Quantitative Research
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Everybody to this PhD life, the podcast devoted to having casual conversations about research, being a researcher and all the stuff between. I'm your host, Alyssa Cortez Kennedy. And today we have the very smart, the very special and serious scholar, Dr. Sandra Guzman Foster.

I'm the only one clapping, but we're all happy that you're here. How are you doing today? I'm doing pretty good. I wish it wasn't so hot outside, but I'm doing pretty well. Thank you. Welcome to South Texas. So we are so excited because this is our inaugural and very first episode of this podcast. And I wanted to share a little bit about why we wanted to create this.

This podcast is being created for a intro to research course at the university that we are a part of. But why don't you share a little bit about why it's important that scholars hear about research in this context?

Well, I think that's important that they get used to and understand why it's important to articulate research terminology, especially when they're ready to get out in the real world, whether they decide to do academic or non-academic careers when they finish their doctoral programs or even the master's students when they finish their programs. You have to be able to explain and describe what you're doing to both the students

expert and the person that doesn't understand the technical jargon as well. So you have to know who your audience is. And so you have to know how to talk about your research depending on your audience.

Absolutely. I 100% agree with you. I think being at the end of this program, as far as the coursework, it's been a really interesting journey to be putting all this information in files, right, in our brain. And then all of a sudden they're like, okay, tell me about it. And then you go out and you're like, um, I know it's in there somewhere. Yeah.

Right. And so you have to get used to not having notes in front of you and do your best as possible to be able to articulate what you're doing so the other person can understand where you're coming from. Absolutely. I 100 percent agree. So we're going to devote this podcast to talking about quantitative research method. And before we get started with the topics today, why don't you just give us a little bit of background about what you've done in quantitative research, Dr. Guzman-Foster?

My quantitative background is not as large as my qualitative background. However, I am starting to entertain the thought of doing mixed methods, which we'll talk about later in another podcast. But for me, quantitative research goes back to when I was in my doctoral program and when I was looking at high six testing scores among students here in the state of Texas.

So what I did was I learned how to look at large data sets and what that meant when they disaggregated data among students' demographics, such as ethnicity, race, grade level, age, even where they live, so their neighborhoods, so their zip codes. So that taught me a lot about where we get the numbers from and what you can do with the numbers and how you can investigate further what those numbers mean. So quantitative data to me is...

It helps me understand the statistics behind what's happening in the world. So patterns, trends, those things like that, that kind of give you an idea of how it impacts society and the population. Because remember, quantitative research is about making sure you have a great sample of the population you're looking at. Mm-hmm.

And I think that's something with quantitative research that we've been witnessing, we've been seeing and hearing a lot more about. I know in my company, not getting too far ahead in the conversation, but my personal experiences working in a large company is,

There are a lot more discussions about quantitative being that you're trying to get a point across with knowing the majority. What do people like? What do they not like? And that's usually the context we see it in. We see it in a lot of popular media.

media, you know, in commercials or in stores with, you know, who's buying what and who's doing what. But I don't think we ever really think about it in a larger context as to what we've been seeing with this pandemic. So that's going to really move us into a large portion of this podcast is talking about why

quantitative research is so important in a realist world, the world that we're living in every day.

One of the experiences that I think it's interesting is within my own company, they're actually using quantitative in your title. So we have a whole department that is devoted to quantitative methods. And so their title is quantitative analyst. And I found that so funny because it's like they've been there all along, but using the word quantitative is becoming very popular. Why do you think that is?

In my experience, that's because quantitative research is seen as a golden rule when it comes to any kind of research. It's been like that for many, many, many years because of organizations or entities such as the National Science Foundation, IES, Institute of Educational Researches. So big companies like that that actually fund projects out there that they want to see what the impact is of whatever it is, an intervention of some sort, a product of some sort.

To those people, numbers are important, and that's why quantitative research seems to be the golden rule. Not to say that everything else is not as important, but when you look at, even when I go back to my experience working at McCurl, which is a nonprofit company, and we did research all around the world in K through graduate school,

Whenever we apply for grants, they always insisted that we had some kind of quantitative piece and angle to it. Otherwise, we wouldn't get funded. And it also depends on the leader of our country. Many times people don't realize that whoever is leading the country and their own paradigms impact how they fund organizations like IES or the National Science Foundation. And if they believe quantitative research has more potential to change policy, then that's what they're going to be looking for when anybody else is getting funding.

