Tiffany Monique Quash, PhD: And welcome back everyone to another episode of color outside the Memos. I'm Dr. Tiffany. Lizzy Bartelt (she/her): And I'm Dr. Lizzie. Tiffany Monique Quash, PhD: And welcome welcome before we get started. Don't forget to email us at Cotm, as in marypod@gmail.com. You can also check out our website@www.cotmpod, dot com where we'll post our updates and new episodes. So, Dr. Lizzie. Tiffany Monique Quash, PhD: what are we getting into today? Lizzy Bartelt (she/her): We're talking about the wonderful world of coding. Tiffany Monique Quash, PhD: I am excited about this, so I don't even know like, actually, let me go back. Let me take a step back. Tiffany Monique Quash, PhD: We're talking about what is coding? Tiffany Monique Quash, PhD: I would like to read what Braun and Clark say in their thematic Analysis book, because we love their book. It's called thematic analysis. A Practical Guide by Virginia Braun and Victoria Clark. Tiffany Monique Quash, PhD: and. Lizzy Bartelt (she/her): We have so many stickies and highlights in our books. Tiffany Monique Quash, PhD: We just love putting stickies everywhere on books. Tiffany Monique Quash, PhD: Honestly, it's part of our coloring outside the Memos. Tiffany Monique Quash, PhD: Yes, definitely, definitely. So they say. The process of coding involves systematically working through each data item and your entire data set. Tiffany Monique Quash, PhD: So this is the next part that I think is really interesting where they say, so where do I start? Or where do you start? Take your 1st data, item, start reading and stop when you think you've spotted something relevant to addressing your research question. Tiffany Monique Quash, PhD: Even if it's only potentially relevant each time you spot something interesting or potentially relevant, tag it with your code label. Each time you encounter some tax, you want to code. Consider whether an existing code applies or you need help to develop a new code Tiffany Monique Quash, PhD: to me that makes a lot of sense. What do you think, Dr. Lizzie. Lizzy Bartelt (she/her): It does. It does, I mean, and it partly does to us, because we've been doing this for a hot minute. And we've been talking about qualitative research for Lizzy Bartelt (she/her): longer and we love it. But for those of you who maybe are new to it. As you're starting to read data, there will be something that just is like, Oh, and sometimes, even when you're doing interviews honestly, you and I are both the same on this that we'll be like Lizzy Bartelt (she/her): this was the moment I wanna I wanna get into this data right now, right now, right? And even when we've done like Lizzy Bartelt (she/her): open text box surveys the very 1st minute that the 1st survey comes in. You're looking at that data. And you're like, what code Lizzy Bartelt (she/her): code code easy for us. I. Tiffany Monique Quash, PhD: Oh, no sorry go ahead! Lizzy Bartelt (she/her): Good. Tiffany Monique Quash, PhD: Yeah, you're good. Lizzy Bartelt (she/her): Okay? Lizzy Bartelt (she/her): it. So it's so easy once you've gotten into it, and you've done it a couple of times to understand it. But when you're 1st getting started. I think it is like, I remember my very 1st qualitative project, and Lizzy Bartelt (she/her): all I got was, this is sort of about bisexuality, and I was like, cool. Lizzy Bartelt (she/her): okay? And it was from a culture that I didn't belong to. It was from a country that I wasn't a citizen of. It was these interviews that I was like, I don't know anything about this I don't know. And like I kept asking questions about what's the research question. Blah blah blah, and they're like just called things that are interesting or kind of like the codes that are on the side. And I'm like. Lizzy Bartelt (she/her): what is this. Does this mean right? And then, as I started reading through the 1st interview, I was like, Oh, this is interesting. Lizzy Bartelt (she/her): Oh, that's not what I would expect this is kind of a fun little tidbit. Oh, this is a really interesting story, and you just start getting it. And you just start grabbing those codes and putting them in and like. Lizzy Bartelt (she/her): it's not as hard as you think it is. It's really kind of basic common sense as you're going through it. Tiffany Monique Quash, PhD: Yeah, I think the one thing that I got out of reading Tiffany Monique Quash, PhD: the Coding Manual for Qualitative Researchers, by Johnny Saldana. And again, I, Dr. Saldania, if you're listening to this, just know that in my heart I call you Uncle Johnny. Tiffany Monique Quash, PhD: She does, though all the time Tiffany Monique Quash, PhD: I do. I do so. Uncle Tiffany Monique Quash, PhD: Johnny says, how many