S1E18: A Conversation with Katherine Masyn -- The Power of Three

[Music] Hello, Quantitude listeners. This is Catherine Mason. And this is the prologue for my Quantitude interview. I'm just going to make a few disclaimers up front. First of all, for those of you who have listened to the Don Hediker tapes, I am not nearly as cool. I have not opened for the remones or medsid vicious and there are no musical interludes during this episode. To Patrick's mom, you may want to just skip this episode. There is very little of your charming child to be heard, because, similar to your long-winded son, I do like to hear myself talk. And my response is to each interview question, average from 10 to 15 minutes. Since this is a 45-minute episode, you will only hear your dear Patrick about three times. For other regular Quantitude listeners, this may come as a great relief. For you all, listen on as you hear even more about the power of three, mad cow disease, evaporated metal films, green laser pointers, mashed potatoes, and Patrick getting schooled on the non-binary nature of life, the universe, and everything. And to you listeners using Quantitude as a drinking game, the rules for this episode are to drink every time you hear me say, et cetera. Now, enjoy the show. So we're very excited. Today we have a special guest with us, Catherine Mason, who has long been one of Greg and my favorite people in the field. And as usual, we're not going to do the introduction because we won't do the service that's needed. So we're going to allow Catherine to introduce herself and we're just going to roll with it from there. So Catherine, tell us who you are, kind of what you're doing now, but more importantly, the origin story for the Marvel comic version of how did you get from where you were to where you are? Hi, everyone. I'm Catherine Mason and I'm a professor of biostatistics at Georgia State University. And in terms of my origin story, I've been a bit of an academic rolling stone for quite a long time, which is not always a good thing, but it's something that sort of worked for me. When I was an undergrad, I was a math major. And I thought that I was going to be a high school math teacher because I really love teaching. In fact, I got a scholarship from the Vienna Rotary Club because of a very compelling personal essay that I wrote about how I wanted to teach the world math. And-- ZVN wrote through the court? Then do you-- No? Viva Vienna, Virginia. The job. That's different. End of the orange line for this, right? From the DMV. Yeah, this is different. Yes. There's actually a Vienna, Georgia as well, but it's not the same. And a sausage? And there was a Vienna sausage, yes. Where were you in undergrad? So I went to William & Mary. And so I was a math major. And I was-- William & Mary is a-- I mean, it's a university, but liberal arts and not a separate math and statistics department. So-- and this was before there was AP statistics in high school. And I hadn't really encountered statistics other than, I think, dice rolling and car draws when I was in maybe grade school. And there was no school of public health. There was a psychology department, but there was no quantitative psychology program. So in terms of quantitative psych or even just statistics, I-- like, I didn't really know about it or know about it as a field or that that was an option for me. And I was actually a psychology minor because I really liked the theory. And I liked sort of the empirical base and scientific process. But I didn't want to do the lab stuff. So I was kind of could have been a double major, but I wasn't willing to run rats and what have you. So as a math major, you could take some advanced courses. And so I ended up in a probability two semester sequence and probability and statistics towards the end of my undergraduate work. And I completely fell in love with it. I hadn't really even thought about going to graduate school because math had gotten a little too abstract for me. And I just couldn't sort of see myself continuing on. And the statistics was amazing. This idea that we could model and quantify uncertainty. And so I was like, all right, well, I gave up on the-- well, at least I put to the side the idea of being a math teacher because I wanted to take and learn more about statistics. And so off I went to graduate school and statistics. Now what I didn't know at the time-- one of the things that I wish that I'd known, but I don't think there was any way for me to know-- was that I actually would care about the application area. I thought that all I wanted to do was just do statistics. And I didn't care what it was about or what types of questions it was answering as long as I was doing my data analysis and my proofs because at that point in time I hadn't done a lot of actual applied statistic stuff. It was still much more sort of mathematical and theoretical. And so I went to graduate school at Cornell in a PhD program in operations, research, and industrial engineering because my advisor at William and Mary had done statistics and operations research. And so off I went there, they offered me a full ride. And I put myself through college. I was young. I didn't really know any better. My parents hadn't graduated from college. And so I was thrilled to be in grad school in some place like Cornell actually pain me to study. I couldn't believe it. I couldn't wrap my head around it. So that was the first of three PhD programs that I was in. [LAUGHTER] Yeah, so well, so the engineering thing didn't really work out for me. And it was a very sort