I recently organized a professional group called EPIC Data Scientists + Ethnographers along with a few others who are both data scientists and ethnographers. Our goal is to form a virtual community to discuss ways to incorporate ethnography and data science, just like I strive to do on this website.
If you are interested in working with others on this or simply interested in learning more, feel free to join. Whether you are both a data scientist and ethnographer, only one of them, or neither, we would love to hear your perspective.
Thank you, EPIC, for helping to develop this and giving us a platform.
Earlier this week, Matt Artz, Astrid Countee, and I ran a workshop at the American Anthropological Association’s 2020 annual conference entitled “Breaking into Tech.” We discussed strategies for anthropologists interested in working in the tech world.
Here is the presentation for anyone who might find it useful but could not attend:
In a previous article, I have discussed the value of integrating data science and ethnography. On LinkedIn, people commented that they were interested and wanted to hear more detail on potential ways to do this. I replied, “I have found explaining how to conduct studies that integrate the two practically is easier to demonstrate through example than abstractly since the details of how to do it vary based on the specific needs of each project.”
In this article, I intend to do exactly that: analyze four innovative projects that in some way integrated data science and ethnography. I hope these will spur your creative juices to help think through how to creatively combine them for whatever project you are working on.
Synopsis:
Project:
How It Integrated Data Science and Ethnography:
Link to Learn More:
No Show Model
Used ethnography to design machine learning software
Used ethnography to understand how users make sense of and behave towards a machine learning system they encounter and how this, in turn, shapes the development of the machine learning algorithm(s)
A medical clinic at a hospital system in New York City asked me to use machine learning to build a show rate predictor in order to inform an improve its scheduling practices. During the initial construction phase, I used ethnography to both understand in more depth understand the scheduling problem the clinic faced and determine an appropriate interface design.
Through an ethnographic inquiry, I discovered the most important question(s) schedulers ask when scheduling their appointments. This was, “Of the people scheduled for a given doctor on a particular day, how many of them are likely to actually show up?” I then built a machine learning model to answer this exact question. My ethnographic inquiry provided me the design requirements for the data science project.
In addition, I used my ethnographic inquiries to design the interface. I observed how schedulers interacted with their current scheduling software, which gave me a sense for what kind of visualizations would work or not work for my app.
This project exemplifies how ethnography can be helpful both in the development stage of a machine learning project to determine machine learning algorithm(s) needs and on the frontend when communicating the algorithm(s) to and assessing its successfulness with its users.
As both an ethnographer and a data scientist, I was able to translate my ethnographic insights seamlessly into machine learning modeling and API specifications and also conducted follow-up ethnographic inquiries to ensure that what I was building would meet their needs.
Project 2: Cybersensitivity Study
I conducted this project with Indicia Consulting. Its goal was to explore potential connections between individuals’ energy consumption and their relationship with new technology. This is an example of using ethnography to explore and determine potential social and cultural patterns in-depth with a few people and then using data science to analyze those patterns across a large population.
We started the project by observing and interviewing about thirty participants, but as the study progressed, we needed to develop a scalable method to analyze the patterns across whole communities, counties, and even states.
Ethnography is a great tool for exploring a phenomenon in-depth and for developing initial patterns, but it is resource-intensive and thus difficult to conduct on a large group of people. It is not practical for saying analyzing thousands of people. Data science, on the other hand, can easily test the validity across an entire population of patterns noticed in smaller ethnographic studies, yet because it often lacks the granularity of ethnography, would often miss intricate patterns.
Ethnography is also great on the back end for determining whether the implemented machine learning models and their resulting insights make sense on the ground. This forms a type of iterative feedback loop, where data science scales up ethnographic insights and ethnography contextualizes data science models.
Thus, ethnography and data science cover each other’s weaknesses well, forming a great methodological duo for projects centered around trying to understand customers, users, colleagues, or other users in-depth.
Project 3: Facebook Newsfeed Folk Theories
In their study, Motahhare Eslami and her team of researchers conducted an ethnographic inquiry into how various Facebook users conceived of how the Facebook Newsfeed selects which posts/stories rise to the top of their feeds. They analyze several different “folk theories” or working theories by everyday people for the criteria this machine learning system uses to select top stories.