codes should I like? We're asking like, how many codes should I have? And he says the actual number of codes depends on the nature of your data, which coding method you use for your analysis and how detailed you want to be, which is exactly what you're saying. Like, you know, when we go into when we're there with our raw data. Tiffany Monique Quash, PhD: And again, some people do this. Some people don't. They have those preliminary codes. Tiffany Monique Quash, PhD: and then they go into well, this goes. This code can also mean this. And then you go into that final round of coding and be like, okay, this is the overarching code of what this all means. And I thought that was really interesting, like reading that from Uncle Johnny's text, and that was on page 33 in his text. And so, yeah, I, Tiffany Monique Quash, PhD: yeah, it just Tiffany Monique Quash, PhD: I don't even know where to keep going with this thought here, you know, I mean, I think coding is a lot of fun. Tiffany Monique Quash, PhD: It can be Tiffany Monique Quash, PhD: overwhelming. Yeah, it can be difficult and overwhelming sometimes. But I think going back to what Bron and Clark said. Just take your time Tiffany Monique Quash, PhD: pretty much in in dealing with the raw data. Tiffany Monique Quash, PhD: So yeah. Lizzy Bartelt (she/her): Well, and I think we talked about this a little bit with transcriptions, too, and like how some people really love transcribing, and how coding is like that, that you're really getting immersed in the data. I kind of think of it. And maybe this is just because I'm talking to you a swimmer. But I kind of think of it like you're jumping all the way into the pool. And you're just being surrounded by this beautiful data, and you're really feeling it. Lizzy Bartelt (she/her): And Lizzy Bartelt (she/her): if we go all the way back to one of our earliest episodes, where we were talking about Bhattacharya's example of a chair, right understanding how people are viewing the world? Are they thinking of a desk chair with wheels? Are they thinking of a wheelchair? Are they thinking of a dining room chair. Are they thinking of? Lizzy Bartelt (she/her): lazy boy recliner? Are they thinking of. Tiffany Monique Quash, PhD: You know of that lazy boy recliner? That's what I. Lizzy Bartelt (she/her): This time in the semester. Me, too. I just wanna be curled up with my book right now and under like 3 blankets with a big cup of tea like that's all I want. Lizzy Bartelt (she/her): anyway. But right? Like. Lizzy Bartelt (she/her): you have to immerse yourself in that data to really understand how people are understanding the world, because otherwise it's really easy to pull out one quote and be like, Oh, they're seeing a chair as a desk chair. But in reality. Maybe that's Lizzy Bartelt (she/her): they're only when they're at work. Are they seeing it as a desk chair? Right? And like they have different identities or codes that they're switching to. And so you really have to be deep into that data to get it. Tiffany Monique Quash, PhD: Yeah, I I you know. And thinking about what you just said, like being able to submerge yourself in the data. I think this is kind of like when people start to use a Qda system, you know. Either they do that or go old school, which I love going old school, but I also enjoy. I've learned. Let me back up and say I learned to enjoy using a program like deduce, even though deduce is not sponsoring us. But. Lizzy Bartelt (she/her): We're open to that discussion to do. Tiffany Monique Quash, PhD: Definitely open to that discussion. Tiffany Monique Quash, PhD: But I mean, it's, you know, when you're using something like that, it's like net. Well, at least for me. Now I feel more comfortable Tiffany Monique Quash, PhD: using a Qda and and going tabletop using the tabletop method. And again, when you're using tabletop, it's just you're sitting there with your raw data, your interviews, or what have you? And you are going to town with your highlighter and pencil or pen, making those notes, or those memos on the sides of the paper, or what have you? Tiffany Monique Quash, PhD: So I think Tiffany Monique Quash, PhD: that's like that other step like, how submerged you want to be into the data. I I also am a slightly, a bit of a control freak. So when you start talking about, thank you for agreeing with me. Tiffany Monique Quash, PhD: So. Lizzy Bartelt (she/her): I mean. But, researcher, it kind of goes with the territory. Tiffany Monique Quash, PhD: It does, it does, it does. And so when you're thinking about that, I think the other part is, how big is your research team. So, Dr. Lizzie, I have to ask you, like, how many, how big has your research team Tiffany Monique Quash, PhD: have they been