of isolating experience. I was the only woman. I was younger by at least five years to the next oldest person in the program. There wasn't a lot of support or encouragement. And so I started looking elsewhere for sort of an intellectual community. And I was sneaking up. So the Cornell vet school is very good, very good school. And I had a couple of friends because I was playing rugby at the time in Ethica, New York. And I had a friend on the rugby team who was also a vet student. And so she convinced me to go across campus and take a class at the vet school. And it was an epidemiology methods course. And I'd never had any exposure to public health at all. I didn't even know what public health was. I didn't know what epidemiology was. But I loved the class. I loved the methods and the way they were thinking about study design and data and what have you. And I was having a little bit of a crisis because I didn't know what I wanted to do. And here I was in this PhD program. I still had at least four more years to go. I wasn't doing particularly well, which I wasn't used to. And so I had this. The professor was great. The epiprofessor. And it was like during the first, like, Matt Cow disease outbreak. So he was like flying back and forth to-- I don't think it was Glasgow where the outbreak was. To figure out how it was spreading and et cetera. And that's sort of where it had started. And I thought that was very exciting, too. I started reading about virus hunters and whatnot. And so I sat down with him at the end of the semester. And I said, I don't know what to do because I don't think I want to be an epidemiologist. And I still love statistics. Like I still felt in my heart like that was what I wanted to do. And I was like, you know, is there something? Is there something that is public health or epi? And also statistics, he's like, biostatistics have you ever even heard of that? And I was like, no, I haven't. But I'm going to go. I'm going to go to the library because there wasn't a lot online at that. And I started reading about it. And I thought, yeah, this makes a lot more sense. And so I had to make the very difficult decision to actually leave my doctoral program. And I tempt in Ithaca because I couldn't afford to leave Ithaca until I was in another graduate program. And there's not a lot in Ithaca other than Cornell in Ithaca College. So I was temping an evaporated metal films company. And I was in charge of scanning. So they were putting coding on sunglasses and motorcycle helmets and that sort of thing. And I was running the spectrophotometer for the test pieces in each of the vacuum chambers. And you had to measure the reflectivity and transitivity and whatnot. So I would just sit there and run these test pieces and record them. And I thought, we should be tracking this. We could set up a whole quality control thing. So I started compiling the data. And I tried to go talk to the management and the engineers that were at the plant and they're like, who they offer you? Tempt person. We're not listening to you. And it made me a little sad. But it also sort of confirmed that indeed, this was pursuing this regardless of what I'd been told in the doctoral program. And I was basically told like graduate school was in for everyone. You maybe should rethink your plans. But I was pretty sure that I wanted to move forward. I ended up in a PhD program, my second PhD program, in biocentistics. But I was also still taking a look. a bunch of epi classes because I love the epidemiology. And I was in the social epidemiology seminar, like Advanced Graduate seminar, and I was a bit of a curiosity because they'd never had a biostat student in their social epi class. And it was one of these you read in Article of Week, some student is in charge of discussing it, etc. And so I got assigned the most analytically complex of the articles, and it was an article that it used a factor analysis to model quality of life, and then analyzed how quality of life, the underlying latent factor of quality of life predicted survival. And this was, although at this point I had been doing more advanced statistics for a while, I had, but it was out of a math stat and then also then biostat sort of perspective. I'd never encountered factor analysis. I'd never encountered latent variables, even though I was a psych minor, they didn't really have anything other than psych, like basic introscience stat, the statistics. So I was completely fascinated by this idea that you could model things that were not directly observed. And I thought, wow, I mean, that kind of blew my mind. And it was like, this is the whole world. Everything's a latent variable. This is amazing. And I'm like, I am transformed. So I was very excited about it. And I wrote this my final paper and the class was sort of latent variables in epidemiology and how it should be used. Everyone should be using something related to that. But I also had never been trained and anything like that. And they cited Yorgas Cog and some of his work. And so, and I had taken my qualifying exams. I had advanced to candidacy. I was ready to start on my dissertation work. And I went to talk to my advisor and I said, you know, I really, and I had been leaning towards something related to survival analysis, which I had done my honor, thesis, and undergrad on something related to that exact confidence intervals for the Catholic Meyer point estimate something like that. So I was leaning towards something survival analysis, very bio-stadi. And, but I was like, I really, I got to pursue this latent