Absolutely. I think there's a huge political aspect to any type of organization or business, and it definitely does affect how those monies or just the ideology of is it important how those terms are distributed and how important we really use them or we take them.

What would you say is something that we can do as scholars to show that quantitative research is important and how can we use that in our daily life?

I think that anytime you people don't realize, like you said earlier, that it is in our lives. So I find it interesting that you said that people in your company are getting titles. But it's kind of funny if everybody in the world, you know, identify themselves and tell themselves. But anything from like if you're looking for you think about it, we use demographics and information statistics when we're buying new things, a car, a house.

When we're looking at people who have kids, you know, the neighborhoods that they live in, what does that zip code say about the kind of education that's offered at that school? What are the tests? I mean, I have a friend who's a realtor and she says the first thing people ask is, what are the test scores of kids in this neighborhood? Has it been identified as an exemplary school? So those kind of statistics come into play even in major decisions like that where you're going to move or you're going to send your child to school.

but also more importantly in your health, right? So like my father has diabetes. And so a lot of times I'm always looking at what the research says about diabetes to make sure that he's eating correctly, that he's getting the same amount of exercise he needs. Not only that, it's hereditary. So I had to do my best to prevent it from happening in my life. And that's what I do. So I use it that way also to kind of guide me to what I need to do to make sure that I stay healthy.

Why do you think it is that quantitative research is more popular than our second topic? And we'll get into this next week with qualitative, but why do you think quantitative is what people go to?

I think people are more comfortable with seeing numbers and seeing the stories behind the numbers. I think numbers provide a safe way to look at a bigger picture and not fine tune into the smaller picture and who those actual participants that are participating in. I think when you don't see a face, it doesn't make it as personal, but, but the fact that the numbers there tells you something about the population you study. Yeah, absolutely. I would agree with you. You brought up a really good, um, uh,

topic about how when we go shopping and we, you know, we're looking at, is it one star or is it five stars? And we really do go off of that, not only as scholars, but as a general public, you know, we walk into Best Buy or you're shopping online and they put the stars on there as far as a rating. Is this product a one through five?

And of course, we're going to go always for the five, but for the lowest amount of money. Exactly. Right. There's so many different facets of quantitative research that surround us every single day. And I don't think we...

we subconsciously, we don't really see it until we actually see it, until we're paying attention. And I think as a novice researcher coming in, it can be so overwhelming to think, how am I going to do this research? How do I know I have enough data? Is this data valid? Is it true to what it's trying to tell me? Have you ever ran into any issues where you kind of questioned your data? Yeah.

Yes, and that's something that I think that researchers kind of struggle with is that data is data. So you have to always go into a research project not knowing what you're going to find. You may have an idea, but that bias is you need to step away from that bias. So you go about your project or your study and whatever data you find, yes, you're going to question it because that's the whole point for the first time when you came up with your research question.

But just because it doesn't come out the way you predicted it was going to come out, that's okay. It's telling you another story. Data is data. So no one should ever get discouraged if they think that their outcomes and the results painted a totally different picture than they thought. It just means you found something that you were not expecting, which is good. How often would you say that happens?

I think it happens a lot, especially when you start off with a question and you're thinking, is this really what I was trying to find? Now, with quantitative research, you can't change the question. Once you start, that's the question where it's different for qualitative research. And we'll talk about that the next time. But with quantitative research, you have a hypothesis and you're trying to figure out if the hypothesis is true or not. And so it's very difficult for anyone to

want to change it in the middle of it, right? So you have to move forward with it. And I think it happens quite often because these surprises come up. But I think that instead of seeing it as a negative thing, it should be considered as a surprising positive thing, because again, you didn't expect it. And to me, personally, when you don't expect it, to me, that's new data that you can share with people who may not have expected either if they actually replicated your study. Yeah.

Have you ever seen just a really bad quantitative project? Yeah. Yes. I think one of the most difficult things, and one of the, I think there's two stories to this. A lot of students think that survey research, which is considered quantitative research, but you can also have open-ended research,

which makes, you know, again, we'll get to that later. But most of it is based on statistics. And so when they do survey data, they don't have a large population sample. And that is the downfall. So when you, so for example, I, the other day when I heard on the news that one of the companies were coming out with a vaccine, they said they had a participancy.

