

Dr. Rachel Ceasar:
I find none of them are very user friendly. This is ATLAS.ti cloud. This is condensed. Oh, the other thing I was going to say, a lot of the academic ones are a little bit more expensive. But usually, the academic ones it's not by month, so it can end up the same.

Dr. Rachel Ceasar:
Okay. So, a little bit about what grounded theory is. The definition and the person I look to and I've included some of her readings is Kathy Charmaz, who unfortunately just died very recently. She was in San Jose. So, her take on this is the way ground theory is the answer.

Dr. Rachel Ceasar:
It's a method to help answer why questions from an interactive suite stance. And it's defined as a systematic method of analyzing and collecting data to develop potential theories that is comparative, iterative and interactive.

Dr. Rachel Ceasar:
And that's just to say, there's a million different methods. I'm not saying that this is the best, I know, Kathy Charmaz passed away, seeing that in the chat. It's not the greatest method, it's not the best method. It's a method that works for a lot of the data I feel UX researchers work through.

Dr. Rachel Ceasar:
There's content analysis which is looking at the frequency of how much maybe one word is said. So, every time shame comes up in the transcript, that exact word, you count that versus maybe a description or conversation about shame won't be picked up in content analysis. Discourse analysis is really looking at language and utterances.

Dr. Rachel Ceasar:
Phenomenology, you're looking at lived experiences. Our sister method would be thematic analysis, where you're looking at themes and patterns which is really helpful. I feel like though grounded theory, what I like about it for UX is it takes it... it's trying to get to the next step of not just themes and patterns, but also to talk about theoretical statements or a statement that you can say, hey, I know the data really well.

Dr. Rachel Ceasar:
I know all these 20 people I interviewed, whether you interviewed them or not. But I can make a statement or advocate for these 20 people based on doing this type of analysis, a systematic analysis. So, instead of just maybe the first five people you interviewed or maybe the five people whose interview stood out the most out of the 20.

Dr. Rachel Ceasar:
The goal here is that you're looking at all 20 people and really trying to understand what they're saying and then create some statements that you feel confident based on what they're saying, all 20 people. It's that thing that all those 20 people are talking about the same thing and it's known and you're the one putting it together from what they're saying.

Dr. Rachel Ceasar:
So, grounded theory. I love this definition, it's recipe based. So, if we're all baking, we all have the same recipe, we hope that what comes out of it is we all make the same cake. And that's how grounded theory approaches qualitative data. It's a tool for studying processes.

Dr. Rachel Ceasar:
What's very nice about grounded theory in terms of using it for research sprints or for building depositories of data is that it promotes an openness. So, you get to lay out every theory, every theme that you're working with, laid out on the table.

Dr. Rachel Ceasar:
And maybe you'll only have three theories or three statements for this particular project. So, it's nice. It leaves it all open, lays everything on the floor. And it makes it really nice for others to pick it up and later work or to build on research. And so, when to use it?

Dr. Rachel Ceasar:
I use it a lot, but there is a rule of thumb I like to use, for one to use this type of method. Because as you know, analysis takes a long time. And so, my rule of thumb is, if you've got about five transcripts and they're about 60 minutes each of one on one interviews, or they're focus groups, and you got a couple speakers. That's all like over 100 pages of transcripts to try to wrap your head around of what people are talking about.

Dr. Rachel Ceasar:
So, when there's more than about 100 pages of printout transcripts, that's what I'm going to use, it's going to save me time. As I mentioned, it's great for research sprints, it's great for building a depository of data. I've used it and I'll tell in this case study of with air taxis and looking at urban mobility.

Dr. Rachel Ceasar:
We did the project in LA, Sao Paulo, Dallas, San Francisco. And we're asking the same interview questions to people all over the world, that was also a great place to use it where we can see, we can just look at the LA data. Or we can look at the LA data with all the other cities involved and try to pull analysis from that, so that was also really helpful.

Dr. Rachel Ceasar:
It's great for co creation. And again, we'll talk a little bit about what this interaction looks like with you and the client as well as with your team and where different places you can delegate. And again, to build upon research and have future research to go back to.

Dr. Rachel Ceasar:
So, the recap. The goal is to understand here not just grounded theory methods, but to set up a system to analyze as well as organize user data. So, we got limited time. But hopefully, some of the goals here and with some of the resources I'm providing is how to read transcripts for context. How to develop a standardized codebook to systematically tag key themes across all data.

Dr. Rachel Ceasar:
How to analyze track and code data. And then, to take all that, you force the data to organize in a way that's data driven. And then, the pull-out statements from there or theoretical statements. So, I'll take a moment there and look to Ben and look in the chat here. Any questions or any [crosstalk 00:15:22] burning question-

Ben Wiedmaier:
I'm not seeing anything here. A lot of folks were just mentioned time being a bottleneck. Of course, getting buy in for this rich, deep detail. So yeah, any time saving to the extent that you can, any time saving is helpful. And [Catherine 00:15:37] says, "Static on the audio."

Ben Wiedmaier:
So, if anyone else on the call, I am not hearing that from Rachel side, it could just be me. But if anyone else is having audio issues, please drop it in the chat. We'll take a look for. That's it, Rachel.

Dr. Rachel Ceasar:
Cool, thank you. So, in the past project and recent project where I use grounded theory was for this research question, how might commuters incorporate air taxi travel into their daily lives? So, that's what these interviews were. And this is what it looked like.

Dr. Rachel Ceasar:
We did focus groups. We did one on one interviews. We got that raw data. We coded it just for LA and then for the rest of the cities. And then, this is one of the statements we came up with, who is air travel for? Access for all. This was extremely important to people.