variable thing. So I went to talk to him. And he said, voodoo. We shall never speak of this again. And I like left very sort of press fallen. So and it's like, this is, we don't do this. This is voodoo and go away. So I was like, okay. And so I went away, but I kept reading about it. And I was like, it doesn't seem like voodoo to me. I'm still very interested in it. So I went back and I said, I really, I really think that I want to do this. And he said, well, all right, fine. But I don't even know who you're going to work with. And I was like, so I went around and I talked to some people and there was one faculty who was willing to be the chair, but he had no interest in whatever I was doing. So he's like, I agree to like on paper be your chair. No one else will even do it. And that was like bio-stat, stat. No one was doing this work. Although interestingly enough, Leo Goodman was at the same institution at the time, but he was over in sociology. And Leo Goodman did a lot of work in late class analysis. And it was the catalog, a categorical analog to factor analysis in terms of his approach with log linear models, et cetera. And but in terms of bio-stat and statistics, no one was doing this work. So this guy said, this faculty member said, I'll be out your chair, but you're pretty much on your own. And I've been, now this was my second doctoral program. So I've been around the block, you know, at least two times now. And I thought, no, that's not a good idea. That's not wise to be out on my own doing something that no one is going to provide me and you sort of guide and surmentorship about. I've literally not had a single class in this thing. But I think that I want to do it. And so maybe I need to go somewhere else. And thus I went on my quest for my third PhD program. But this time, instead of looking just in a general content area, because it's like, again, I never knew. Like I didn't know that it was important to find a mentor and find someone to work with, right? That I knew the area I was interested in. And I looked into who was doing that work. Who was taking new students? And that is how I ended up ultimately working with Bank Mutant at UCLA. And he was, I mean, I think I was a very strong applicant because I'd done already a tremendous amount of graduate work. So they're like, oh, we don't usually have someone with this kind of experience, like doctoral levels, the statistics work. I was like, yeah. So I was really able to like, you know, hit the ground running in terms of my graduate research assistant work because I had already done a lot of the foundational coursework. And that would, in all of my friends and the other programs thought it was crazy because although I was enforced to retake classes, I still had to put in the credit hours in the new program. It's like the credit hours didn't get counted. So I sort of started over a third time, but it was the right fit. And if I hadn't had the conviction and I mean, maybe I would call it courage now, I don't know what it was to leave and start over and sort of follow my heart as well as my gut then I might not have ended up in the place that I did. And that was fantastic. And I figured out within my first year of working with Bank that in fact, and this was really irritated me. And it still sticks in my craw. And I had been working with latent variable models the whole F and time except nobody called them that, right? And the fact that everyone was so dismissive about the methodology, I was like, I was doing mixed models. I was doing random effect models and it was like, seriously? I was doing it all along. I was like, you know, Dorothy and the Wizard of Oz. It's like you could have gotten home all along. It's like, click your heels. So there it was with my Ruby slippers at UCLA and that. But I was also engineering public health, banked had a joint appointment and statistics and education. So then I was in the school of ed and it helped me get a lot of exposure to all of the different application areas. And I think that that was kind of amazing. And one of the things I love about what we do is that it does have application across a range of substantive areas. I like to think about it as social science statistics, quantitative methodology, sort of statistics with heart. You know, you get to, I was never going to be the front line person asking the big research question, collecting the data, running the big studies. But I wanted to be a part of those, the research projects that were actually creating, impacting the like social change. And then once I finished, just to sort of, I'll make my long story just a tad shorter, is that I also have now been faculty at three different universities. And I was reflecting on that the other year as I was contemplating moving yet again. And it's like, so it takes a lot to move around. I was like, oh, well, maybe like three is a charm for me. I was like three doctoral programs. And I've been at three universities, faculty at three different places. But also in different areas. So as a faculty in human development and family studies, I was a faculty in a school of education. And now I am actually back in public health and biostatistics, which I never thought would happen because I never thought that a school of public health or a biostat department would ever hire someone who does the type of work that I do. And that just shows that, that the field has changed in terms of the sort of cross-pollination of methodology that you see a lot more traditional social science stat, psychometric stuff, making its way into these