And I was like, okay, did I hear that correctly? That's a little low. Exactly. Especially if you're looking for a vaccine that's supposed to help people worldwide. For like 5 billion people. Right. So eight is not exactly a good representation of the population. So...

When you hear that kind of thing, those kinds of things, you're like, wait a minute, what exactly are they really saying? It was just too small of a sample size for them to even announce it. I was almost embarrassed for them because I was like, I would never share that publicly because that is not a good way to start a study with a small sample size. Yeah. This is not an exploratory case study. Right. Exactly. Exactly.

And the further you get along as a researcher, you'll completely understand that joke. But, you know, you bring up such a great point is that

I think as novice researchers, so I'll talk from the perspective where I'm at in my program, still as a doctoral student, being at the end and actually doing a pilot study, you go in and you're like, oh, I'll interview five. And then you send out five surveys and you get one back.

Or you go in and you say, I'll send out 100. That will for sure be enough. And you only get 20 back. That is very time consuming. And it's almost alarming when you realize you're not getting responses. And it can be very scary. What would you say to that?

I would say that you just have to really throw a wider net because you're right. You don't know what the response rate is going to be, and that can be very disappointing and frustrating. But the larger net you throw out, the more likely you'll be able to get more responses. Now, when I was studying quantitative research, I was told that if you get at least 33% return rate, then you're to a good start.

Of course, you want more than that. It'd be nice to have 53% or above. But for a novice researcher, I think 33% is okay to start with because you're just learning how to do that kind of research. So don't be alarmed. Don't be frustrated, but just throw out a wider net. Do you think people tend to choose the method versus letting the research lead to the method?

Yes, and I think that's one of our downfalls as researchers is that we don't let the question, because remember, traditionally, some of us have been taught different ways. And that's kind of frustrating about it is that a lot of people are taught different ways because it depends on your instructor's epistemology and ontology, right, and how they view the world.

So for some students, it could be confusing. However, if you receive a variety of information, then you get to choose which one works best for you. But I always say that the research question drives the design and the approach and the methodology. So what question are you trying to answer and what is the best way to get the results that you're trying to answer? And to me, the question drives because when I hear you say, I want to do a quantitative study, I go, well, what's your question?

And it just doesn't make sense if you don't know what your question is before you decide what kind of design we're going to use. Yeah. And I've seen that and experienced that with a lot of my peers that have already gone through dissertation or they're thinking about what advanced course they're going to take. Well, am I going to take advanced qual or am I going to take advanced quant?

And they choose it based off of, well, I want to do a quantitative study because I'll graduate faster. And it's kind of like, I don't think that's really going to work out that way. But go ahead and run with that. And that is a common perception that people do choose that for that one reason. You know, reflecting back when I did my research, it took me a year to

to a year, basically about a year and two months before I finally analyzed it, wrote up my report. But to me, that's not that long because I have to spend time in the field, which is expected, because my question had to be answered with me spending time in the field and then doing my transcription and analyzing my data. But it was a qualitative study. So again, I've met people who've done quantitative studies and it's taken them the exact same amount of time.

So it just depends on your question and what it is you need to do to actually get to the results of that question. Yeah. And I think one of the best pieces of advice that I've gotten throughout this whole program is from you with that exact answer is don't try to lean towards one or the other because I think what I have learned is what makes a good researcher a good researcher is a researcher being well-rounded in each area of research. Right.

And, you know, it's not a race. It's a light jog to finish your PhD and for others, maybe a very quick walk. Yeah.

But that, but, and it's not to call anyone out or to make anyone feel sorry, but it's just, it's a reality that I think as, as novice researchers, we have to stop and really look, why am I really doing this? Why am I pursuing a PhD specifically a quantitative or a qualitative project? Why, why am I doing this? And if you can't answer that, then why,

the passion is going to have to come from somewhere else.