How users think the overall system works influences how they respond to the newsfeed. Users who believe, for example, that the algorithm will prioritize the posts of friends for whom they have liked in the past will often intentionally like the posts of their closest friends and family so that they can see more of their posts.
Users’ perspectives on how the Newsfeed algorithm works influences how they respond to it, which, in turn, affects the very data the algorithm learns from and thus how the algorithm develops. This creates a cyclic feedback loop that influences the development of the machine learning algorithmic systems over time.
Their research exemplifies the importance of understanding how people think about, respond to, and more broadly relate with machine learning-based software systems. Ethnographies into people’s interactions with such systems is a crucial way to develop this understanding.
In a way, many machine learning algorithms are very social in nature: they – or at least the overall software system in which they exist – often succeed or fail based on how humans interact with them. In such cases, no matter how technically robust a machine learning algorithm is, if potential users cannot positively and productively relate to it, then it will fail.
Ethnographies into the “social life” of machine learning software systems (by which I mean how they become a part of – or in some cases fail to become a part of – individuals’ lives) helps understand how the algorithm is developing or learning and determine whether they are successful in what we intended them to do. Such ethnographies require not only in-depth expertise in ethnographic methodology but also an in-depth understanding how machine learning algorithms work to in turn understand how social behavior might be influencing their internal development.
Project 4: Thing Ethnography
Elise Giaccardi and her research team have been pioneering the utilization of data science and machine learning to understand and incorporate the perspective of things into ethnographies. With the development of the internet of things (IOT), she suggests that the data from object sensors could provide fresh insights in ethnographies of how humans relate to their environment by helping to describe how these objects relate to each other. She calls this thing ethnography.
This experimental approach exemplifies one way to use machine learning algorithms within ethnographies as social processes/interactions in of themselves. This could be an innovative way to analyze the social role of these IOT objects in daily life within ethnographic studies. If Eslami’s work exemplifies a way to graft ethnographic analysis into the design cycle of machine learning algorithms, Giaccardi’s research illustrates one way to incorporate data science and machine learning analysis into ethnographies.
Conclusion
Here are four examples of innovative projects that involve integrating data science and ethnography to meet their respective goals. I do not intend these to be the complete or exhaustive account of how to integrate these methodologies but as food for thought to spur further creative thinking into how to connect them.
For those who, when they hear the idea of integrating data science and ethnography, ask the reasonable question, “Interesting but what would that look like practically?”, here are four examples of how it could look. Hopefully, they are helpful in developing your own ideas for how to combine them in whatever project you are working on, even if its details are completely different.
This is a research paper I wrote for a master’s course on Applied Anthropology at the University of Memphis. The overall master’s program sought to train students in applied anthropology, and the goal of this course was to teach the foundations of what applied anthropology is, in contrast to other types of anthropology.
Even though I found the course interesting, its curriculum lacked the readings and perspectives of applied anthropologists in the business world. As I discuss in the paper, statistically speaking, a significant number of applied anthropologists (and a University of Memphis’s applied anthropology program alum) work in the business sector, so excluding them leaves out what might be the largest group of applied anthropologists from their own field. I wrote this essay as a subtle nudge to encourage the course designers to add the works of business anthropologists, particularly UX researchers, into their curriculum.
Due to the lack of resources by applied business anthropologists in the curriculum, I had to assemble my own resources entirely by myself. Other applied anthropologists have told me they have encountered this as well. So, hopefully, in addition to the essay potentially providing helpful analysis of applied business anthropology, its bibliography might also provide a starting collection of business anthropology resources for you to explore.
Data science’s popularity has grown in the last few years, and many have confused it with its older, more familiar relative: statistics. As someone who has worked both as a data scientist and as a statistician, I frequently encounter such confusion. This post seeks to clarify some of the key differences between them.