like in over the years. Lizzy Bartelt (she/her): It would say, the biggest one that was actively working on data was probably 5 people. But realistically it was 3 main people and getting beyond that feels almost untenable to me. Lizzy Bartelt (she/her): And like the time that it was really, mostly Lizzy Bartelt (she/her): technically, it was 5 people. It was like 3, 2 of us that were really on coding, and then 3 people that were really touching the quant data. And it was a mixed method study. Lizzy Bartelt (she/her): And I didn't have anything to do with the quant data on that specific study. I was really like leading the coding part of it, and the person who I was working with on coding had never really coded before. And so I was teaching them how to code and teaching them how to do qualitative work, and that was Lizzy Bartelt (she/her): such a labor of love to do that. Lizzy Bartelt (she/her): and that's not to say, for all of you who are brand new to qualitative coding or qualitative work. Not to discourage you from doing that because it's a joy to do. But it is. It's a lot to teach somebody who's never taken a course, who's never listened to this. Podcast you already are ahead of the game. If you're listening to us, Lizzy Bartelt (she/her): let us know. Tiffany Monique Quash, PhD: Definitely ahead of the game definitely ahead of the game. Lizzy Bartelt (she/her): Right, you know. Tiffany Monique Quash, PhD: I think the other part to it is, and Tiffany Monique Quash, PhD: I'm I'm looking it up now. Tiffany Monique Quash, PhD: Inter-rater reliability. Tiffany Monique Quash, PhD: And. Lizzy Bartelt (she/her): And. Tiffany Monique Quash, PhD: I'm going to go back. Tiffany Monique Quash, PhD: So on pages 53, and 54, and Salad's book says, team members can code both their own and others data, others, data Tiffany Monique Quash, PhD: gathered in the field to cast a wider analytic net and provide a quote, crowdsourcing reality, check unquote for each other, for these Tiffany Monique Quash, PhD: types of collaborative Tiffany Monique Quash, PhD: ventures, intercoder agreement, intercoder or inter-rater reliability or inter interpretive coverages. I can't even pronounce this word the percentage at which at least 2 different coders agree and remain consistent with their assignment of particular codes to particular data is an important part of the process. So I did so in that whole. Tiffany Monique Quash, PhD: What I just read is pretty much making sure that everybody is on the same page when you're coding. I think there's a little bit of wiggle room when you're when you've got those initial codes, those pre codes. But then, when you come back together as a group, it's like, you know what, not only finding these, but these are our like second round of codes. And then you've got that overarching round of codes, you know. Lizzy Bartelt (she/her): Yeah, yeah, yeah. Lizzy Bartelt (she/her): I so hot. Take Lizzy Bartelt (she/her): I don't love inner radar reliability. Tiffany Monique Quash, PhD: Tell me why. Lizzy Bartelt (she/her): Oh, well, so one, I think it quantifies qualitative work in a way that feels really troubling to me Lizzy Bartelt (she/her): because part of what qualitative work is doing is saying Lizzy Bartelt (she/her): so. For instance, if we're both reading Lizzy Bartelt (she/her): a study where we're both reading interview right? And we're coding it. Lizzy Bartelt (she/her): And my white midwestern brain codes something and picks up on something that somebody is saying as theme Lizzy Bartelt (she/her): your black Southern East Coast brain picks it up as code Y, Lizzy Bartelt (she/her): then, like, okay, actually, that's really interesting. And when we're going through those codes later on, and we're looking at that, and we're saying, Okay, so what's going on with this data. If I see I coded it as X, and you coded it as Y, then actually, this is a really interesting piece, and I want to go back into that and understand what the nuances are there, or to say when I'm reporting that data, actually, this goes into a larger theme of Lizzy Bartelt (she/her): Z, because it encompasses both Code X and Code Y, that are really important, right? And if you're just doing integrator reliability. Lizzy Bartelt (she/her): and you're trying to have this perfect sense of everyone doing the exact same coding. Then you're missing that beauty and nuance, and then are your themes just your codes. And like, is that. Okay? Or is that not? And I don't know. I mean, that's kind of a moral, philosophical way of approaching qualitative work or approaching any research work right, but I think there's beauty in having different codes come up. Tiffany Monique Quash, PhD: Yeah, I agree with you there, you know, I