Dr. Rachel Ceasar:
We made those statements. There were some quotes to back that up. Because this is all, again, a person driven. We thought of some opportunity areas of what this could look like as a product or service. And then, what came out of it for the next step, for the industrial designers and the engineers is to build the eVTOL air taxi around it being accessible, autonomous and courteous.

Dr. Rachel Ceasar:
And this is what came out over and over in our data. I want to be able to wear a skirt and not have to worry about my skirt flying up if I'm a woman. I want to be able to take my elderly parents. If they have a cane, will they be able to use this air taxi?

Dr. Rachel Ceasar:
And so, this came up over and over again. This is Embraer is a legacy company. So, to push you X to the forefront and have it drive design was extremely exciting for qualitative research to be there for them. The case study we'll be working with is looking at the role of farm employers and meeting the delivery of health and HIV services to farmworkers.

Dr. Rachel Ceasar:
So, this is a little bit more researchy, a little bit less UXey, it's more like a service design project. But the reason I use this example is because there's a lot of context to it. So, even though the transcripts will look very straight forward, no one says apartheid and no one talks says race in these transcripts, and it's really the background to what's happening.

Dr. Rachel Ceasar:
So, just a little background, this is a study from South Africa health insurance company, wanted to see, hey, should we keep giving money this year to these different health services, private and nonprofit? Are they making a difference for farmworkers and their employers?

Dr. Rachel Ceasar:
This is what we end up coming up with for the final presentation, the final deck that we gave to the client. And I want to show you, this is a real project. This is what the breakdown was. I was seeing people, the frustration or where you struggle most with user research, whether your research team of one or a large team, it's time.

Dr. Rachel Ceasar:
It takes a lot of time to do user research and also takes time to do analysis. But this is what we came up with the team and I had to break it down. It's about half a day to onboarding, familiarize myself with what the project was about. We had a coding meeting.

Dr. Rachel Ceasar:
That's where I had read a couple transcripts, I say, "Hey, this is what I'm feeling are the main codes. This is what I'm feeling is happening in the data." I took about four, five days to code. And then, I presented, I threw on the wall, hey, this is what I'm hearing across all 20 transcripts, how does this ring true to the client and their capacity to take next steps?

Dr. Rachel Ceasar:
And so, that was about seven days. I think I did about 45 hours, less than 56 hours. So, that's what this look like. And the goal, again, was employer attitudes and practices to the provision of HIV, TB and related health services for farmworkers.

Dr. Rachel Ceasar:
This is what that project looked like from start to finish. And so, we'll be using this project to understand it. Yes, you will. There will be a recording of this session for people asking it. And we'll get more into codes, I'm seeing from Stephanie. We'll go through that process as well what this looks like.

Dr. Rachel Ceasar:
So, this is the first part, getting your data ready. And there's different things you can do. So, you'll see when we go through this, the analysis part, which is at the very end, always feel a little bit weird in the workshop like that part of the workshop, the three-hour workshop is really short.

Dr. Rachel Ceasar:
But because you're doing so much work and checks and balances in the beginning, that once you get to the actual analysis part of really understanding what's happening, there's not as much work to do. A lot of it's happening ahead of time.

Dr. Rachel Ceasar:
So, for this first part, we're going to go through transcript one. And I'm relying a little bit less on the software, not just because there's so many different ways to work in software, but this has always been the way I've done it. I work in Google Docs or Word or something where your clients or people who don't have the software or maybe are uncomfortable using the software.

Dr. Rachel Ceasar:
I try to have it as accessible up front between when we're getting the raw data, try to use a resource or tool that as many people can access. And then, as we get more toward the more specialized or expert work, then I move more to the software.

Dr. Rachel Ceasar:
So, it's just something I've been trying to do. And so, the first part is, as soon as you get that interview back, whether you're the one doing the interviews or someone else's, is to process it, to use some time to go ahead and process it. And what's called officially initial memoing. but we're just going to call brain farting.

Dr. Rachel Ceasar:
Because I got feedback what that doing memoing, initial memoing and then the memoing you do later in the software was very confusing for people. So, we'll just call it brain farts. And this is anything, I mean, it can be free association. As soon as you get that interview back, you have it transcribed which I won't get into here, but if you know me, I'm really for humans doing transcriptions, not AI.

Dr. Rachel Ceasar:
If you are a consultant or on salary, if it takes you time that you have to go back and listen to the recording, it's not worth it. It's already costing you more money, the project. Great, better to have a professional do it. So, you get the transcript back.

Dr. Rachel Ceasar:
You're just writing down context, patterns, actions, routines, and I just do that through the Google Docs comments. That way, you and your team can go on there and just what's happening in this interview, just to get an idea. This is also a great way to delegate.

Dr. Rachel Ceasar:
You can send this to other team members or people who aren't necessarily have a research background. Oh, highlighter tool in Google Docs. I have not done that before, but that's a good idea too. So, this is a way to get others on board. I've had clients who like, "Hey, I want to know what's going on the interviews. Let me hear what's going on."

Dr. Rachel Ceasar:
Or, "Let me put my thoughts." Or, "Hey, this is interesting." So, this is a great step to bring people on board. So, let's switch over to sample transcript. And I've included two transcripts in there. So, if you want to try this on your own later as well. There's all these people, armadillos and bats and rhinoceroses in the Google Doc.