Before I get into their differences, though, let’s define them. Statistics as a discipline refers to the mathematical processes of collecting, organizing, analyzing, and communicating data. Within statistics, I generally define “traditional” statistics as the the statistical processes taught in introductory statistics courses like basic descriptive statistics, hypothesis testing, confidence intervals, and so on: generally what people outside of statistics, especially in the business world, think of when they hear the word “statistics.”
Data science in its most broad sense is the multi-disciplinary science of organizing, processing, and analyzing computational data to solve problems. Although they are similar, data science differs from both statistics and “traditional” statistics:
Difference
Statistics
Data Science
#1
Field of Mathematics
Interdisciplinary
#2
Sampled Data
Comprehensive Data
#3
Confirming Hypothesis
Exploratory Hypotheses
Difference
#1: Data Science Is More than a Field of Mathematics
Statistics is a field of mathematics; whereas, data science refers to more than just math. At its simplest, data science centers around the use of computational data to solve problems,[i] which means it includes the mathematics/statistics needed to break down the computational data but also the computer science and engineering thinking necessary to code those algorithms efficiently and effectively, and the business, policy, or other subject-specific “smarts” to develop strategic decision-making based on that analysis.
Thus, statistics forms a crucial component of data science, but data science includes more than just statistics. Statistics, as a field of mathematics, just includes the mathematical processes of analyzing and interpreting data; whereas, data science also includes the algorithmic problem-solving to do the analysis computationally and the art of utilizing that analysis to make decisions to meet the practical needs in the context. Statistics clearly forms a crucial part of the process of data science, but data science generally refers to the entire process of analyzing computational data. On a practical level, many data scientists do not come from a pure statistics background but from a computer science or engineering, leveraging their coding expertise to develop efficient algorithmic systems.
Difference
#2: Comprehensive vs Sample Data
In statistical studies, researchers are often unable to analyze the entire population, that is the whole group they are analyzing, so instead they create a smaller, more manageable sample of individuals that they hope represents the population as a whole. Data science projects, however, often involves analyzing big, summative data, encapsulating the entire population.
The tools of traditional statistics work well for scientific studies, where one must go out and collect data on the topic in question. Because this is generally very expensive and time-consuming, researchers can only collect data on a subset of the wider population most of the time.
Recent developments in computation, including the ability to gather, store, transfer, and process greater computational data, have expanded the type of quantitative research now possible, and data science has developed to address these new types of research. Instead of gathering a carefully chosen sample of the population based on a heavily scrutinized set of variables, many data science projects require finding meaningful insights from the myriads of data already collected about the entire population.
Difference
#3: Exploratory vs Confirming
Data scientists often seek to build models that do something with the data; whereas, statisticians through their analysis seek to learn something from the data. Data scientists thus often assess their machine learning models based on how effectively they perform a given task, like how well it optimizes a variable, determines the best course of action, correctly identifies features of an image, provides a good recommendation for the user, and so on. To do this, data scientists often compare the effectiveness or accuracy of the many models based on a chosen performance metric(s).
In traditional statistics, the questions often center around using data to understand the research topic based on the findings from a sample. Questions then center around what the sample can say about the wider population and how likely its results would represent or apply to that wider population.
In contrast, machine learning models generally do not seek to explain the research topic but to do something, which can lead to very different research strategy. Data scientists generally try to determine/produce the algorithm with the best performance (given whatever criteria they use to assess how a performance is “better”), testing many models in the process. Statisticians often employ a single model they think represents the context accurately and then draw conclusions based on it.
Thus, data science is often a form of exploratory analysis, experimenting with several models to determine the best one for a task, and statistics confirmatory analysis, seeking to confirm how reasonable it is to conclude a given hypothesis or hypotheses to be true for the wider population.
A lot of scientific research has been theory confirming: a scientist has a model or theory of the world; they design and conduct an experiment to assess this model; then use hypothesis testing to confirm or negate that model based on the results of the experiment. With changes in data availability and computing, the value of exploratory analysis, data mining, and using data to generate hypotheses has increased dramatically (Carmichael 126).
