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Dr. Ruth Brown is an assistant professor in the Department of Psychiatry and the Virginia Institute for Psychiatric and Behavioral Genetics at Virginia Commonwealth University and an expert practitioner of state-of-the-art data science tools and the founder of the HOPE Lab. She is also the Co-Founder and Senior Advisor at Aigora. Dr. Ruth Brown-Ennis received her Ph.D. in Clinical Child and Adolescent Psychology from Virginia Commonwealth University.
Her general research interests focus on better understanding the interplay between environmental and biological factors in the development of depression, anxiety, and traumatic stress and using these findings to improve mental health care. She has a particular interest in the role of these factors in people with developmental disabilities.
Transcript (Semi-automated, forgive typos!)
John: Ruth, welcome to the show.
Ruth: Yeah, thank you for having me.
John: Yeah. We should get the elephant in the room out of the way for people who don't know, Ruth is also my wife, and she's on the show because I've gotten many requests for people who would like to know more about the other Co-founder of Aigora. And it turns out to not just be some self-indulgent thing that I'm doing to have Ruth on the show. Ruth is really truly qualified social scientist, and there are many overlaps between sensory science and social science. In fact, many of the tools that we use were developed by social scientists. And actually, I think at this point that river has started to flow both ways because increasingly I see tools from sensory science in particular FactoMineR, Factoextra Package, the tools developed at the Agrocampus group are now finding their way back upstream to social science and are commonly used there. So, Ruth why don't we start with your background and how it is that you got? Let's just follow your research trajectory. Let's forget about sensory consumer science. Let's just talk about the work you've been doing, so maybe you can start with your working graduate school and then into the present.
Ruth: Sure, my working graduate schools in pursuit of PhD in Clinical Psychology started off with a Masters where I was doing research on kind of treatment of adolescence with ADHD and so that really piqued my interest in some kind of measurement questions that was in relationship to what we call treatment integrity. Basically kind of internal validity of a study and whether or not therapists are doing what they're supposed to be doing or in my case of my thesis, we were training parents to use behavior management strategies. And so whether or not the parents were actually using the things that we taught them to do and then how that was impacting them, their child's improvement. And so, yeah, that kind of sparked a lifelong interest in measurement. I went on to then get my PhD. And they're also focused on trying to measure what was happening in the therapy room and how that was associated with kind of treatment outcomes and then also some on symptom measurement, how we measure symptoms in people and are there differences across different groups of people? So the same measure, the same kind of questionnaire, symptoms of depression or anxiety. Is it measuring the same people from different backgrounds, for example and that's been a big part of my work.
John: Right, and now you've been at VCU for, I mean, you did your postdoc there and then into your professorship, and actually you went to graduate school there. When did your postdoc start and how long have you been a professional researcher there?
Ruth: Yeah. My postdoc started in too many years have passed now, I can't remember exactly the year. I think 2011 when I started.
John: So about ten years now?
Ruth: Yeah, ten years ago.
John: Yeah, that's great. Okay, and one of the things you mentioned, measurement and something I've always admired about you is trying to get, I mean, I've admired a lot of things about you, but in terms of scientifically, I think you are very good at kind of homing in on, well, you're very aware of the difference between data and the world. And I think there's a lot of confusion really around. People get numbers and they assume those numbers are meaningful. But I think a sensory and consumer scientists are very attuned to the difference between data and the world. Can you talk to us a little bit about what are some of the, you know when you're reading research, I always see this with you. You see, there's some news headline, you start to dig into the data. Can you take us through your process when you're trying to get to the bottom of these questions of measurement, like, what is it you're skeptical about and what are things that people should look for when they're trying to make sense of the numbers that they see?
Ruth: Yeah. That is one of my kind of favorite things to do when I'm reading research. I mean, it's true for a lot of people to kind of pick apart research and kind of understand its limitations because every study has its limitations, and it doesn't necessarily mean it's flawed. It just means that there's no perfect study and so we have to really understand the limitations to understand when the results apply and when they don't.