Dr. Rachel Ceasar:
So, great. I'm glad you all are there. So, let me open this up a little bit so you can see it. So, this is a real transcript. Of course, I've changed it a bit for ethical reasons, but the gist of it is there. So, you just did your interview, your one on one interview with an employer who works on a farm, who oversees their employees.

Dr. Rachel Ceasar:
And what I like to do as soon as I read through where go through very quickly, at the very end, I like to put a very short summary of what happened in this interview, or this was the guy with the big hat who'd said some racist comments and believe farmworkers are like this, whatever it is.

Dr. Rachel Ceasar:
But just like one or two sentences to say, what was the main thing or the most interesting thing that came out of this interview. And this just can be helpful when you're going through all the interviews and trying to remember what's going on.

Dr. Rachel Ceasar:
So, we'll just go through and read some of this together and I'll open this up, so you can add the comments. And there's no mistakes here. And that's the one thing I want to put in here as well, is that there's no... it's okay, not everything that gets put in this brain farting section is going to be... make it to the end to become a theoretical statement, and that's okay.

Dr. Rachel Ceasar:
I want you to feel free. Even the codes can be bad. I have memos at the end that are just WTF, just whatever you want to put in there. But the idea is, just get to you interacting with your data. And this is the first part when you get your interview data back.

Dr. Rachel Ceasar:
So, we'll just go through this together. I like to put start in the beginning just so when I do go through coding, maybe there's a whole bunch of like, "Hey, the recorder doesn't work." Or, "Blah, blah, blah." This back and forth. That when I actually transfer this to do coding, this is where we start. So, just put that in bold.

Dr. Rachel Ceasar:
Okay. So, we'll read a couple of these. So, how has HIV impacted this district? I don't know. It's not that big impact, but if you take the district every farm has people with AIDS. For instance, three people per farm, that is 15 days you're losing on the farm, that affects the farm because you have planned work stuff.

Dr. Rachel Ceasar:
But the irrigation guy, the guy who puts water on the trees is not here for five days. Now, you must get someone else from the other section to come and fill in for him. Or you must teach another guy to stand in for him, so it affects the farm a lot.

Dr. Rachel Ceasar:
So, I'm going to over memo or over brain fart. I won't maybe necessarily when I get a 30-page transcript back go through every paragraph like this. But you can imagine what this might look like when you get it back. So, what I'm hearing here, and again, these aren't descriptions, you don't really want descriptions, you want what's going on, what might connect to another transcript later on.

Dr. Rachel Ceasar:
So, I'm hearing here, if someone is sick, that's a loss in productivity. So, maybe there's something about productivity and AIDS here. If someone is sick, then I lose money. They didn't really say money, so I'm not going to push that. We'll go like this, so you can open this up a little bit more, so you can see it better.

Dr. Rachel Ceasar:
Okay, there we go. Okay. So, that's the first. And then, the transcript continues, can you give examples of how HIV has affected this farm? You get through the season and people just stop working and they disappear. You had one of our top farm managers, he was very ill, I think he was absent for three months.

Dr. Rachel Ceasar:
We had him in hospitals and doctors, I'm not sure if it was malaria or HIV, farmworkers don't want to talk about it. So, maybe there's something here about stigma or privacy, issues around health, my status. I don't know. I don't know if these will be important. I don't know if this will be an issue in the other 19 interviews that we did. You just want to play around and see what's happening here.

Dr. Rachel Ceasar:
And we'll do a couple more and then we'll move on. So, you've mentioned AIDS a lot of times. How has it impacted this area? It is a big impact and it is treatable. We knew that four or five people that took the treatment and they were the best workers ever.

Dr. Rachel Ceasar:
They had energy. They wanted to work. They were motivated because they got help. They've got the ability to come forward and say, "Please help me go to the health clinic every month." They didn't tell me they've got AIDS, they just told me I want to go there every month for treatment.

Dr. Rachel Ceasar:
I didn't ask what treatment, I just know. So, there's questions and maybe trust. Again, the privacy issues around health. There seems to be some relationship between the employer and the farm worker of it's intertwines of their health, being healthy means you can work.

Dr. Rachel Ceasar:
So, maybe there's something there. Being healthy means can be productive on farm. So, these are just examples of what that might look like. Let's go back to here. And again, really simple. And some people say, "Well, I did do that. I do the same process.

Dr. Rachel Ceasar:
And what's a little bit different about grounded theory is that you stop and you write it down. And people tell me like, "Well, we had a meeting and we did this." But the one thing that's nice about grounded theory especially with COVID, and we're remote, or we're bringing in extra people to help is that it's trackable.

Dr. Rachel Ceasar:
So, from the moment you get the interview to all the way from a theoretical statement, every thought and every interaction with the data is tracked and written down. And it takes a little bit extra time. But you'll see once we get to the analysis part, it's all there for you.

Dr. Rachel Ceasar:
So, it's important to track everything and that these aren't interpretations, but that they are systematically little by little we're getting closer to understanding what's happening in the data. So, go ahead in the chat if you're able to follow along. What brain farts did you come up with? What are you reading here? What are you seeing? What are the contexts you feel or reading between the lines of what's happening here?

Ben Wiedmaier:
Hey, Rachel, this is Ben. I would just want to surface a few questions that came up when you went through that exercise there. Corinne asks or pardon me, let's see. Oh, both Yale and someone in the chat asked about having partners to code take notes. How do you typically manage the operationalization of the doing, of the note taking or the transcribing? Is it just you?