Data science as a discipline has been at the
forefront of utilizing increased computing abilities to conduct exploratory work.
Conclusion
A data scientist friend of mine once quipped to me that data science simply is applied computational statistics (c.f. this). There is some truth in this: the mathematics of data science work falls within statistics, since it involves collecting, analyzing, and communicating data, and, with its emphasis and utilization of computational data, would definitely be a part of computational statistics. The mathematics of data science is also very clearly applied: geared towards solving practical problems/needs. Hence, data science and statistics interrelate.
They differ, however, both in their formal definitions and practical understandings. Modern computation and big data technologies have had a major influence on data science. Within statistics, computational statistics also seeks to leverage these resources, but what has become “traditional” statistics does not (yet) incorporate these. I suspect in the next few years or decades, developments in modern computing, data science, and computational statistics will reshape what people consider “traditional” or “standard” statistics to be a bit closer to the data science of today.
For more details, see the following useful resources:
I recently integrated ethnography and data science to develop a Show Rate Predictor for an (anonymous) hospital system. Many readers have asked for real-world examples of this integration, and this project demonstrates how ethnography and data science can join to build machine learning-based software that makes sense to users and meets their needs.
Part 1: Scoping out the Project
A particular clinic in the hospital system was experiencing a large number of appointment no-shows, which produced wasted time, frustration, and confusion for both its patients and employees. I was asked to use data science and machine learning to better understand and improve their scheduling.
I started the project by conducting ethnographic research into the clinic to learn more about how scheduling occurs normally, what effect it was having on the clinic, and what driving problems employees saw. In particular, I observed and interviewed scheduling assistants to understand their day-to-day work and their perspectives on no-shows.
One major lesson I learned through all this was that when scheduling an appointment, schedulers are constantly trying to determine how many people to schedule on a given doctor’s shift to ensure the right number of people show up. For example, say 12-14 patients is a good number of patients for Dr. Rodriguez’s (made up name) Wednesday morning shift. When deciding whether to schedule an appointment for the given patient with Dr. Rodriguez on an upcoming Wednesday, the scheduling assistants try to determine, given the appointments currently scheduled then, whether they can expect 12-14 patients to show up. This was often an inexact science. They would often have to schedule 20-25 patients on a particular doctor’s shift to ensure their ideal window of 12-14 patients would actually come that day. This could create the potential for chaos, however, where too many patients arriving on some days and too few on others.
This question – how many appointments can we expect or predict to occur on a given doctor’s shift – became my driving question to answer with machine learning. After checking in with the various stakeholders at the clinic to make sure this was in fact an important and useful question to answer with machine learning, I started building.
Part 2: Building the Model
Now that I had a driving, answerable question, I decided to break it down into two sequential machine learning models:
The first model learned to predict the probability that a given appointment would occur, learning from the history of occurring or no-show appointments.
The second model, using the appointment probabilities from the first model, estimated how many appointments might occur for every doctors’ shift.
The first model combined three streams of data to assess the no-show probability: appointment data (such as how long ago it was scheduled, type of appointment, etc.); patient information, especially past appointment history; and doctor information. I performed extensive feature selection to determine the best subset of variables to use and tested several types of machine learning models before settling on gradient boosting.
The second model used the probabilities in the first model as input data to predict how many patients to expect to come on each doctors’ shift. I settled on a neural network for the model.
Part 3: Building an App
Next, I worked with the software engineers on my team to develop an app to employ these models in real time and communicate the information to schedulers as they scheduled appointments. My ethnographic research was invaluable for developing how to construct the app.
On the back end, the app calculated the probability that all future appointments would occur, updating with new calculations for newly scheduled or edited appointments. Once a week, it would incorporate that week’s new appointment data and shift attendance to each model’s training data and update those models accordingly.
Through my ethnographic research, I observed how schedulers approached scheduling appointments, including what software they used in the process and how they used each. I used that to determine the best ways to communicate that information, periodically showing my ideas to the schedulers to make sure my strategy would be helpful.
