I am a dissertating doctoral candidate in sociology at the University of Chicago, research associate at Northeastern University, and CEO of Volunteer Scinece. What ties these three roles together is my interest in studying how to make organizations more effective. I am currenlty wrapping up my dissertation in which I performed a field study of a highly effective charter school. I wanted to understand how they managed to be effective despite a almost forty years of ineffectual school policy. I trace the challenges they confronted to improving their culture, teacher development, and test performance and show how despite their strong organizational capacity, they struggle to translate scientific knowledge into organizational best practices.
I pursue my interest in organizational effectiveness by studying the determinants of team, network, and organizational performance at the Lazer Lab at Northeastern University. I originally joined the Lazer Lab at in 2014 to work on a project to build an online lab facilities for the social sciences. I led the initiative to validate the platform by selecting a dozen studies from across the social and behavioral sciences to replicate and recruiting tens of thousands of volunteers to participate in them. The results were published in Social Psychology Quarterly. Today, we are using Volunteer Science to design and build experiments where we examine which groups, networks, and organizations perform best on different types of tasks. And in 2019, we spun the Volunteer Science technology out of Northeastern to bring it to more researchers looking to put their theories and ideas to the test.
In my role as CEO of Volunteer Science, I oversee all aspects of building a company around the technology. I work with researchers at universities in Boston and around the globe to build new studies, design infrastructure, and apply for funding. I lead a team of developers and marketing staff to design and build the technology in conversation with our users, both researchers and volunteers. In the past five years, I have managed over $5 million in grant- and contract-based research funding for work conducted on the website.
I grew up on a farm, though my family no longer needed it for sustenance by then. My grandfathers repaired machines and my grandmothers worked office jobs for the school system and cotton mill. My parents worked for the hospital and post office. We grew tobacco when I was really little and then we had cows. It's a tree farm now. Anyway, that's where I grew up, with my brother, sharing pastureland with my grandparents and an aunt, two uncles, and a cousin. The whole extended family lived within a twenty minute drive.
This was all just outside Henderson, NC (Not the in the mountains, that's Hendersonville (apparently this is even confused on Wikipedia). Their apple festival is nice though, or so I've heard. People I've met from there are all nice too.) Henderson is an old textile mill town that's now dominated by health care and government service work. My whole family is from this place. I've dawdled in my ancestry and nearly all branches of my family arrived to the new world in the mid 17th century, got to Henderson and farmed the hell out of it for 300 years. I took an DNA test from Ancestry.com once and it told me I had more British DNA than most people living in Britain.
I went up through Vance County Schools and the North Carolina School of Science and Math and then went to Appalachain State University. I lived in the mountains for five years and never skiied once though I grew up doing it every year. I was busy reading philosophy, which is what I wanted to do. As best I can remember, I never was ambitious growing up. But that changed abruptly in a story I won't relay here. Anyhow, once I landed at App State I decided I would get a Ph.D. and try to get into the Harvard of philosophy programs. I read Nietszche, Foucault, Derrida, Martha Nussbaum, Peter Singer. And I even made a second major out of taking all of the theory classes in every department. That's how I came to have a degree in philosophy and interdisciplinary studies. I was all set to begin my applications for doctoral programs when I did my first empirical study. After that, in my fourth year of college, I decided to apply for sociology programs. I got into the MAPSS program at UChicago and the rest is curriculum vitae.
This is a terrible picture. First, you shouldn't let the zucchini get that big. It loses its flavor. Second, it's not even my zucchini. It's from my uncle who still uses the farmland to grow stuff on occasion. But, it's a happy picture signifying our love of growing things we can eat.
Also, to clarify, I'm not in this picture. The woman in this picture is not my alter ego. She's more like my ego's alter.
I make my own cheese. After a year of trying, I'm starting to get recognizable flavors.
If you catch me typing into my phone on the T, this is what I'm doing. Writing was originally a way for me to give live to some of the ideas that I thought were interesting. Now, I'm three years into a novel I've decided should be a ten year process during which I actually learn how to write. But, I'm also just over 10,000 words into a second novel exploring the moral line between ecological engineering and ecoterrorism. In both books I find I am simply unable to be cynical about humanity. Though I'm fine not resolving moral grey areas. Besides the books, I have a tryptich of short stories imagining the implications of the ability to continue about our lives while we sleep (imagine being able to working on Mechanical Turk while lucidly dreaming). And I'm casually accumulating short stories for a colleciton on masculinity.