Dr. Rachel Ceasar:
So, I always hire some. I always pay a third party to transcribe it. Same Day Transcripts is supposed to be really good. I used to use Rev, but then I got feedback in a session that those people are really underpaid. They do a great job and it's a 24-hour turnaround service.

Dr. Rachel Ceasar:
But yeah. I record it on my recorder. I record it on my phone and then I'll have a second colleague recorded as well or have it recorded on Zoom, and then quick time in the background like every researcher's greatest fear is that you won't record it.

Dr. Rachel Ceasar:
And then, I don't take too many notes because I'm very want to... if I'm the main person asking the questions, my goal is to listen and provide, have it be a conversation. So, I actually memorize my research questions ahead of time. And if there is in the budget and I always recommend it to have a junior colleague or a second researcher to take notes, then I always advocate for that.

Dr. Rachel Ceasar:
To have someone who's actually taking notes versus one person really just listening very carefully on the interview, making sure all the questions are being asked, but also listening and being able to follow up.

Ben Wiedmaier:
That's great. We have a question from let's see, I believe it's Sarah or Melody, Melody about participant privacy. Do you assign pseudonyms? Do you ask participants if they have a particular name? They would like to go by? How do you manage the confidentiality and anonymity especially when you're working with a third-party transcriber, potentially.

Dr. Rachel Ceasar:
Yeah. So, in the past, because a lot of the work I do also needs to be like HIPAA and a lot healthcare work, so I have to be careful. So, there's a way. One, use a service that is HIPAA or if you need to have it at that level, so like Same Day Transcripts does that.

Dr. Rachel Ceasar:
And they came out of healthcare, actually. You make sure that they can't download it. Or, if they download it, it's only able to exist for a short period. Like it doesn't live on the transcriptions person's desktop, that's one way to do it. Like using box or something like that where it's like a cloud where it exists there and you can determine where, who gets to share it.

Dr. Rachel Ceasar:
Same Day Transcripts, I'll put that in there, Same Day Transcripts. And they came out of healthcare, so they're very aware of how to do this. And so, I don't really leave that up to participants, I leave it up to me. So, I usually have a coding system. I like to know the order I did the interviews in because that helps me to remember who's who. And also, it's good. As you're learning, maybe the first couple of interviews weren't so great or you didn't do a good job.

Dr. Rachel Ceasar:
So, it's helpful to know who came first in that order. So, I'll put 01 for the first interview. And then, if they came from farm A, then all those transcripts will get farm A. If everyone was from another area or zone, I'll put B, so be like B01. You can add a couple characteristics to it.

Dr. Rachel Ceasar:
But I usually create the names of the transcripts and then somewhere in a locked cabinet printed out or locked up in box, you can have the code of who is who, but that should be locked up, the official real names connected to the fake names.

Ben Wiedmaier:
And last one, Rachel, this is great. I know we have a lot to get to. And that reminds me of my days as a graduate student with the institutional review boards asking who has access to the filing cabinet with my survey data. Both Tulsi and Michelle are asking about the difference between thematic coding and grounded theory.

Ben Wiedmaier:
Is thematic coding a way of doing "grounded theory?" Is grounded theory the approach of leveraging semantic coding. Could you tease those out a bit more, please?

Dr. Rachel Ceasar:
Yeah. So, thematic and I don't have in this version. There was an article that gives a nice visual of it. So, thematic analysis, it's really similar and it's so subtle, but it's really like the goal is to come up with themes and patterns. And grounded theory, it's less about let's stick with those themes and patterns, but let's create statements of what needs to happen next.

Dr. Rachel Ceasar:
Like the goal is not themes even though themes come up, but it takes it the next step. And hopefully, we'll have time to go through what memoing is. So, we'll have coding and memoing and it's two different ways to help us get to those theoretical statements.

Dr. Rachel Ceasar:
And that's what I've seen as the difference between grounded theory and thematic analysis. And depending on what projects, maybe you're doing something that's more discovery research and themes are fine, that's all you have.

Dr. Rachel Ceasar:
Maybe you're not in a position or the interviews weren't quite there to go to the next step of creating theoretical statements or saying like, "This is what's happening in the data." So, it depends what research you're doing and what are the questions you're trying to ask.

Dr. Rachel Ceasar:
So, let's talk a little bit next about the codebook. And this will help a lot. I love the codebook thing. Again, takes forever to do, but it's, again, a lot of the work upfront that will help you make analysis very easy once you go through all the data.

Dr. Rachel Ceasar:
So, the codebook and what is grounded theory coding? I think there was a question earlier of just like, well, what is coding? What does that mean versus coding with computer code and coding for qualitative analysis? So, grounded theory coding from Charmaz, it's about reading very closely and like a highlighter.

Dr. Rachel Ceasar:
Highlighting and tracking and making sure you tag statements, actions and events. And this is to help move into not from describing and descriptions which we're always trying to do here to move into analysis. So, when you're coding, what you're trying to do is like ask, ask between you and your interaction with the data, what is this data a study of?

Dr. Rachel Ceasar:
From whose point of view? When, how and with what consequences. And so, here's a really nice example from Charmaz, and this article is in the resources in the Google Docs. But one of the things she says to do is to use gerunds, I'm not sure if I'm pronouncing that right.

Dr. Rachel Ceasar:
But basically, INGs. So, you're trying to look not for just blanket, what is happening, who, what, where, when, why, and how, but what are the processes, what meanings are happening here. So, for example, these are the exact same interviews, the transcripts.

Dr. Rachel Ceasar:
And then here, she has codes, friend's support, hospitalization, conflict with a doctor. But then, if you train your mind to start developing codes to highlight in the in the transcripts, things you want to track and follow, you start training yourself to use INGs.