I constructed an interface to communicate the information that would complement the current software they used. In addition to displaying the number of patients expected to arrive, if the machine learning algorithm was predicting that a particular shift was underbooked, it would mark the shift in green on the calendar interface; yellow if the shift was projected to have the ideal number of patients, and red if already expected have too many patients. The color-coding allowed easy visualization of the information in the moment: when trying to find an appointment time for a patient, they could easily look for the green shifts or yellow if they had to, but steer clear of the red. When zooming in on a specific shift, each appointment would be color-coded (likely, unlikely, and in the middle) as well based on the probability that it would occur.
Conclusion
This is one example of a projects that integrates data science and ethnography to build a machine learning app. I used ethnography to construct the app’s parameters and framework. It tethered the app in the needs of the schedulers, ensuring that the machine learning modeling I developed was useful to those who would use it. Frequent check-ins before each step in their development also helped confirm that my proposed concept would in fact help meet their needs.
My data science and machine learning expertise helped guide me in the ethnographic process as well. Being an expert in how machine learning worked and what sorts of questions it could answer allowed me to easily synthesize the insights from my ethnographic inquiries into buildable machine learning models. I understood what machine learning was capable (and not capable) of doing, and I could intuitively develop strategic ways to employ machine learning to address issues they were having.
Hence, my dual role as an ethnography and data scientist benefitted the project greatly. My listening skills from ethnography enabled me to uncover the underlying questions/issues schedulers faced, and my data science expertise gave me the technical skills to develop a viable machine learning solution. Without listening patiently through extensive ethnography, I would not have understood the problem sufficiently, but without my data science expertise, I would have been unable to decipher which questions(s) or issue(s) machine learning could realistically address and how.
This exemplifies why a joint expertise in data science and ethnography is invaluable in developing machine learning software. Two different individuals or teams could complete each separately – an ethnographer(s) analyze the users’ needs and a data scientist(s) then determine whether machine learning modeling could help. But this seems unnecessarily disjointed, potentially producing misunderstanding, confusion, and chaos. By adding an additional layer of people, it can easily lead to either the ethnographer(s) uncovering needs way too broad or complex for a machine learning-based solution to help or the data scientist(s) trying to impose their machine learning “solution” to a problem the users do not have.
Developing expertise in both makes it much easier to simultaneously understand the problems or questions in a particular context and build a doable data science solution.
A friend and fellow professor, Dr. Eve Pinkser, asked me to give a guest lecture on quantitative text analysis techniques within data science for her Public Health Policy Research Methods class with the University of Illinois at Chicago on April 13th, 2020. Multiple people have asked me similar questions about how to use data science to analyze texts quantitatively, so I figured I would post my presentation for anyone interested in learning more.
It provides a basic introduction of the different approaches so that you can determine which to explore in more detail. I have found that many people who are new to data science feel paralyzed when trying to navigate through the vast array of data science techniques out there and unsure where to start.
Many of her students needed to conduct quantitative textual analysis as part of their doctoral work but struggled in determining what type of quantitative research to employ. She asked me to come in and explain the various data science and machine learning-based textual analysis techniques, since this was out of her area of expertise. The goal of the presentation was to help the PhD students in the class think through the types of data science quantitative text analysis techniques that would be helpful for their doctoral research projects.
Hopefully, it would likewise allow you to determine the type or types of text analysis you might need so that you can then look those up in more detail. Textual analysis, as well as the wider field of natural language processing within which it is a part of, is a quickly up-and-coming subfield within data science doing important and groundbreaking work.
This is a follow-up to my previous article, “What Is Ethnography,” outlining ways ethnography is useful in professional settings.
To recap, I defined ethnography as a research approach that seeks “to understand the lived experiences of a particular culture, setting, group, or other context by some combination of being with those in that context (also called participant-observation), interviewing or talking with them, and analyzing what is produced in that context.”
Ethnography is a powerful tool, developed by anthropologists and other social scientists over the course of several decades. Here are three types of situations in professional settings when I have found to use ethnography to be especially powerful:
1. To see the given product and/or people in action
2. When brainstorming about a design
3. To understand how people navigate complex, patchwork processes
Situation
#1: To See the Given Product and/or People in Action
Ethnography allows you to witness people in action: using your product or service, engaging in the type of activity you are interested, or in whatever other situation you are interested in studying.