The production of knowledge often leads to changes in the way people understand and act in the world in ways that make that knowledge more or less true. While this has been studied in financial markets, it also happens in organizations. Specifically, I show how an urban charter school uses the conception of organizational culture to build its own culture of discipline and order. However, the attempt to use the concept of culture to create culture leads the school into a difficult position when implementing, realizing, and then observing whether or not they were successful. It becomes impossible for them (or anyone) to know whether or not the school was successful. This raises substantial problems for organizational researchers looking to create interventions for schools or any other organization.
The most important policy tool used to create effective schools in the United States is high stakes testing. In this framework, the ideal school embraces accountability and implements the practices to master the test. Rather than being loosely-coupled or decoupled, the ideal school succeeds through what I call deep coupling. I show how my schools achieves deep coupling to Common Core and still see their passing rates drop by half compared to the state test taken the year before. I show how accountability policy creates goals that are outside of a schools’ control and point to the way in which accountabilty should be engineered to drive change improvment.
One of the reasons No Excuses charter schools are purportedly more effective than traditional schools is that they have more effective teacher development programs in the school. Yet teachers in No Excuses charter schools also tend to work longer hours for less pay and without job security, leading to what’s called "churn and burn." I explore how this happens at my focal school. Rather than being systematically planned or an accident, the churn and burn system emerges from many disparate decisions converging over time. In each decision, the school invests more in and expects more of their teachers without compensating for the new responsibilites. What holds the system together is a set of beliefs that teachers are and should obsessive perfectionists, high achievers, and true believers. These values allow teachers and staff to take on increased expectations without led the staff to choose and support the long hours, low wages, and insecurity. These conditions are likely to be found in other charter schools where these beliefs about teachers are more common, and may explain how the general pattern of churn and burn took shape in the field.
Interest in laboratory experiments on social organization has grown in the past decade driven by high-profile successes, a growing capacity to perform large scale experiments, and an increased training in experimental methods. This new interest has the potential to reverse a perennial lag in the use of laboratory experiments in the social sciences and offer new insight into the basic social processes. However, the field is dispersed and fragmented. Long-standing traditions in social psychology, experimental economics, and organizational behavior have long remained within their disciplinary boundaries. New approaches to experimenting with markets, networks, and groups are coming from heterodox fields like computer and network science. In this paper, we propose a three dimensional framework with which to connect these various lines of research into a single endeavor. The first dimension is the specific organization form being induced or simulated in the lab and include markets, networks, and teams. The second dimension is the kind of tasks participants are asked to perform, such as decision-making, puzzle-solving, or resource allocation. These two dimensions are chosen because research consistently shows that altering the task or the structure of the organization fundamentally changes the collective dynamics that emerge. These collective dynamics, including processes like path dependency and equilibrium, represent the third dimension. We argue this framework provides a map of the field, its areas of strength and weakness, and can act as a foundation for an experimental research agenda.
Social life increasingly occurs in digital environments and continues to be mediated by digital systems. Big data represents the data being generated by the digitization of social life, which we break down into three domains: digital life, digital traces, and digitalized life. We argue that there is enormous potential in using big data to study a variety of phenomena that remain difficult to observe. However, there are some recurring vulnerabilities that should be addressed. We also outline the role institutions must play in clarifying the ethical rules of the road. Finally, we conclude by pointing to a few trends that are not yet common in research using big data but will play an increasing role in it.
Experimental research in traditional laboratories comes at a significant logistic and financial cost while drawing data from demographically narrow populations. The growth of online methods of research has resulted in effective means for social psychologists to collect largescale survey-based data in a cost-effective and timely manner. However, the same advancement has not occurred for social psychologists who rely on experimentation as their primary method of data collection. The aim of this article is to provide an overview of one online laboratory for conducting experiments, Volunteer Science, and report the results of six studies that test canonical behaviors commonly captured in social psychological experiments. Our results show that the online laboratory is capable of performing a variety of studies with large numbers of diverse volunteers. We advocate for the use of the online laboratory as a valid and cost-effective way to perform social psychological experiments with large numbers of diverse subjects.
In this paper we explore what predicts data quality and participation by human subjects in an online, games-for-science community. The advent of games for science, citizen science, and online laboratories represent a new world of possibilities for conducting scientific research with human subjects. However, many questions remain about the quality of participation across individual games. In this paper, we use data on user behavior from a unique dataset of 40,000 game sessions across 14 studies to explore what factors predict game completion, consent, and data quality among volunteer participants on a single platform called Volunteer Science.