Exactly. And that's what I try to always encourage students to do. Your goal is not to be a mini Dr. Guzman Foster, a mini Dr. Sinclair, a mini Dr. Ortiz. Your goal is to become you. What is it that you're passionate about and that you want to study and research? And if the question leads to a quantitative design, then go for it. This is a learning experience that you will never forget. I promise. Your dissertation stage is something that will be with you forever and ever. And

And I don't regret one day that I learned so much from my committee members and so much from my mentors. And I'm so glad that I did what I did. So how do we bring this all together? You know, how do we say in right now in today's world? I mean, we have so much going on. The political climate, the fight for equality, COVID-19,

How do you see all of this quantitative research working in today's world? Let's start with COVID. What have you seen with COVID that they're integrating quantitative research? What I see, and again, you have to always check your sources because you want to make sure you're getting the right information. So one of the things as researchers, we always have to make sure we're actually critiquing what we're hearing, what we're seeing, and look at the sources. So even though there are

counting all these numbers of COVID cases, they're still missing, missing data because there's people that are not being accounted for. And that is a problem because if you want a true picture of what's happening, you've got to try to figure out how to include all that kind of data.

So if we truly want to know what we're facing as a community, as a country, as a global community, we need to really know where those numbers are. And so I think of many people who live out way out in the rural areas or people who live in like indigenous folk communities that live out in, you know, like in Brazil, who's, you know, right now suffering a large amount of the same thing in Mexico.

So those kinds of things, you need to know the true numbers so you can know exactly what you can need to do to help them. Hence, we're turning to vaccine now, right? So that study that said they only had eight participants frightened me.

But we need to make sure that people are doing what they need to do. And if they're testing the vaccines on actual people, which they are, they need to make sure they're doing it on people that represent our population, not just a few in this one tiny little city in the United States, but the whole entire population, because we all have different lifestyles. And this disease is impacting people in many different ways. Some of them are getting it faster. Some are not. Some are recovering faster. Some are not. Those kinds of things you need to take into consideration.

So that's how quantitative can help us, but they have to make sure they have the correct sample and they have to make sure they have the right population representation. Yeah. And, you know, we've heard it so many times that a large majority of the population that is missing is people of color, Black, Mexican, Asian American. You know, there's so many other populations that are not being Native Americans. Sure.

huge, a huge population. And it's so unfortunate because we are losing in a battle that we can actually be winning if we were doing this testing correctly. Exactly. And then also it's related to policy. So why aren't those people is it obviously healthcare is not is not affordable, and they're not it's not accessible in many of these populations.

So we have to look at economics. You know, who can actually afford to go see a doctor? Are we offering service for those who can't? And how do we get it to them? Because, again, I just now heard the governor talk about how they're going to start having testing locations for people who don't have jobs and can't afford it. So we've had this since how long? And we're just now thinking about that. So, you know, we need to look at everything involved, not just at what the numbers are and how many vaccines we can make.

Yeah, absolutely. And I think I think he hit the nail on the head when he said, you know, we're not testing everybody. We're never going to have a true idea of how many cases there ever were at one point. It's very possible that there's been so many more recoveries or infections that just never were asymptomatic. They never had any symptoms yet.

And it's a very tough situation to be in when you're trying to figure out how do we help people with a vaccination. And so I think it takes a whole community and starting small and checking on your family and checking on your friends. And if they don't have access to that, how do we get them access? Take it upon yourself to figure it out and moving forward because there's a lot of people who are suffering from this. And unfortunately, now we're seeing it

And not in third world countries, of course, but even in countries that are modernized and have access to health care that we can't get them the testing. Exactly. Exactly right. Our numbers are all over the place. I actually have a friend who started doing her. She's in the program, but she started taking her own, making her own histograms of what

of the numbers. So when the mayor gives out his numbers, she actually like moves them around and does her own math to figure out how many actual cases compared from the day before and a month before. And she even found some really interesting data that how they, they moved them around.

But that's a whole nother conversation we can have. But that's exactly what she needs to do, especially if you're a scholar and that's how you see the world through numbers. Yeah. That's actually a great practice to do on your own.

So let me ask you this, moving forward and thinking about COVID, how do you think, and we're going to talk, I'm so excited for our next episode with Qualitative, because you and I both lean towards that. We're not supposed to be biased, but we are. Right, exactly.

But how do you see us being able to help those populations? Like we talked about Native Americans, African-American, Hispanics, Latinx, you know, accounting for all of the Central American countries. How do you see us being able to get data and possibly just having to use qualitative to do that?