Dr. Rachel Ceasar:
I mean, the goal here is not to have more codes, but to really focus in on these in between actions or processes. So, instead of just friend's support, now we're having receiving friends help and seeking care, it becomes a little bit more specific.

Dr. Rachel Ceasar:
And hospitalization we have gaining medical access. Being a medic admitted to the hospital. So, you can see how already just the term hospitalization and then these two experiences or processes that a patient has to go through, this is extremely important.

Dr. Rachel Ceasar:
And this will be extremely important down the line if you're developing services or a product to have this into two different experiences here. Conflict with doctor becomes getting a bad doctor, taking action against the MD. Again, these mean exactly the same thing.

Dr. Rachel Ceasar:
And the goal is not to have a million codes, but you're capturing different experiences. And this is really important when you want to really have things in the person's perspective. So, my rule of thumb here and I think someone asked this earlier as well, line by line coding.

Dr. Rachel Ceasar:
You want to code in chunks. You don't want to code five paragraphs. And again, this goes back to your transcription services. A lot of times with AI, you're not getting natural pauses. So again, you're having to do that work as you're going through analysis.

Dr. Rachel Ceasar:
The goal I always want to pay extra with transcripts, so I have a nice clean transcript when it gets to me for analysis. So, you don't want to cut up sentences either, you want to have a nice chunk, you want it to be a paragraph size. You don't want to have to go back into the data later and say, "Oh, what happened before after that was said?"

Dr. Rachel Ceasar:
So, there's a nice balance there of having natural pauses in the text. My rule of thumb is to have 40 codes max. Because again, you're memorizing it. You don't want to have to constantly be looking down at your codebook to see, okay, you're looking down at your codebook and you're looking in the transcript that's about 20, 30 pages, you want to be able to memorize it.

Dr. Rachel Ceasar:
You also don't want to have too many codes because then you might be cutting it too close, maybe it's too specific. You don't want to have too few codes because then it might be too broad. One thing I like to do is have what's called a good quote code. And that's a way to tag or follow everything that I feel like is a really nice quote and maybe I want to use that later.

Dr. Rachel Ceasar:
And that just helps to also understand what are codes. Codes, you can imagine if we were doing this paper wise is if we had 40 different highlighters on the floor and we had all our transcripts printed out, each highlighter is essentially something I want to track, something I want to highlight in the text.

Dr. Rachel Ceasar:
So, let me show you an example of a completed codebook. This is for a study on vaping. So, the format I use, I have the code name and then I have the definition. And then, I have straight examples from the data. So, let me show you here. So, this is a code I like to have a lot called Origin Story.

Dr. Rachel Ceasar:
So, these aren't INGs, but it's like what's the first time I've used vaping? Or, here's my good quote every time that happens. And then, I've got my INGs. Vaping versus smoking, comparing vaping and smoking cigarettes. And then, I have the exact definition.

Dr. Rachel Ceasar:
And as I go through the transcripts, this become tighter and tighter, closer and closer, better and better define. So, vaping versus smoking, comparing vaping and smoking cigarettes. And then, I have like I like the buzz of the cigarette more than the buzz of the vape, different way.

Dr. Rachel Ceasar:
I just keep having closer and closer example. So, my team and I, we know when we're coding for vaping versus smoking, everybody knows what we're talking about. And so, I wanted to show what a completed one looks like. So, we don't have too much time, but I'll maybe just... this is what that activity would look like in the three-hour workshop version.

Dr. Rachel Ceasar:
Now, that we've gone through and just did a little brain farting of what is happening in our interviews as soon as we get them back, we go through that first transcript and we start seeing what are some codes. What are some important things in transcript one and two that's coming across all the 20 transcripts?

Dr. Rachel Ceasar:
Are there's some codes, are there's some things I want to highlight and follow through? So, I'll show you what I came up with just because of time. But I just came up from the two transcripts that are in the resource guide, here is what I came up with.

Dr. Rachel Ceasar:
So, I have a good quote code as usual. I have demonstrating productivity. So, if you remember from transcript one, we were having some questions about loss of productivity, privacy issues around health and my HIV status, stigma, trust. And so, when I went through the rest of the two transcripts and run through them, there's something here that I want to track across the two transcripts about productivity.

Dr. Rachel Ceasar:
If you go through the two transcripts, having a lunch break is for worker's health, that's worker's time. The getting sick cost the farm. So, there's something about productivity and I want to follow that across the transcripts. Maybe there's something interesting there.

Dr. Rachel Ceasar:
The other code that comes up, preventing health issues. Preventative programs or measures to health care issues. So, there was a discussion in the transcripts about winter time prevention of the flu. This is one that came up when we were going through the transcript, experiencing stigma, feeling of shame or disgrace, a threat to one's reputation.

Dr. Rachel Ceasar:
So, in the example in the transcript, there's a conflict of if the employer has knowledge of a worker's HIV status. So, these are some of the codes we can come up with from the two transcripts I provided of these are things I want to track and follow.

Dr. Rachel Ceasar:
These are quotes that maybe will have some weight at the end to what I want to do with the data in terms of services or products. So, that's the next step. We don't have too much time, but I like to use the codebook. Again, this is a place to delegate.

Dr. Rachel Ceasar:
The codebook is a nice document. The goal is to have a standardized code. So, what I try to do is, you get one to two transcripts between you and your team, even you and your boss or client, maybe they don't have time. But the idea is, you take one or two transcripts, they can be at random and you create these codes, you do a very close read of one to two transcripts.