Ruth: But, yeah, measurement is then the thing that I tend to kind of zero are people talking about and using terms in a way that kind of captures the limits of measurement, especially within psychology, probably much like a lot of the kind of sensory and consumer science where we're measuring things or attempting to measure things that we can't actually see and test objectively. So there's always a subjective component to it when you're measuring, like salinity or something, maybe you know exactly how much salt is in the water or something. But then as far as how somebody perceives that is going to be different and so I think that's kind of similar with psychological research, too. You know one person's level of depression is different from another person's level of depression and so then that will, we assume that causes them to then circle different boxes on a questionnaire. But it may not because people have different response styles. Maybe depending on what they've experienced in their life. Somebody who's experienced a lot of really major kind of traumatic events is going to view an event differently than somebody who hasn't had many stressful experiences. There's a lot of reasons why those numbers might mean different things to different people, or why they might have checked those boxes in a different way. That's one source of that variation. But then also, sometimes we get lazy when we're writing and talking kind of using generic terms or something called the jingle-jangle fallacy, which I really like. I actually get them confused, which is the jingle, which is the jingle. But the idea is that sometimes we use the same word to refer to something very different. So, for example, in some of the research that I've done on impulsivity is one where impulsivity can actually mean a lot of things. A lot of people think they know what impulsivity means. And you say to somebody, we're studying impulsivity. Well, what impulsivity means to you, what it means to me can be very different things because there are actually many different types of impulsivity. There's impulsive behavior when people are feeling excited and they're risk seeking and then there's impulsivity when people are frustrated and they're trying to escape something unpleasant or a couple of facets, and then there are other kind of lack of conscientiousness kind of impulsivity. And then similar with resilience is another thing that I study is resilience and that's another thing where a lot of people know or have an idea of what resilience means. But the way that you measure it, you can end up with very different results depending on how you're really operationalizing it. And sometimes we get lazy even in our scientific writing, in our thinking and lose track of the thing that we're actually measuring versus the thing that maybe we think we're measuring.
John: Okay. Well, I definitely want to get back to that. I would say that we certainly have that issue in sensory and consumer science because you'll have something like, the last example of creaminess, right? That creaminess means different things to different people. And so you have a trained panel. You can train them on creaminess, but you go out to consumer, what does creamy mean? Is creamy mean it tastes like milk? Does it mean it has a consistency of lotion? It's really not clear what creamy means and so that you have to nail down your terms. You have to know what you're measuring. And then even beyond that, there's some latent idea of creaminess that you think maybe you have in your head as a product developer, right? Or someone says, well, we need to make this creamier. Okay. You have your interpretation of that. Maybe you have some physical interpretation. It might have nothing to do with the way people are using these words. So that's great. I did a quick look up here on jingle-jangle Fallacy, and I say, this is great. This is a very useful idea. A jingle fallacy is when you think two things are the same because they have the same name. A Jangle fallacy is when you think two things are different because they have different names.
John: So Jingle-Jangle, that's very good. It might be the title for the show. That's great. Let's get back then.
Ruth: Because it's Christmas time.
John: Yes, jingle-jangle. That's perfect. Okay. That's great. Let's talk about that then. So how do you tease that apart as a researcher? How do you try to get to the bottom? I mean, these are latent constructs oftentimes that we're getting to. And I actually think there's not enough attention paid in sensory and consumer science latent construct. But can you talk a little bit about your views on this and how you handle that?
Ruth: Yeah. I kind of think about it as a kind of process of triangulation. Basically, when you think about kind of like GPS, the way that they send multiple signals and kind of see how they interact with this kind of idea that you need at least three kind of lines or whatever to identify a point in space. You probably actually know more about that as geometry than I do. But I think about it, there are strategies like multitrait multimethod approaches where you attempt to measure something kind of multiple ways using multiple methods, so, for example, you might within the context of my research, we had to do, like self report, informant report, which is then where we ask somebody to report on maybe, for example, for parents and children. We ask the child, we ask the parent what they think the child's symptoms are. We ask a teacher, an observer, somebody else, or we set up some condition like this is use phobia research where we set up some tasks where they have to approach a spider if they have a spider phobia and you measure how far away, how close they're willing to get, and then you can look to see the pattern of correlations between those scales to see are they correlated the way that you would expect them to. If you compare them with other things that are supposed to be unrelated and do you see patterns of low correlation with those other items. And so that's a useful strategy. Factor analysis is another and factor analysis can be used to do multitrait multimethod analysis. You can do it just kind of a generic correlation table. But you can also use factor analysis, then where you are putting in measurement factors to account for differences and trying to load everything onto this latent factor.
John: Structural modeling, some latent analysis, this kind of thing or latent profile analysis, something else you're interested in?
John: I think there's a bias in sensory and consumer science towards PCA over factor analysis. And I don't think that we do enough factor analysis. I think that for whatever reason, historically, we got caught up with tools that are mathematically easy to use. Okay and there's a kind of appeal to decorrelating things, but I think sometimes we should, yeah, this is really good. So I really like what you said also about the approach avoidance, because that's a kind of behavioral metric. So can you talk about the range of data? So you've talked about these self reports, you've talked about informants, you have some behavioral measures, what are some of the other sources of data that you find to be helpful in your research?