other areas of research. So really, I mean, it's just a cliche, typical track of the high school teacher rugby engineering vet, epi, stat, ed, bios, entry into the field. Yeah, I mean, it's like you've heard the story a lot of times. Yeah, I mean, and yeah. So yet again for younger listeners who are five-year plans and ten-year plans and are trying to crack the secret code for how to succeed, just give it up. Just roll with, you know, what's in front of you, what your opportunities are. And I see all of academia in what we do is like milling around at the backstage door at 3am. Then you get using air quotes lucky because you get to see the stars from a Hamilton come out, but you stood out in the rain for five hours waiting for it. Yeah, I mean, I think that, you know, it's one of these things that is true. Like you can't anticipate the opportunities that are going to come, but you have to be open to them. And I, you know, I am a plan full person. So I've always got the five-year plan and I do sometimes have some issues when things are not going according to plan. There is that if you're not, if you don't have some flexibility, I actually feel like maybe I've gotten less flexible at that happens when you age. How do you feel that what you do fits in with the people around you? do they understand what you do in that environment? - And how would you characterize what you do? - So to your first question, there are people who do understand what I do, but for the most part, no. And that is a tough thing. And I think that in my general research area, I work with latent variable models. But I work with models that deal with multiple measurement issues. So measurement, non-invariance, measurement error, et cetera. And the measurement of things that are difficult to actually measure or quantify. And in a longitudinal context, so understanding individual differences. And that's really where my interest is with regards to both longitudinal models and this intersection with finite, mixture latent class models is being able to capture individual differences, rather than ignoring them, or treating them as nuisance parameters, or trying to just get the best, what's happening on average, but not really understanding that the average may not represent a single person in the population. And that's sometimes the people in the tails of the distribution and the outliers are the most interesting and the most important to pay attention to. In public health, with regards to epidemiology, there is a lot of surveillance work that goes on. And it is important work because you actually do need to know what's happening overall and on average before you can take that deeper dive in terms of studying the variability. But their methods around that, the surveillance stuff is a lot more just sort of like summary and not modeling. In terms of the sort of rooted in medical science, this idea of measurement error, measurement instruments and measurement bias, and this sort of thing doesn't really factor in as much they think about diagnostic test sensitivity and specificity, but beyond that sort of thinking in a multivariate world, for the most part they don't, it's like a particular disease outcome. There may be co-occurrence, but they have a relative degree of precision with regards to some of the physical health outcomes. So they're not necessarily used to thinking in these multivariate systems and they don't get trained that way. And then no one uses the same language, no one uses the same notation, and that's still as much of a problem now, as it was 20 years ago, as it was 40 years ago. In the school of public health, though, because people are very interested and focused on and prioritizing looking at health disparities, eliminating health disparities, focusing and promoting health equity, that there's more attention to the social determinants of health. And in Atlanta, which is an incredibly diverse city, and Georgia State is a very diverse university, we don't have a majority race, ethnicity group on campus, that it is a little microcosm of the greatest health disparities that exist in our country. So from one side of the city, sort of northwest to the southeast of Metro Atlanta, the difference in life expectancy is, I think, over 10 years, it's some shocking difference. The rate of death for breast cancer in the African-American female population is much, much higher. They're differentially affected by triple X breast cancer, which is particularly lethal. So there's a lot happening in terms of understanding social determinants. And as soon as you get into social determinants of health, you have a bunch of psychologists and sociologists piling on. And they are bringing their sort of quantitative toolbox to the sandbox. And so there are, we have quite a few folks in the school of public health that are actually trained as psychologists. And they have been exposed a little bit more on the psychometric side of things. So they understand a little bit more what I do. But the more traditional folks that have been more collaborative, classically trained in epidemiology and public health don't necessarily familiar with the methods. And you also have to consider that most schools of public health are sort of built on and fashioned after the medical school model. So the bio statisticians-- and this varies from place to place. But I feel like when you have a quantitative psychology program, there's a little bit more sort of collaborative like your psychologist first in a quantitative methodology, second possible, or what have you. But with bio