think the question. And I know we've covered this in our in the past. I definitely like us talking like, what's the difference between a code and a theme? Lizzy Bartelt (she/her): Yeah, yeah. Do you like actually want an answer? Tiffany Monique Quash, PhD: Yeah, I think I think. And because I get kind of Tiffany Monique Quash, PhD: twisted in that conversation with people like, Am I talking about themes, or am I talking about? Codes are the same thing, are they not the same thing? You know. What is your take on that. Lizzy Bartelt (she/her): Yeah. So I'm trying to find, like a good example that will work for a lot of people. And I think it's kind of like Lizzy Bartelt (she/her): saying, Book X is like a code is saying, book X is a Sapphic queer romance, and a theme is saying, it's a romance book. Lizzy Bartelt (she/her): Right. So like a code is really really nitty, gritty, specific. And a theme is looking at all of these codes and going well Lizzy Bartelt (she/her): like these Lizzy Bartelt (she/her): 10 codes are like the the data within these 10 codes all come together to make this bigger theme of romance, or whatever it is, right. And Lizzy Bartelt (she/her): so like, I think a theme is bigger and broader. But you can't start with a theme. You have to start with the codes and get into the nitty gritty of all of the data. And then you're looking at all of that data, and you're going instead of the full interviews you're just looking at all of those pockets of codes, and you're going. Oh, these are rising together to form this bigger story of what's. Tiffany Monique Quash, PhD: And the name. Lizzy Bartelt (she/her): And the data. Tiffany Monique Quash, PhD: That makes absolute sense to me. I you know now that I think about my own research, I'm like, did I analyze this correctly, like. Tiffany Monique Quash, PhD: who knows? Who knows? But I think Tiffany Monique Quash, PhD: like now, I feel like I can go back and be like, Okay, well, maybe this is the other approach that I could have taken with this particular study. You know. Tiffany Monique Quash, PhD: This one question that we have up here, and and again Tiffany Monique Quash, PhD: follow listeners, followers. You know we have our our set of questions that we ask each other, and one of them is how often to talk up between coders, and I always feel like Tiffany Monique Quash, PhD: it's important to talk to each other when you're coding Tiffany Monique Quash, PhD: at the same breath. I don't. Tiffany Monique Quash, PhD: I want to hear that person's Tiffany Monique Quash, PhD: take on whatever we're coding, or like the interview or the focus group. Or what have you? Because I don't. I don't want to be an influence Tiffany Monique Quash, PhD: onto what they're finding out. So that's kind of that's where my brain went went to when when thinking about that particular question, what about you. Lizzy Bartelt (she/her): Yeah, I mean, I think so. Right? Like, I think, it's really it depends on the study Lizzy Bartelt (she/her): depends on a lot of different pieces. There have been some projects where I barely talk to the other coders at all. Tiffany Monique Quash, PhD: Because. Lizzy Bartelt (she/her): I'd worked with them before I knew their style. I trusted their work like we were both getting it done regularly, and I didn't have that much conversation. But then we got done, and we spent an entire day sitting at a coffee shop, going back and forth on what we saw from the data. And that was really really helpful, right? And Lizzy Bartelt (she/her): other times where I've talked to people every single week and checked in, and that was really helpful to keep us motivated and on track. And so it kind of depends on the study. It kind of depends on the personality of the coders. It kind of depends on Lizzy Bartelt (she/her): how traumatic the coding is. It kind of depends on what else you have going on. Lizzy Bartelt (she/her): there's a million different reasons for talking to people regularly or not, and like, if somebody is new to it, like, I've had a couple of students who I've taught how to code, and Lizzy Bartelt (she/her): they wanted a lot more of that hands on coding experience, right? And so like it's it's just. Lizzy Bartelt (she/her): There are too many factors for me to have a single answer on that one. Tiffany Monique Quash, PhD: Yeah, that makes sense. I mean, I think. Tiffany Monique Quash, PhD: like, even when we did, the black, queer women. Tiffany Monique Quash, PhD: Higher education study, you know. I think we met like what twice a month. Lizzy Bartelt (she/her): Something like that. Tiffany Monique Quash, PhD: Like that. And there was 3 of us. Well, yeah, 3 of us. And so I think it was really important for for us to meet, but