Dr. Rachel Ceasar:
And then, maybe that's enough. Maybe you're happy with your codes there. Maybe you need to do a third... my codebook still hold up with this third transcript that I'm choosing at random. And then, the goal is to have a standardized guide for the codebook to shut it down.

Dr. Rachel Ceasar:
Because you don't want to be reading five of the 20 transcripts and then say, "Hey, let's add 10 more codes." Then you have to go back and apply. You have to have the same codes applied to all 20 transcripts, to everyone you talk to. So, the idea is, it's not five transcripts or 10 transcripts, but you're trying to get a real look or overview of all 20 people you talk to for this particular study.

Dr. Rachel Ceasar:
The codebook is also a nice way to have everyone on the same page. It's a document where you can say, "Hey, these are the things we're hearing in the text in the transcripts. These are what's we're seeing is important to the 20 participants."

Dr. Rachel Ceasar:
Is there anything else we want to add? And this is a great way for the client to say, "Hey, I want to know every time they've talked about costs." Or, "I want to follow or track every time someone said this was taking a lot of time." So, there's different things you can track in there and it's a way to get everyone on the same page.

Dr. Rachel Ceasar:
And it's also a way to cover yourself to say, "Hey, does everyone agree with the codes?" And then, close the codebook down, that's the goal, is for this to be a standardized guide, leave it open, have everyone contribute, and then close it down.

Dr. Rachel Ceasar:
So now, we'll go into a little bit into the coding of the data and what that looks like. I won't have too much time to get into. Again, we don't want this to be a discussion about what are the technical aspects of what it looks like in different software, because every software is a little bit different.

Dr. Rachel Ceasar:
But just more of what's the theory and practice around coding? So, here's our codebook, we came up with about five to seven codes. And even though we're moving toward the software, it's still an interaction between you and the data. The software is just really to help you instead of you having to print it out across your apartment or home and then have 40 highlighters.

Dr. Rachel Ceasar:
I tried to stress so much, don't let this software intimidate you because it's really chunky and it crashes all the time. And people are constantly creating new software. But just imagine it's between you and your team and really getting close to the user. It's trying to understand what what's going on in the transcript.

Dr. Rachel Ceasar:
And this process, this method really forces you to stay close to the data. That's why I also like working with the software because you see the transcript and you see your coding, or you see your memos, or you see your brain farts, everything is together and attached to the actual quotes in the transcripts.

Dr. Rachel Ceasar:
So, you're never moving away. The goal really is to preserve what the people are saying in their transcripts and for you not to not to move away from that. To very step by step stay and be able to be an advocate for what's being said in the transcripts.

Dr. Rachel Ceasar:
So, just to recap where we are in the process. We did our interviews. We got them back. We did some initial memoing or brain farts just to like what is happening in this data as you're getting the interviews back. So, it's helpful. I forgot to add that it's not just helpful to start thinking about what's happening in the transcripts, it's also a good way to check in with yourself, "Am I doing the interviews correctly?"

Dr. Rachel Ceasar:
Are there things I could be doing better in transcript one that I need to be doing in the second interview? The next thing is you create a codebook. You get one or two transcripts of the 20. You figure out some codes that come out of it after reading through all 20 of them.

Dr. Rachel Ceasar:
You do a close read of one to two and you see if the codebook holds up to that. Then we go into the software and we start coding and then the next step from there is memoing. Also, where can you delegate here? Where can you get your team members involve coding?

Dr. Rachel Ceasar:
And even if you don't have a lot of research experience, talking out the codebook, having that interaction between you and your boss or client, or other team members, you don't need to have a huge research background. You do need to know the data well.

Dr. Rachel Ceasar:
And that can be helpful to delegate or help other colleagues to take on those responsibilities to maybe they... of the 20 transcripts, you read five or 10. And they read or code the other 10. So, that's a way to help spread the workload because it does take time.

Dr. Rachel Ceasar:
For the senior or for the main researcher, it's helpful for you to be involved so you can create the codebook. And also, for memoing which we'll get to in a little bit. You're really the one who needs to come up with in conversation with your client as well as other members of the team of coming up with the theoretical statements.

Dr. Rachel Ceasar:
We don't have too much time. So, I apologize if I zoom through this a bit. I definitely want to see we have time for questions at the end. So, I'll go through a couple of these of what coding looks like. And then, I'll look to the attendees and Ben for questions.

Dr. Rachel Ceasar:
So, this is what it looks like. Yeah, all your transcripts, this is ATLAS.ti. You got all your transcripts here. You got all your codes here. I like to color code them. And then, you can see here is the chunks of text that I put together, that I'm reading together.

Dr. Rachel Ceasar:
And then, you add as many codes as possible. And this is not to overload it and be not thoughtful about it. But the idea is, you don't ever want to have to go back into the raw data. You're moving slowly away from raw data to data that raw data has been thought through or analyzed, or coding.

Dr. Rachel Ceasar:
So, it's easy to add more codes then later going back into the transcript and seeing, "Oh, where's that one quote where I talked about trust or stigma with the with the participant. And so, adding more, you're just stacking codes and adding codes as you go along.

Dr. Rachel Ceasar:
This is more specific to the two transcripts that we're working through. So, you can see creating cost, good quote and then memos. As you're coding, you want to start thinking, now that you're organizing and approaching the data with these five to seven or less than 40 codes, you're coding, but a little shortcut to help you get to theoretical statements beyond coding.