statisticians and statistician statistical cores in medical schools, it's like you are there to analyze other people's data. So that is where I think the greatest sort of confusion and misunderstanding comes. It's not that people aren't familiar with my particular area of research is that they can't really wrap their heads around necessarily. Because they just don't have a frame of reference. I mean, they're very intelligent people, but that there are people that actually work on the tools, making better tools, developing and extending tools, and not just using the tools with their data. But they see the bio statistician as sort of a, you don't have your own research. You're just helping analyze data. Like that's your work. That's what you do. And it's more of a service. You teach our students. You analyze our data. And trying to, especially for the more junior bios.faculty, trying to find a way to help everyone around understand that they have their own research program and their own research agenda, and that they're held accountable for that level of productivity and scholarship just like everyone else is, and that they're not going to make it if all they're doing is data jockeying for someone else. I was in a working situation once that was exactly what you described. And I was grousing about it with my wife and she paused for a moment. And then she kind of lit up and she said, oh my gosh, you're the Xerox repairman. And I said, what? And she said, well, you have these technical skills that nobody has. You're fun. You're integral to this part of the department that the Xerox machine needs to be working. But when it's working, you're not needed. And they'll call you when the machine breaks. And I was like, oh my god. That's the entire thing that you just described. For younger people, there used to be these things called Xerox machines. Greg and I are still trying to get our head around losing the mimeograph machine. I like the smell of the blue. It is. Oh, that was the best. I have been in that situation repeatedly where you really have to try to educate the people around you. And you're in this incredible minority, right? They really do view you as whether it's Xerox repairman, or as I've said, you're the mechanic they bring on the road trip, just in case the car breaks down. When people say, oh, but what's your content area? The answer is this is a content area. And if people like me and you, and you, if we don't make methods better, then we're not going to be able to take things further than they are right now. So it's a conversation I've had many, many times. And it's to great frustration. I will say that I've had a lot of pleasure working with our school of public health. I'm not located in there, but I've had a partnership with them for a while now. And just as you said, they're starting to recognize the value of a lot of these psychological constructs. And that for people like us, that means two things. One, a lot of instrument development that is native to the health environment. All right, trying to flesh out constructs that might have been assessed in a variety of ways elsewhere, but importing them into a health context and creating instruments specific to that domain. And then using that as predictive of what health disparities, health outcomes, I tend to work with folks who are interested in vaccination and vaccine disparities in the role that constructs such as trust and the various dimensions of trust play in that kind of thing. So you're preaching to the choir here. I feel for you when you have to have those conversations. But if they allow you to flourish, and you clearly are flourishing, at least within our field, I think that's terrific. I do think that it's something that for up and coming folks, that it is a different-- in terms of being an academia and being an academic methodology, a serious amount. All right, let's do it. There we go. Greg's opening the Diet Code. There we go. But folks, listening, is Rakshae to conference together. That's how the three of us are here. There are no rooms available. And so Greg and I had to actually break into a room. And we are hiding in here now. And we pasted signs on the outer door that says, please do not disturb recording and process. And we're kind of hoping that that means we won't get caught. But yesterday we've already been caught and escorted out. So yeah. That's amazing. I was about to say, the signs are-- they're not superficial. They're little like ripped pieces of paper with its raw recording and progress and with scotch tapes. So if this stops suddenly, that's why. So now that Greg has completely thrown up there-- Yeah, Cafe, and you were starting with the brilliant-- I was. --in some of the things. Yeah, so saying that for folks that are starting out, it's easy to get sort of eaten alive by the demands. Because people-- it's not that they don't value your skill set. they absolutely do and you're going to be in demand, but you also have to pursue your own scholarship and that can be very difficult to do. I know it took me a long time and I still, I think I struggle with it on a daily basis because I actually really like working with people and collaborating and I like working with real data and so that's, you know, it's one of the things that's really satisfying about the job and when I'm doing my own work like at my home institution, I'm pretty isolated and it's, you know, it's nice to be able to do collaborations with other methodologists sort of remotely but since like sometimes the choice to do my own work is also the choice to, you