Many other social science research methods involve creating an artificial environment in which to observe how participants act or think in. Focus groups, for example, involve assembling potential customers or users into a room: forming a synthetic space to discuss the product or service in question, and in many experimental settings, researchers create a simulated environment to control for and analyze the variables or factors they are interested in.
Ethnography, on the other hand, centers around observing and understanding how people navigate real-world settings. Through it, you can get a sense for how people conduct the activity for which you are designing a product or service and/or how people actually use your product or service.
For example, if you want to understand how people use GPS apps to get around, one can see how people use the app “in the wild:” when rushing through heavy traffic to get to a meeting or while lost in the middle of who knows where. Instead of hearing their processed thoughts in a focus group setting or trying to simulate the environment, you can witness what the tumultuousness yourself and develop a sense for how to build a product that helps people in those exact situations.
Situation
#2: When Brainstorming about a New Product Design
Ethnography is especially useful during the early stages of designing a product or service, or during a major redesign. Ethnography helps you scope out the needs of your potential customers and how they approach meeting said needs. Thus, it helps you determine how to build a product or service that addresses those needs in a way that would make sense for your users.
During such initial stages of product design, ethnography helps determine the questions you should be asking. Many have a tendency during these initial stages to construct designs based on their own perception of people’s needs and desires and miss what the customers’ or users’ do in fact need and desire. Through ethnography, you ground your strategy in the customers’ mindsets and experiences themselves.
The brainstorming stages of product development also require a lot of flexibility and adaptability: As one determines what the product or service should become, one must be open to multiple potential avenues. Ethnography is a powerful tool for navigating such ambiguity. It centers you on the users, their experiences and mindsets, and the context which they might use the product or service, providing tools to ask open-ended questions and to generate new and helpful ideas for what to build.
Situation
#3: To Understand How People Navigate Complex, Patchwork Processes
At a past company, I analyzed how customer service representatives regularly used the various software systems when talking with customers. Over the years, the company had designed and bought various software programs, each to perform a set of functions and with unique abilities, limitations, and quirks. Overtime, this created a complex web of interlocking apps, databases, and interfaces, which customer service representatives had to navigate when performing their job of monitoring customer’s accounts. Other employees described the whole scene as the “Wild West:” each customer service representative had to create their own way to use these software systems while on the phone with a (in many cases disgruntled) customer.
Many companies end up building such patchwork systems – whether of software, of departments or teams, of physical infrastructure, or something else entirely – built by stacking several iterations of development overtime until, they become a hydra of complexity that employees must figure out how to navigate to get their work done.
Ethnography is a powerful tool for making sense of such processes. Instead of relying on official policies for how to conduct various actions and procedures, ethnography helps you understand and make sense of the unofficial and informal strategies people use to do what they need. Through this, you can get a sense for how the patchwork system really works. This is necessary for developing ways to improve or build open such patchwork processes.
In the customer service research project, my task was
to develop strategies to improve the technology customer service representatives
used as they talked with customers. Seeing how representatives used the
software through ethnographic research helped me understand and focus the analysis
on their day-to-day needs and struggles.
Conclusion
Ethnography is a powerful tool, and the business world and other professional settings have been increasingly realizing this (c.f. this and this ). I have provided three circumstances where I have personally found ethnography to be invaluable. Ethnography allows you to experience what is happening on the ground and through that to shape and inform the research questions we ask and recommendations or products we build for people in those contexts.
“Data science is doable,” a fellow attendee of the EPIC’s 2018 conference in Honolulu would exclaim like a mantra. The conference was for business ethnographers and UX researchers interested in understanding and integrating data science and machine learning into their research. She was specifically trying to address a tendency she has noticed– which I have seen as well: qualitative researchers and other so-called “non-math people” frequently believe that data science is far too technical for them. This seems ultimately rooted in cultural myths about math and math-related fields like computer science, engineering, and now data science, and in a similar vein as her statement, my goal in this essay is to discuss these attitudes and show that data science, like math, is relatable and doable if you treat it as such.