Dr. Rachel Ceasar:
Now, that you're in there and really reading very closely the transcript is memoing. So, what is memoing? So, here, you'll see my example. I've got all my codes. We've had codes about productivity, demonstrating productivity, creating costs, building trust.

Dr. Rachel Ceasar:
And then, my mental here is there's something about productivity as being a health barrier, or there's evidence about... so let me tell, let's walk back a bit, what is a memo? And so, many teams do not do this. And they said, "Well, we did it in meetings or we talked about it."

Dr. Rachel Ceasar:
It's so important to do this step because you're never going to be that deep into the data again. And while you're in there and just closely coding, it can take two hours, the first transcript. And then, as you get better and better and memorize the codes and know what you're looking for, to highlight in the transcripts, it can take 30 minutes for one-hour interview, or 25, depending how fast you're going.

Dr. Rachel Ceasar:
Memoing, while you're in there is that's not the brain farts from before because now you these codes, you know how you're reading the data, you're really in the thick of it. Memos are concepts. They're a higher level of a code. They're really providing a record of your analytical process.

Dr. Rachel Ceasar:
So, for example, in our two transcripts on farmworker's health might be productivity equals health barrier. There's something there. There's something I want to track. And this is again, a place if you're divvying up the coding and the memoing, this is something that you can have junior colleagues do as well to help split up the work.

Dr. Rachel Ceasar:
And this is what it looks like here where you go in the software. Well, so, what's the difference between codes and memoing? What was the point of doing codes if I now have to go in and memo? So, memos are like codes and they're generated from the data, but they can also be tentative theories.

Dr. Rachel Ceasar:
So, you're coding the data, but maybe there's a memo that might be a potential academic paper in the future. Or maybe there's a memo that it sounds like a key opportunity area that aligns very nice. The idea is that you're actually the memo or this idea is linked to the transcript itself.

Dr. Rachel Ceasar:
So, it's not a post note on the wall that you have an idea, it's completely hyperlinked and linked in the software to where it's happening in the transcript. And again, it helps you stay close to what is being actually said and experienced in the transcript.

Dr. Rachel Ceasar:
And so, we don't have too much time to actually go through it. So, let me show you what it looks like when it's done. This is like the last part in the analysis where there's not much more to be done because now you have your coding. And now, you have your memos.

Dr. Rachel Ceasar:
So, the goal here just to backtrack a bit is to go from raw data to memoing to categories of memos. And really get toward that more conceptual level of what is happening across all 20 transcripts. So, let me give an example. This is in Nvivo, I heard someone really loved Nvivo in the chat box.

Dr. Rachel Ceasar:
So, this is example from the air taxi where the goal here is to have a product, an air taxi that everybody loves and is fabulous. And so, you can see here, this is across 60 transcripts. It's a little hard with our example of just two transcripts.

Dr. Rachel Ceasar:
But as I was coding, feeling safe, bouncing time and costs, I kept seeing and my team, we kept seeing there's something about not knowing who's using it. I want to be safe, but everyone needs to feel safe. Who is this service for? It shouldn't be a luxury service. It should be for everybody.

Dr. Rachel Ceasar:
And so, I went back in there and I started seeing, there seems to be a pattern across all 60 transcripts, there seems to be something about access. So, I actually went back into the memos and then started creating these categories. There seems to be something about access and it showed up numerous times across all 60 transcripts, being savvy end up coming up a lot.

Dr. Rachel Ceasar:
And so, you can see the codes help me organize how I want to read, me and my team, how we all should be reading the transcripts. And then, the memos is all of us working together to see what keeps popping up across all 20 or 60 transcripts. And so, then you start sorting, comparing of the memos you've created.

Dr. Rachel Ceasar:
At the very end, when you're done coding and memoing, you start organizing them, what keeps popping up? Some of them, there was experienced treat, that didn't go anywhere. Client incentives, it's there, maybe we're not going to do anything with it.

Dr. Rachel Ceasar:
And so, that's the goal. So, let's go back to our example with the farm employers. This is what it looked like at the end. What can we say about the data? And everything is we're looking to the transcripts. We're looking to see how we can back up our statements, the ideas, it's not my interpretation, it's our team.

Dr. Rachel Ceasar:
We've created a standardized codebook with codes where we're looking at, for example, demonstrating productivity. We memo, we had about two instances in the text there where there's something about productivity and health. And then, our theoretical statement is healthcare access is tied to productivity, health time is directly equated into farmer's time and costs.

Dr. Rachel Ceasar:
And this is what I presented back to the client. So, I'm going to take a little break there because it's 11:00 and we're about to run out of time. I want to go ahead and look to Ben and maybe some of the questions here of what people are thinking or feeling here.

Ben Wiedmaier:
Yeah. First of all, thank you so much, Rachel. This has been really, really helpful. I know that asking you to do something that arguably should be done in like a three to six-hour workshop and a crash course of about an hour and 15, we really can't thank you enough. A lot of the questions from folks are about some of the operationalization.

Ben Wiedmaier:
How do you advocate for this method? What's a good sample size? Where do you keep them? So, I don't know if there's any, maybe you can distill it into if there's somebody out there who hasn't leveraged grounded theory, who has always wanted to, or is trying to advocate for this method? How might they begin to do something like that, a way that they could do it right away? Oh, Rachel, you are muted.

Dr. Rachel Ceasar:
Oh, sorry.

Ben Wiedmaier:
There you go.

Dr. Rachel Ceasar:
So, how do you advocate for this? And I get this question a lot. I mean, one thing that I try to tell clients why I need extra time to do this, I'm going to turn my video off for a second, because it looks like I'm being a little bit slow, my internet.