Ruth: Well, something that I'm kind of interested in exploring more. I'm starting to kind of dip my toes and do it are kind of biological measures, biomarkers. And so for my study that I'm doing now, I'm looking at epigenetic markers of stress and depression. So trying to look at changes and DNA methylation is this kind of process by which genes get turned off or on by things that happen in our environment or our diet, things like that. And it can have pretty strong effects. I mean, the major finding of a lot of the genetic research, the kind of genome wide association studies is that most especially psychiatric conditions, there are not big, single kind of genetic causes. They all account for these teeny tiny fractions of the variance. But then DNA methylation seems to be much more potent in terms of its relationship with these factors. Other kind of stress, hormones and things like that are things that I'm interested in. So try and incorporate this as another source of data and thinking through and I have to go through this process of triangulation, trying to figure out how for something like stress or depression, especially for a group of people who maybe have difficulty expressing themselves. Whether you're talking about children or you're talking about people with disabilities. They may have difficulty expressing themselves. Their parents may not know what's going on in their mind or emotionally. So they may have a hard time understanding what's going on. And so we have to figure out creative ways of adding more data points, more types of data so that we can start to get clearer pictures and so for, like, treatment or kind of outcomes research do we then see correspondence and how things are improving if self report and parent report and biomarkers are improving? Well, that's pretty good that we're doing something meaningful.
John: Right. That's fascinating. I mean, definitely a lot of overlap between sensory and consumer science with the diversity of the data that we deal with and the way that we, it's a challenge to make sense of all the data. Okay, I don't want to get into hard technical questions because right before the holiday here, but I am kind of curious about let's talk about data science because I know that this has been a journey you and I have been on, to some extent together and part of why when I proposed Aigora to you, I think you thought it was a good idea. You've been giving me advice over the years. We should not involved in the day to day operations, but you have a good idea of what we're doing. Can you talk about data science and how it's transformed your approach as a researcher and how it's helped you? What are some of the, what would you say what were the general tools, like the general lessons from data science that you think would be valuable to any scientists say?
Ruth: Yeah. I mean, I think actually the thing that I have gotten the most out of that, I've really appreciated the most out of learning because I learned statistics in school and I use kind of statistics and I use SPSS for all of my graduate training and a lot of my postdoc. But the thing that really appealed to me about switching over to R was some of the automated reporting and being able to basically program my analysis and to get the report. I really love using Markdown because I can then do my analysis in an R document, these little code chunks. I can have my means and I can have my correlation tables and I can have my regressions and everything and I can just have the results then kind of piped into the text. So that then if I make some change because I am the kind of person where what I do with analysis, I'm like, no, what happens if we separate this analysis out by males and females and then rerun the analysis? What that used to mean when I was using, like, SPSS, for example, and I would run the analysis and get this output pages and pages of output tables and then I would have to go through this process of by hand putting into a word document, all of the little correlation values and sometimes I might have, like, 20 variables in a correlation table and so then if I made a change, I would have to go back and change all that. And so then the risk for errors in that or in the text kind of reporting beta coefficients or something like that or the text results have a regression, the possibility of missing something if I've updated the analysis was really anxiety provoking to me and just tedious. And so with R then I feel like I can be more assured that the results that I'm then submitting to a Journal for publication are the correct results. So that's one of the things it streamlines that process and less efficiency and I feel like better integrity. That's the thing that I really like. It's also made accessible a lot of new tools and things that aren't available in SPSS. For example, I don't know how common SPSS is in sensory science.
John: Statisticians within companies will use it.
Ruth: Yeah. R then just kind of opens up the universe of tools out there.
John: Right. I think an underappreciated aspect of automated reporting is it supports curiosity. Then maybe you have some question you're like. I wonder what if this is like you're saying how we do working on things? What about this? What about that? If all you have to do is change a few lines of code and press go, then you'll investigate it. And if you're going to have to spend 5 hours, you don't even have to think about it, what will happen is it'll affect you subconsciously, right? It will limit your thinking because you'll just stay away from it because deep down, you know, it's going to be too much trouble, right?
John: Yes. It's definitely been because it's inspiring you to watch you also. Can you talk about your process of learning R? Have you found it easy, hard, easy sometimes, hard sometimes, how is your experience learning R?