The “Math
Person”
In the United States, many possess an implied image of a “math person:” a person supposedly naturally gifted at mathematics. And many who do not see themselves as fitting that image simply decry that math simply isn’t for them. The idea that some people are inherently able and unable to do math is false, however, and prevents people from trying to become good at the discipline, even if they might enjoy and/or excel at it.
Most skills in life, including mathematical skills, are like muscles: you do not innately possess or lack that skill, but rather your skill develops as you practice and refine that activity. Anybody can develop a skill if they practice it enough.
Scholars in anthropology, sociology, psychology, and education have documented how math is implicitly and explicitly portrayed as something some people can do and some cannot do, especially in math classes in grade school. Starting in early childhood, we implicitly and sometimes explicitly learn the idea that some people are naturally gifted at math but for others, math is simply not their thing. Some internalize that they are gifted at math and thus take the time to practice enough to develop and refine their mathematical skills; while others internalize that they cannot do math and thus their mathematical abilities become stagnant. But this is simply not true.
Anyone can learn and do math if he or she practices math and cultivates mathematical thinking. If you do not cultivate your math muscle, then well it will become underdeveloped and, then, yes, math becomes harder to do. Thus, as a cruel irony someone internalizing that he or she cannot do math can turn into a self-fulfilling prophecy: he or she gives up on developing mathematical skills, which leads to its further underdevelopment.
Similarly, we cultivate another false myth that people skilled in mathematics (or math-related fields like computer science, engineering, and data science) in general do not possess strong social and interpersonal communication skills. The root for this stereotype lies in how we think of mathematical and logical thinking than actual characteristics of mathematicians, computer scientists, or engineers. Social scientists who have studied the social skills of mathematicians, computer scientists, and engineers have found no discernable difference in social and interpersonal communication skills with the rest of the world.
Quantitative and Qualitative Specialties
Anyone can learn and do math if he or she practices math and cultivates mathematical thinking.
The belief that some people are just inherently good at math and that such people do not possess strong social and interpersonal communication skills contributes to the division between quantitative and qualitative social research, in both academic and professional contexts. These attitudes help cultivate the false idea that quantitative research and qualitative research are distinct skill sets for different types of people: that supposedly quantitative research can only be done “math people” and qualitative research by “people people.” They suddenly become separate specialties, even though social research by its very nature involves both. Such a split unnecessarily stifles authentic and holistic understanding of people and society.
In professional and business research contexts, both qualitative and quantitative researchers should work with each other and eventually through that process, slowly learn each other’s skills. If done well, this would incentivize researchers to cultivate both mathematical/quantitative, and interpersonal/qualitative research skills.
It would reward professional researchers who develop both skillsets and leverage them in their research, instead of encouraging researchers to specialize in one or the other. It could also encourage universities to require in-depth training of both to train their students to become future workers, instead of requiring that students choose among disciplines that promote one track over the other.
Working together is
only the first step, however, whose success hinges on whether it ultimately
leads to the integration of these supposedly separate skillsets. Frequently,
when qualitative and quantitative research teams work together, they work mostly
independently – qualitative researchers on the qualitative aspect of the
project and quantitative researchers on the quantitative aspects of the project
– thus reinforcing the supposed distinction between them. Instead, such
collaboration should involve qualitative researchers developing quantitative
research skills by practicing such methods and quantitative researchers similarly
developing qualitative skills.
Conclusion
Anyone can develop mathematics and data science skills if they practice at it. The same goes with the interpersonal skills necessary for ethnographic and other qualitative research. Depicting them as separate specialties – even if they come together to do each of their specialized parts in a single research projects – functions stifles their integration as a singular set of tools for an individual and reinforces the false myths we have been teaching ourselves that data science is for math, programming, or engineering people and that ethnography is for “people people.” This separation stifles holistic and authentic social research, which inevitably involves qualitative and quantitative approaches.
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