Dr. Rachel Ceasar:
But one thing I tried to let people know is that this takes a lot of time. This is literally 100 or whatever how many pages it is and there needs to be a way, it's a lot of data. And I find that it takes a while but it can really get very good results. You really preserve what the users are saying.

Dr. Rachel Ceasar:
And if people are serious about doing user data or doing the interviews and really trying to stay close and listen to them, this really forces you and your team to listen to it. But there's no way around it, you have to read and reread what's happening in the transcripts. And then, there's ways to split that up. There's ways to get junior members involved.

Dr. Rachel Ceasar:
I've had people who had never coded or have ever done research to help me read the interviews and that can be really helpful to have. Maybe an engineer from the team to read some of them or a developer and say, "Well, what's happening here?" Or, get them on board to have them part of the conversation as you're developing the codebook.

Dr. Rachel Ceasar:
What's important to people here. And so, that's different ways to advocate for it, is to really say like, "This is the way we need to spend time and this is a larger conversation I think that's happening in UX is how can we do justice to the people we interviewed?

Ben Wiedmaier:
Right? Yes. People Nerds had a panel a few weeks ago about not doing extractive research within communities of color or underserved communities more broadly. And these folks who are working in the public sector were very, very clear to incentivize and to ensure that there is more of an exchange of ideas and information between participants broadly defined and researchers, so that it isn't, again, just let me take from you your knowledge.

Ben Wiedmaier:
I guess one other question, Rachel, before we let you go. What would be a good number to get going? You're standing in front of stakeholders who are like, "Okay, you want to do some interviews." And again, these folks might not themselves fully understand what grounded theory is.

Ben Wiedmaier:
Do you have a number? Is it think about the population you're trying to say something about? What advice do you have on someone, again, who's trying to advocate for this in terms of sample size or number of participants or those sorts of your questions?

Dr. Rachel Ceasar:
Yeah. And I think that goes back to what is your research question? Are you just trying to get a lay of the land of what's happening in this field? Or, do you really want to know specifics of how this service needs to be better or this product needs to be better?

Dr. Rachel Ceasar:
So, it depends on your research question is. But I always say, if you could even talk to five people for 30... any user research is better than none, to create a product or service for people without any of their input can be seen as unethical for some folks who are researchers or arrogant to create things for people that maybe nobody wants.

Dr. Rachel Ceasar:
And I think some of the value of user research is that you might save your company time and resources from developing things that nobody wants or needs. The pushback is always like, well, people don't really know what they want, so we would never get these products or services in the first place.

Dr. Rachel Ceasar:
But I feel like user researcher is always happening there someplace. You're not just getting ideas from nowhere. You're always generating them from your interactions with people and having a more formal process to do that and really engage with people.

Dr. Rachel Ceasar:
Even if it's just getting out of the lab and talking to a couple people and just getting some insights on what works and what doesn't is extremely helpful to just take on other people's mindsets and see how they're experiencing the world and the difficulties or things that facilitate that process.

Ben Wiedmaier:
Certainly. Yeah. So many of other People Nerds folks have said that in advocating for user research as an approach to business, they're advocating for empathy, for the humans who make the business possible. And then, methodologically that often aligns with more open ended, unstructured, qualitative, investigatory type methods as opposed to the more traditional consumer or market insights like, let's do a survey, or let's consult the secondary research.

Ben Wiedmaier:
I think that the beauty of being in the human centered user experience world is that qualitative and mixed method is broadly so well aligned with the charter of empathy. So, Ben, from People Nerds, if you're out there trying to advocate for these sorts of methods, go back to that empathy.

Ben Wiedmaier:
Good businesses being empathic, being comprehensive and being nimble. And if you're relying on business decisions from a survey you ran in 2019, then you might not be making the smartest, sharpest or sound decisions. Rachel, thank you so very much.

Ben Wiedmaier:
It was an absolute pleasure to get to spend just a little bit of time with you. Folks, Dr. Rachel Ceasar is on LinkedIn. If you want to holler at her, Rachel, are there other ways that folks can get in contact with you if they have other questions or follow ups? Other ways that you'd like for them to get in contact?

Dr. Rachel Ceasar:
Yes. I can put my email here. I was also curious. There was a lot of great questions that I couldn't get [inaudible 01:03:07]. I noticed [inaudible 01:03:09] Q&A and some of the chat questions be saved?

Ben Wiedmaier:
Yes. And you know what, why don't you and I talk, Rachel. We could maybe figure out a time for us to grab 15 or 20 minutes to go into the Slack channel, the People Nerd slack community. And we can just invite folks who registered and maybe we can just, again, have a little conversation with folks, again, at your availability and convenience, and we can get some folks together, that would be great.

Ben Wiedmaier:
Yeah, there are some really good questions more about what do you turn the codes into? What is the deliverable look like? I think these are really great questions that I would love to have conversations around. So, for those of you who are still on the call the 135 of you, just stay tuned in your inbox, we'll have some next steps where we can pick Rachel's brain and her brilliance for some more.

Ben Wiedmaier:
Thank you so much again, Rachel. Stay safe out there in LA. For those of you still on the call, stay safe wherever you are. Thank you again. We'll hope to see you again on another People Nerds webinar workshop. Be well, stay well. Take care.

Dr. Rachel Ceasar:
Thanks, Ben. Thanks, everyone.

Ben Wiedmaier:
Got it, Rachel. Take care. Bye-bye, everyone.

Dr. Rachel Ceasar:
Bye.