Ruth: Yeah. It's been a little bit of a I think love-hate relationship at times. I do like it. If I were in a position where I was doing more, where I had more opportunities to do data analysis, it would be a lot easier. I have had to focus more because early in my career and still getting data and so kind of limited and the access to data that I have so I'm spending my time getting data. Of course, now I'm going to be spending my time writing grants in order to get more funding in order to get more data. I haven't had as much time to do data analysis, and I enjoy it. I really like to do it. But then sometimes it's been a struggle because I feel like I'll learn how to do something. And then it might be six months before I get a chance to come back and use it again. Sometimes I feel like I'm having to go back and relearn some of that stuff or spend some time refamiliarizing myself with it. So that can be a challenge. But there's just so many great resources out there. Data camp was one of the things, one of the places where I got started learning. I really found that extremely valuable videos were kind of easy to understand and digestible and opportunities to practice. They also use a lot of LinkedIn learning. There's a lot of really great R and just general kind of data science courses, data visualization courses and things like that on LinkedIn learning. So I found that to be helpful then, of course, just like, you know, YouTube and Stack Exchange and all that kind of those kind of resources where I just kind of Google, but it is a new language. You have to learn how to ask questions the right way to get your answer.
John: Yes, that's right. That's a skill. You definitely learn. That's good. So actually, I don't want to rush you here, but we're almost out of time. There's a question I want to get to, which is the kind of a two sided question, right, because you've seen Aigora now. We're almost three years old and you watch us progress. The two related questions that I want to ask you before we have to wrap it up are what have you seen from Aigora that has been interesting to you as a researcher yourself? You know tools that we've been using technologies, this kind of thing you've seen us embrace that you are interested to use and then on the flip side, what are some of the tools from your neck of the woods that you think sensory and consumer scientists should embrace? More things that you're really excited about in your own research, you know ways of looking at the world, kind of lessons that you think would be useful for sensory and consumer scientists to learn or to at least think more about?
Ruth: Yeah, that's a good question. Some of the things that Aigora does that I am so jealous of, I wish that I could get my hands on or, like the dashboards being able to use shiny apps in order to create interfaces for data exploration. I see that I'm like, I need a Hope lab.
John: Yeah. Well, maybe we can bound to something.
Ruth: That would be awesome. Just kind of seeing how the tools, because I think that would help my students a lot. I mentor undergraduate students, and a lot of them are interested in learning more about doing data analysis. They get kind of some basic coursework in their undergrad, but they're interested in learning more, but trying to teach them how to use R is the learning curve is so shallow on that. They wouldn't be able to put in enough kind of time and effort in the time that they had with me in order to really benefit from that. But something like a shiny app where they can look at data and see visualizations of it and kind of have an opportunity to play and explore with the data that they're collecting, I think would be a really great way to engage them and kind of spark their curiosity. So that's definitely something I would say. And then also, we've talked about this, too, before kind of using technologies like smart speakers and thinking about how to some extent there may be a technological gap, but I think thinking about universal design and how strategies like that can really open up opportunities for people who may have been left out of research.
Ruth: With developmental disabilities, for example, that could potentially be really beneficial in terms of doing research and data collection. So that's something.
John: Well, that takes us, I think to one of your passions which is the fact that people with developmental disabilities are so often excluded from research. And I think that they are almost certainly excluded from consumer research as well. And so I think that right now there's a big push. Happily, I'm glad there's awareness in consumer science that certain groups have been in various ways marginalized by the way that research is conducted. Well, for one thing, very often, this changed with Covid, actually. This is one of the right sides of Covid ,is you would have to come to a central location to do a test. That means that if you aren't available in the middle of the day because you have a job or whatever, you oftentimes can't participate. And so a lot of times what we're talking about are wealthy, middle aged housewives who could come and do the research. That meant the products were getting developed and really many large companies are developing their products. But developing products for that group, for the most part. Right? I mean, you see, now Johnson and Johnson making progress with their band-aids have being different colors and you see an attempt and I think real progress and including groups. But do you think that this is something that we should be more aware of in consumer science is making sure that people with developmental disabilities aren't left out of research?
Ruth: Yeah. Absolutely. Some estimates, I think about one in five people, one in five children in the US have some sort of developmental disability. It's a very heterogeneous group that could be physical disabilities, intellectual disabilities, a lot of different types of disabilities and yeah, I think you're right that they get kind of left out of product development and research in general yet they use these products. Company developing some packaging like lotion packaging and things that have Braille on them or kind of easy open tops. A lot of times the form is put over the function, making something sleek and beautiful, face creams and things like that, for example. But if the package can't be easily opened, then it's hard for a lot of people. But people with arthritis and people with mobility issues and things like that, they just didn't get kind of get left out of that. Yeah, so I think that that's a big area of opportunity and growth.