know, sort of like hide away, there aren't a ton of programs, but you two are lucky in terms of being at places where there is a critical mass of scholars in a similar area. Well, the parties are crazy. Well, yes, I can only, I'm at off the hook. Oh yeah. There are not a lot of those programs out there and for the folks that are aspire to get into academia or be on the academic track more likely than not simply because of availability, you're going to end up in a position that is not at one of the places that maybe you've even studied. I mean, I was really lucky about the community that I had at UCLA and I feel like a little of my movement earlier on was chasing that sort of feeling and that sense of belonging and community of scholarship. I really missed that. I missed lab meetings. I missed, you know, that sort of thing but you're not, chances are you're not going to end up in a place like that and so you have to, one, you have to seek out people to work with so that you're not isolated and you have to find some understanding. And senior folks that are going to sort of help you find your way and navigate academia in general even if they're not working in your content area, IE methodology, but it can be tough and if you're, if you're not careful, you can be sort of even alive. So something I'll add is that you have done particularly nicely as a Patrick and that is finding a way to get the methods that you're interested in and development of those methods embedded within or partnered with particular context areas. And sometimes that's completely out of necessity. If you need funding, you need to partner with, you know, alcohol researchers or something to try and do your thing, but, but we can inspire by those problems too, right? College come to us for sure where they go. We have no idea how to do this and you think about it and you go, well, there really isn't a good way to do that. So let's put our heads together and see if we can come up with something. It's inspiring methodologically and, you know, sitting next to you two have done great things with that. So there's a lot of positive to be associated with that kind of environment. Yeah. And I think, you know, for me, just learning so early on that being in that social epidemiology class and it's like that put me on the path. I mean, to the research that I do, it wasn't a stat class, right? It was like sitting in an applied class and talking to these substance people and then reading these papers and that led me to the methodology. But I, I mean, I do like listening to methodological talks, but I kind of, I like the substance of talks. So turning a little different direction. I don't know. I can't speak for you too, but I will. I think we all three love our day jobs, right? As this is a great gig to have. When you think overall the responsibilities you have, all the things you do when your day to day job, what do you derive the most enjoyment out of? So I do love my day job. I cannot imagine doing anything else. However, I don't love my day job every day of my day job. And I think that a lot of academics sort of struggle with balance. And we've certainly talked about that amongst ourselves. And that sometimes there can be more demands than there are. There is hours in the day and that can get a little overwhelming in terms of trying to figure out priorities. I love teaching and I love working with students. Yes, I started out wanting to be a high school math teacher and, you know, I pursued this other track, but I never stopped wanting to be a teacher and an educator. I made a choice because I liked the research so much that I didn't want to only teach. But when my friends say, well, you know, you could make so much more money, right? I mean, our skill set has market value. That's just the reality. And academia, you are not going to get paid what you're worth on the open market in terms of the skills, especially now in this sort of data driven world that we live. But I can't or that I should go work for a think tank or this sort of thing. It's like, but I don't know that I could leave the teaching and the mentoring behind because I really like working with graduate students. And that's a rewarding part of the job. But it also takes a lot of energy and it takes a lot of work. And I usually have at the end of the, towards the end of the semester, this sort of like crisis of faith because I've put so much of myself and my time into the class. And the reality is that for those of us that care about the teaching aspect of it, we're in terms of our metrics and how we're rewarded. It's not valued by our institutions in the same way in terms of the balance. It's like being a good teacher is never going to be the thing that gets you tenure trying to figure out a way to sort of like, tamp that down. So I like that part of it. I struggle with it some days in terms of it taking me away from the science. And I love my collaborations, but sometimes it takes me away from the work that I most want to be doing. I think that part of what I like is the variety. I think that if I had to do the same thing every day, that whatever it was, even if it was my most favorite part of my day, if I had to do that thing every day, I would go out of my mind. I mean, if I had to show up and like teach Monday through Friday, I'd get fired. Yeah. But I had to punch a clock or answer to someone. I mean, it would just be a disaster. I could never hold down a real job at this point. So there are three things that I wanted. And you know, at least two of these I would imagine three things. So