we also had so many Tiffany Monique Quash, PhD: participants. And then, of course, me and my long winded interviews. So I think it. I think that's really really important. Tiffany Monique Quash, PhD: So Tiffany Monique Quash, PhD: in developing a codebook. And this is probably, you know, so that we start to wind down this particular episode when we're developing that codebook. Tiffany Monique Quash, PhD: is it? Tiffany Monique Quash, PhD: Do you want to do that? Want to develop that codebook before you get started or after you get started? You know. Is it inductive or deductive reasoning. You know. I mean, I think that's where my brain is like. I like to start establishing codes, or at least preliminary codes at the beginning. Tiffany Monique Quash, PhD: and then see where I go from there, like where the data text, text takes me from there. Tiffany Monique Quash, PhD: AI is in my brain right now. Lizzy Bartelt (she/her): Yeah, right? Well, in that. Isn't that just the thing? I think that is exactly it. It is. Lizzy Bartelt (she/her): It depends on what your research question is, it depends on what theories you're using. It depends on. Lizzy Bartelt (she/her): Your position. Tiffany Monique Quash, PhD: Now. It could also depend on your positionality too. Lizzy Bartelt (she/her): Right? Right? Absolutely positionality. It can depend on Lizzy Bartelt (she/her): did you do an interview guide? Did you do a semi structured interview? Guide? Did you do a photo? Elicited interviews? Did you do artifact elicited interviews? Did you do Lizzy Bartelt (she/her): like? There's so many different pieces of all of those things that will shape how you move forward and. Tiffany Monique Quash, PhD: Right. Lizzy Bartelt (she/her): One of the things I love about qualitative research is that it allows for that versatility and flexibility. But one of the things that I think, is really annoying about starting in. It is how hard it is to grasp. If A then B if x, then Z right like. Tiffany Monique Quash, PhD: Right. Lizzy Bartelt (she/her): How do you learn all of those flow charts? Because, frankly, no one's ever made them. But maybe we should right. Maybe that would be really helpful to new or emerging researchers. Right? To be able to say, like, Okay, if this, this, this, and this, then this Tiffany Monique Quash, PhD: But the. Lizzy Bartelt (she/her): The problem is that there's always going to be some loophole that we couldn't think of for the flow chart. There's always going to be some. Lizzy Bartelt (she/her): as you like to say, X factor that we just don't know right. Tiffany Monique Quash, PhD: Right. Lizzy Bartelt (she/her): And so like, I think Lizzy Bartelt (she/her): I think it's a little. It's a little bit art in that way, too. Right? Qualitative research is not just a pure science, it is also an art, and it is learning Lizzy Bartelt (she/her): where to go with the flow and how to follow that line, how to follow that coloring, how to follow that Lizzy Bartelt (she/her): vibe. Almost. It's following the vibes of what the research has given you. Tiffany Monique Quash, PhD: I like that. So I like what you just said. There, like qualitative research, is an art. Tiffany Monique Quash, PhD: And I I think you're I. You hit the nail on the head there, you know, like it really is. I mean, you can't just Tiffany Monique Quash, PhD: throwing, not not knocking my quantitative folks. You can't just throw in some numbers and be like, or some, you know, and be like here. This is the final answer, you know, I mean, and I know we were talking earlier about how AI can use come up with themes within a within an interview. But you, as the researcher, still have to do your work, you know, to make sure that you've submerged yourself in the raw data and not be dependent on Tiffany Monique Quash, PhD: even a Qda system, you know, like you. Still, you still need to do the work. Tiffany Monique Quash, PhD: So. Lizzy Bartelt (she/her): Well, in exactly cause we've talked about that before, right of like, I love Qdas. But then I pull all of that coding data out of the Qda. And then I sit there with highlighters and Lizzy Bartelt (she/her): papers all around me, and little post-it notes that I'm scribbling on that look like, you know, a murder board, and no one can make sense of aside from me. And then I'm figuring out how to put the data together. And it's that detective work. It's that investigative work that you're trying to figure out how everything that is Lizzy Bartelt (she/her): so seemingly differential fits together for a bigger story. Tiffany Monique Quash, PhD: Right? Right? Right? Tiffany Monique Quash, PhD: Wow, I feel like we've covered so much today. So for people who are definitely into coding, I mean, this is