I think that it's important that people understand that you have to be invited into some of those communities. You can't just go as a researcher and say, hey, I just want to talk to you guys. It takes time because they have to trust you. And I know that's really hard for people to understand, but that is actually a given because if I, you know, I'm not going to trust everybody. And so if you want me to answer some questions, tell me a little about who you are and why you're doing this and how is this going to benefit my community?

So the idea is that you have to have some kind of knowledge of those communities and the challenges that they are facing and not judge them. I mean, because we do tend to judge,

and just speak to them through compassion, right? So we want to know what's going on. Why do you think that, you know, you're not getting the services that other people are getting and how can we help you? So I need to talk to you and you need to tell me what's going on in your community and then other communities can do the same and then you can come up with data that can actually help, you know, kind of

draw up a plan of action to help these communities because they deserve it too. Just because they don't have the economic resources that other people have doesn't mean we're going to just let them die. And that's something we have to think about as humanity. And I know that's hard because funding has to come from somewhere.

And again, when you talked about the political ideologies, that's where that kind of interferes. Yeah. And I think that's the biggest difference. You know, when we're talking about quantitative research is that understanding quantitative research is understanding that you're usually sending a survey to somebody and they're filling out a bubble or putting a number by, you know, a close ended question and,

but it's, there's no face to that actual person. And that's the biggest difference with quantitative and qualitative research is that, uh,

quantitative is primarily used to give data, give a solid, well, I'm not going to use the word solid, that's not scholarly, an idea of what the population or what the community is saying, but it's a generalized idea. It's not specific to one person.

Exactly. And that's why people are so adamant that people fill out the census, right? Because that's how we decide who needs as a population and a community, we decide who needs those extra resources. At least that's what the intention is. Now, whether that really happens is another conversation. Yeah.

So that's why you see all these people saying, fill out your census, fill out your census. We could have so many conversations. Exactly right. So again, the conversation, but that's how people are using quantitative data in that situation with the census. Yeah. What would you say leaving this conversation? Why is quantitative, you know, why do you appreciate quantitative research?

I appreciate quantitative research because it does, it complements other methodologies as well. I don't ever discount it. It's just my questions have never really led to me doing more quantitative than qualitative. However, I do see the advantages of using quantitative because again, it complements other methodologies. What advice would you give students, novice students, and also PhDs and researchers on the field right now doing this type of work?

I would say don't be afraid. It's a learning experience and go for it. Eventually you'll get used to it. I was terrified of numbers and quantitative research when I was a doctor. But once I learned and actually applied it and practiced it, the fear went away and I felt more confident. And that's the reason why I feel like I can do quantitative research today.

Yeah, I feel the same exact way. I think after going through your courses and talking to people about this, finding not only a mentor, but also, you know, someone you connect really well with in class and that you can confide in and talk about the different types of research styles, specifically quantitative, because you have to learn how to use all these different programs and

input data and just being around it. It's like learning a language. When you're emerged into a situation, you're bound to pick it up at some point. You hit the nail on the head when you say it's another language. Exactly. It is. And it's not hard to learn, but if you don't practice it, you'll never learn it. Yeah. So I think what we can take away from this whole conversation is, you know, it's definitely a learning experience and it's ever-changing. There's

Yeah, you can make mistakes, but if you're not, if you don't ask the right questions and you don't, you know, see the difference between A and B, you're not going to be able to move forward. You have to be very open-minded to, you know, using quantitative and it's an excellent method to use. Obviously they're doing it for some reason and it's been around for a long time. And I don't think it's going to go away anytime soon. Do you? No, I do not. I do not think it's going to go away.

Well, I want to thank you so much for coming on. And I really appreciate this discussion. I think everyone who listens to this episode is going to really enjoy this conversation. And we want to give you the opportunity to ask us some questions. If there's anything in this episode that stood out to you, agree, disagree, or you're just totally confused on, let us know.

On our next episode, we're going to have a lightning round of questions. And so you will be able to ask the questions and we will give you a very complex or simple answer. I don't know how that's going to go, but we want to give you that opportunity. And so we look forward to hearing from you.

Dr. Guzman Foster, thank you so much for being with me today. I was happy to be here. I enjoyed it and I look forward to future podcasts. We will see you on the flip side, folks. Have a great day. Thank you too. Bye.