one is the first time I would say I got to know you ish was when you came out to Maryland for conference that we had on next year's. Yes, the very first silver conference, the inaugural silver conference. It was maybe 13 or 14 years ago. And I specifically wanted you to come out because of what a gifted instructor you were. You're still okay. No, I think so. I know. But so, whatever. So people notice, you know, we see this and you're right. Your institution might go, "Well, I mean, she doesn't suck." So everything's okay. But the field recognizes this about you and values that. And you might remember this, I use a green laser pointer and I, when I teach and I have since, since her presentation because I thought green was so much cooler than a red laser pointer. And so for the last 14 years or so, I've used a green laser pointer because you inspired me to use a green laser pointer. Thing three, one of my student, do you know Harry Potter or at all? I don't know that one. Okay, right. One of my students refers to as her patronus or spirit. So that she channels her inner Catherine to be able to be successful. And that's a pretty high compliment. Yeah, that's, I really appreciate that. Well, you mentioned the work in terms of some of the dissemination and the translation piece. And that comes through the instruction and being committed to sort of getting the tools out there because you're a tool maker and no one's using your tools. I mean, it doesn't, it's not very satisfying. There is the, those types of publications too. And there are some folks that don't necessarily count or value that type of work. But I don't know that everyone can do that type of work. I remember when I was a graduate student reading some of Judy Singer and John Willett's work and thinking how amazing they were in terms of doing some of that translation and dissemination work and making this stuff on paper accessible. Right? Because there's only going to be so many people that come through the classroom, right? But it's like who you can reach by doing that type of writing and making that sort of contribution to a broader, to a broader field. And then they, you know, they did great methodological work as well. But that, where they were at, that work was valued. And depending on where you're, where you are and how the bean counters work, then not always going to, if it's not original, generative methods work, it's not necessarily going to count as much. But I think that we need people doing all of those sorts of work and communication and translation as well. I think it's an important part of it. And I am actually really committed to that aspect of it. I mean, it's like I want to be doing innovative stuff. But I really, I enjoy the translational piece as well. It happens in the classroom though. You have students in your classroom, especially at an introductory level where you are either going to be the person who puts the last brick in the wall that keeps them from continuing. We're going to be the person that helps to tear down the wall and make them decide to step over a few bricks and go on to the next class. And you're a kind of, you're a wall terror downer. And that's the kind of person that we need as an ambassador out there bringing more high quality people in. So, Greg, I have a thing where we throw little binary things at you and you have to give a quick response one more. Patrick, I don't believe in binary. No. I'm going to give only non-binary. You are going to give non-binary responses to my 54 year old male binary options that I give you. Now, the thing is, Greg and I haven't prewritten these. We make them up on the fly. So, I'm going to start. I got one and then Greg is going to scramble and come up with one as well. You can respond as long as you want. They're just a quick you have to pick one or the other. Classes are continuing on. Both. Did you not understand the binary part? You have a pit. It's a fluid thing. It is, you can have both. All right. Tell me how you can have both. I believe that there is a dimensional categorical spectrum, my friend. And then-- A dimensional categorical spectrum. Yes. Right? On the two ends of the spectrum. Two ends. So, they're binary and the ends. All right. Don't mess with me. Towards one infinite side of the spectrum, there is the purely categorical discrete. And on the towards the other infinite side, there is the purely continuous. There are the real numbers, infinitely many numbers between any two numbers kind of continuum. No two people share the same value. And then there is much in the middle. So, this is a false dichotomy. All right. The continuous in the cat. It is. False. It's old school, man. All right. So, you have to expand your mind into the non-binary latent world. All right. So, take for example a situation where you have multiple categories of say people because that's usually who we're categorizing on a particular construct. But there are variations in both in degree and in kind. So, you can think about a particular psychiatric condition where there may be something where you believe there's a discrete sort of diagnosis or state. But there still may be levels of severity within that state. And so, it is at once in terms of understanding individual differences at once both categorical and continuous. So, I'm not being interviewed here in my opinion matters not at all. I completely agree. Hey, there you go. So, you basically were setting me up. It wasn't a set up. That's just what you were. It was a set up. It was a little bit of a set up. Yeah. Is I'm trained as a