John: Right. I think that's very good. It's great. Well, and then I think we already touched on this a bit as well. I do think that from your research, you're interested in the kind of latent measures, trying to different ways of getting at what reality is rather than just data which is just approximations of reality. Like trying to get closer to reality through multiple views, analysis that are maybe more focused on trying to get to these late measures. I think that is definitely something we should be aware of. Okay, so we're basically at time, is there anything else on the topic you want to say before we wrap it up?
Ruth: Well, I mean, I could talk all day.
John: Okay. Let's wrap it up then with this question, we always ask for advice for young researchers and I know that you have, now we should mention somehow I didn't make it into your introductory bio, but I'll put this in the show notes that you have lead a group, a laboratory at BCU called Hope Lab. So what does Hope stand for again?
Ruth: It stands for Healthy Outcomes through Psychosocial Equity for people with intellectual and developmental disabilities.
John: Right, because equity is a big topic in your world. These people have been denied equal access.
John: I think, very unfair ways. So that's good. We will put that in the show notes. So you have students that you mentor, what advice do you have for young researchers? What would you say to an undergraduate or a graduate student who's coming up and is going to be starting the path for her career?
Ruth: Let's see. That's another topic that I feel like I have a lot of things that I could say. I think a big one is I guess the kind of main advice is be willing to ask for help.
Ruth: When you need it, seek out support, ask questions and seek out support. I think, especially students who come from underrepresented populations.
John: Yeah. Maybe have new role models that can really help them.
Ruth: Yeah. First generation college students or first generation graduate students, students from racial ethnic minorities, students with disabilities, for example, I think it's important to kind of seek out mentors, find somebody who's doing the job that you think you might like to do and talk to them about it. Ask them about it, especially in kind of academia. I think for the most part, faculty are looking for people to mentor who are excited about being mentored. And so don't feel like you're wasting their time or it's like they're somehow I don't know, outside of out of reach, whatever. Reach out to them. There may be some jerks out there, but I think by a large, most faculty are very giving of their time. And if students are eager and want to learn, then that's true for me, at least I'm excited if somebody is excited and wants to learn and just trying to figure things out, I will be very giving up my time. So I would say yes, ask questions, find out and don't be afraid to change directions. You might decide don't give up too easily, but also don't be afraid to kind of change, you know pivot change directions if you discover that the path that you were planning on pursuing ends up not being what you hoped it would be or as fulfilling as you wanted it to be.
John: I definitely agree with that latter point. I think sometimes people hold themselves back from getting started because they feel like they have to know for 100% what they want to do before they start. And the best way to figure out what you want to do is to start doing stuff.
Ruth: Yeah, exactly. Just try it out and see how it goes. There's a balance between when to really knuckle down and try to overcome challenges. But if you're just feeling like if your heart is not in it, then maybe it's a good idea to change. But if it's just hard, don't give up yet. If your heart is in it, but it's hard, keep going.
John: Yeah. I was checking with Hamza, our director of technology today, about how you say at the end of the year, it has been a hard year. We've worked really hard this year. However, we've accomplished a lot, and you're never going to achieve anything worth doing without having moments where it's legitimately hard and having to power through it. There's no magic path that is just easy. It doesn't work like that. But as long as you feel engaged, then keep going. So that's good. Alright, anything else you'd like to say to our audience with? How can people get in touch with you?
Ruth: I'm on LinkedIn, and so I think you've got my LinkedIn. The show notes I suppose that's probably the best, easiest way to access.
John: Okay, and we'll put a link also for the Hope Lab. Who knows, maybe somebody decides to change. Hopefully, we don't think there's a lot of sensory science at the time. I do think that it's very interesting. I never really thought about the exclusion of people with developmental and intellectual disabilities from Sensory and Consumer Research, but that almost certainly has happened.
Ruth: Yeah. I mean, if there is somebody out there that's interested in exploring that and developing products or things like that and wanting to learn more how to kind of engage people with intellectual disabilities and things like that in the research process. I'd be stoked to collaborate.
John: And you mentioned universal design, the idea that if you develop a product, universal design comes out to the idea that things that are, say better for people with disabilities can be better for everybody. That's kind of a close version of it.
John: Same way here with products. So I think there's a lot, actually, I look forward to our future discussions that I'm sure we will have soon on this topic. But anyway, thanks a lot. Thanks for being on the show and I would say Happy Holidays to everyone, this is our last episode of 2021. It's been quite a journey. Thank you everyone who's been tuning in and thank you, Ruth, for being on the show.
Ruth: Yeah. Thank you for having me. My pleasure.
John: Okay, that's it. I hope you enjoyed this conversation. If you did, please help us grow our audience by telling your friend about AigoraCast and leaving us a positive review on iTunes. Thanks.
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