definitely one of our I want to say one of our shorter episodes, but it's all good, because I feel like coding Tiffany Monique Quash, PhD: is one of those experiences that every qualitative researcher should have going into it, and and to be unafraid. Tiffany Monique Quash, PhD: When it comes to the coding process there, I don't want to say there's a wrong way of doing it. But there's a way that you can Tiffany Monique Quash, PhD: definitely get more out of the process by just taking your time and reading and reading the raw data, experiencing, listening to the raw data. Lizzy Bartelt (she/her): Well, and I think I think this is to one of those times where. Lizzy Bartelt (she/her): like, if you were to describe, step by step every single thing you have to do to like I don't know. Lizzy Bartelt (she/her): Swim the butterfly stroke. Lizzy Bartelt (she/her): It would be like really boring and really daunting right. Lizzy Bartelt (she/her): But some things you just have to get in the middle of and do, and coding is one of those things you just have to Lizzy Bartelt (she/her): jump in and do. Tiffany Monique Quash, PhD: like, just get up on the on the board. And just jump. Lizzy Bartelt (she/her): Yeah, right? Like. Lizzy Bartelt (she/her): because it doesn't matter how many words we give you or how many books you read on it, like you're still not gonna know how to do it until you do it. Tiffany Monique Quash, PhD: Yeah, no, I I totally agree. I totally agree. And and even in the process of it. Tiffany Monique Quash, PhD: you know, you and I can be looking at the data like you said earlier, like. Tiffany Monique Quash, PhD: we have 2 different backgrounds. We can come at this data from different. We are going to come at the data from different lenses. I think that that's something that's really important. And when you have your research team be like, okay, so what did you find? What did you find? Okay? So now let's take the next step and and come up with even Tiffany Monique Quash, PhD: themes that themes codes that will definitely represent Tiffany Monique Quash, PhD: a representative of what you're what you've discovered. I think that's something that's really really important. So Tiffany Monique Quash, PhD: well, Dr. Lizzie, is there any any. Lizzy Bartelt (she/her): Dr. Tiffany. Tiffany Monique Quash, PhD: Any last words. Lizzy Bartelt (she/her): Do it. It's fun. Coding is one of my favorite things in the entire universe. It is so much fun to sit with the data. And yes, it's hard and yes, it's time consuming. But Lizzy Bartelt (she/her): the feeling you get at the end is like spending a day making a bread that you've gently worked with, and you get to smell all these beautiful smells, and you get to like, really feel it. And when you figure out the puzzle of coding at the end of the day. It is just oh, it's such a satisfying feeling. Tiffany Monique Quash, PhD: Oh, yes, now you're making me hungry for bread. Lizzy Bartelt (she/her): Always. Tiffany Monique Quash, PhD: Yes, Carbs, I would have to say the same thing like, just jump, you know, off that diving block and get into it. You know. I mean, definitely read up on how you could. You know code your data. But go with your gut. I think that's where I'm at right now with talking about coding. Go with your gut, you know, when you see something within that raw data like like, huh? That's interesting, you know. Like, don't Tiffany Monique Quash, PhD: you know. Be afraid, be unafraid and unapologetic. That's the word I'm looking for, unapologetic on how you code Tiffany Monique Quash, PhD: right there. Lizzy Bartelt (she/her): I love that I love it so much. Lizzy Bartelt (she/her): Check out your references. Tiffany Monique Quash, PhD: Yeah, definitely. And our references are in the notes. I was gonna say, research notes. And Tiffany Monique Quash, PhD: on our website definitely, take a look at that. Again, if you have any questions or comments or anything like that, definitely be unafraid to email us at cotmpod@gmail.com and definitely check out the website. Send your students. Send yourself to our website. www.cotmpod.com, where we will post our episodes. And again, we're so excited that you joined us Tiffany Monique Quash, PhD: for our episode. We are slowly creeping up to Episode 30. So. Lizzy Bartelt (she/her): Cook the pillow. Lizzy Bartelt (she/her): I feel like that just needs a big musical accompaniment listening to Pink Pony Club earlier, and that's still in my head. Tiffany Monique Quash, PhD: So thank you all so much, and we are so excited. I'm Dr. Tiffany. I'm here. Lizzy Bartelt (she/her): Dear Lizzie, have a great one. Tiffany Monique Quash, PhD: Cheers, everyone. Lizzy Bartelt (she/her): Bye. Tiffany Monique Quash, PhD: Bye.