clinical psychologist in the field of diagnostic psychology. You date back, I mean millennia, but you know, you were in trying to say there are continue on with psychological distress, but he just began to notice in his own patients that there was a point where there was some fundamental shift in what was presenting itself and that there was something happened at some transition point. So, I agree. I just wanted to see what you're asking. Healer. Healer. Healer. Healer. Healer. Healer. Healer. Healer. Healer. That's a really hard one to follow up with a binary question, but I think mine is a bit as important. Catfish or hush puppies. I do live in the south. I'm going to go off script and cross. Okay, that's the best answer possible. That's my death row food. There's a big plate of mud. I swear to God, this is something you've given thought of what you're lost me a loser on death row. I actually have. Like you have it. Come on. What's your mashed potatoes? Mashed potatoes? Yeah, I love mashed potatoes. Just a big, just a big plate of mashed potatoes. Top ins. More potatoes. I'm very brave. Yeah, I could go brave. These sour cream. Okay, you're making a pretty good case for that because I really don't need to be watching my health at that point. What about twice baked potatoes? The best in the world. And I actually make killer twice baked potatoes. You come over to my house sometime and I can make it. Because then you've got both the best of mashed potatoes and baked potatoes together. Killed together with sour cream because that's the secret. The cheese and the sour cream. Wow. So you and you have a diet. I didn't mean to put crawfish in as a death row food for you. So would you have a new end of interview question? What is your death row dinner? I think I think I would have to just call an audible on the day of because you don't know when you're sitting there, right? I mean, you can imagine what you might most want. But like you're on the outside right now, right? You can have what you most want pretty much any day of the week. So do you really know after like decades of sitting on death row and it's now finally come to pass, right? You know more pardons, no more appeals and this sort of thing. In that moment, the food that you want to put in your mouth, right? Like, you might feel like lethal injection food might be different from electric share food. Oh, it might, but it's like, you know, it's like we're craving stains that you remember. I mean, maybe it's something that's like weird, but from your childhood, right? You're facing your like on death store and I don't know. Maybe it would be like a peanut butter and jelly sandwich. You know, I think this approach is kind of consistent with your origins. Tie it back. Tie it back. Just like to see what happens. All right. I don't know. I mean, if I had to commit to something, I'd say it, but I'm definitely working on my dissertation right now, but I'm thinking of change and feel because you know, I'm just feeling it. And three is magic. Three is a magic number. So there shall we try the end of the final question that we pose. All right. You go ahead. All right. I'll screw this one up like you did the last one. Yeah. I will screw this up as I did the last one. So what is something you thought you knew, but later found out that you were wrong. Oh boy. Well, I thought it was wrong once, but I was mistaken. Yeah. That's quite good. Thank you. Don't get them started. I feel like it's a really long list. I feel like I am, I mean, it is cliche to refer to myself as a lifelong learner, but I do think that that's one of the great things in terms of what we do is that we get to just be students of the world forever. Right. I mean, I feel like everything that I knew is either wrong or different or what have you. I mean, I thought that I was going to have my PhD and be married with kids when I was 25 and I was pretty wrong about that, which caused me some distress when I turned 25 and I hadn't achieved those goals, but I developed other goals. But out a couple of years to that, and that's exactly where you were. Well, that's true. Thank you. Should we wrap it up? Go ahead and sign up with your smooth radio voice, Greg. This has been Greg Hancock and Patrick Kuren with Quantitude. Patrick has to go to the bathroom. Is he? Okay, we're going to come right another three. And we're out. That's good. Awesome. And now for the epilogue, what I didn't say at the beginning was that at the time Patrick and Greg were lowering us unsuspecting and good-natured friends and colleagues to do interviews in a random hotel meeting room for unauthorized reporting sessions at this conference of ours, they had yet to release a single episode of Quantitude. None of us actually knew what these knuckleheads were really up to. So if you were to ask me today what is something I thought I knew, but later found out I was wrong, I think you might know my answer. #rogrets. But in all seriousness, I do want to thank you for listening and thank Patrick and Greg for the opportunity to contribute. I really do love what they're doing here. For obvious reasons, this episode was brought to you by the number three and the color green. It is also brought to you by the letter E. Is it a mathematical constant, a function, a residual? My middle initial, it's all of those things and more. And finally, by the base two number system, the only acceptable binary. I'll close with the standard Quantitude disclaimer, this is most definitely not NPR. Although some tote bags for contributors might be nice. I'm just saying.