A variety of studies have shown that machine learning methods like convolutional neural nets and random forests can be used to accurately infer characteristics of people online such as their gender, age, race, or political orientation. However, these methods are atheoretical: producing models which fail to generalize across context and offer little insight why some models perform better in some contexts and not others. This study compares the performance of state-of-the-art, atheoretical models to models informed by social theory on the task of inferring gender in text. Theory-laden models are developed using gender systems theory in sociology. Texts come from five corpora: blog posts, tweets, crowdfunding essays, movie scripts, and professional writing. The results show that models of gender built from theory are as accurate or more accurate than state of the art models. However, performance still varied substantially across corpora and, in some cases, even poor models with little theoretical motivation perform better than the best models. The success of this model suggests the presence of anomalous gender differences with little theoretical explanation.
Ideal points are central to the study of political partisanship and an essential component to our understanding of legislative and electoral behavior. We employ automated text analysis on tweets from Members of Congress to estimate their ideal points using Naive Bayes classification and Support Vector Machine classification. We extend these tools to estimate the proportion of partisan speech used in each legislator’s tweets. We demonstrate an association between these measurements, existing ideal point measurements, and district ideology.
Research on ascriptive inequality investigates how social groups differ, whether resources are allocated unequally by group differences, and what mechanisms create and sustain this unequal allocation. In the sociology of gender, gender system theory links these three questions into a single theory but has yet to be tested comprehensively. In this study, I perform such a test using data from DonorsChoose, a crowdfunding website for public school teachers in the United States. The data is large and diverse enough to measure the gender differences theorized by gender system theory and allows us to examine whether these gender differences correspond to inequalities in funding. Critically, the data also contain a natural experiment whereby teachers’ identity was hidden until 2008. This allows for a direct test of the causes of gender inequality hypothesized by gender system theory. The results show that inequality only emerges after educators’ identity was published. And, deanonymization caused inequality to emerge across all types of gender difference. These results provide robust support for gender system theory and contribute to research on the structure and causes of gender inequality.
You know what one of the deadliest and costliest problems in hospitals is? Shift changes. When patients switch from one nurse to another or one doctor to another, information gets lost, medications are forgotten, procedures get switched. In education, up to 30% of the variation in student learning is attributable to the school, not teachers, not students' social class, not genetics or IQ. Organization matters.
Science and technology have led to massive progress in medicine, agriculture, computing, and a variety of other fields. But fields like education, health care, food service, and management have not experienced such revolutions. The problem is not a lack of tehcnical innovation as Baumol's theory would have it. It's poor research-to-practice translation. In education, we know active learning is much better than traditional forms of teaching, yet most teachers (and professors) still lecture. In organizational science, we know culture plays a critical role in organizational performance, innovation, and resilience; and yet there are no proven programs for creating a culture or changing it.
In my research, I evaluate organizational effectiveness and develop programs to improve organizational performance (however defined) in provable, scalable ways.
Medical interventions like pills and shots are deceptively simple. You take a pill twice a day and your cholesterol goes down or you get three shots and you're immune to hepatitis B. It's not because finding interventions is easy, but the world they exist in is complicated. To know whether an intervention worked, we have to be sure what you take qualifies as a valid version of the intervention, that you've taken it as prescribed, that you validly monitored the expected outcome, and that we've accurately diagnosed the underlying problem. The pill must be simple.
Using Volunteer Science, I am developing web applications that can be used to diagnose and treat problems with organizations and teams. Building on foundational work by Anita Woolley and her colleagues, we are looking to diagnose problems with collective intelligence and identify interventions that improve team's ability to perform together.
I'm currently working with researchers at UT-Dallas' Polycraft World project to design and develop a virtual society from which we can create and experiment with more realistic organizations. In fields like psychology, biology, genetics, and neurology; certain animals are used as models for studying general phenomena. The advantage of model organisms is that they are relatively easy to reproduce and sample from, it is more ethical to experiment with them, and they offer a more realistic mode for testing scientific theories and interventions using experimental methods. At Polycraft World, we are working to create a realistic virtual society from which we can create and experiment with teams, organizations, governments and other forms of social organization to better understand how they work and develop interventions to make them work better.
Project funded by the DARPA Next Generation Social Science program.
Three forces are fundamentally changing experimentation in the social and behavioral sciences. 1) The ubiquity of computers connected to the internet makes it possible for researchers to perform experiments with subjects anytime, anywhere. 2) The accumulation of data and technologies on our myriad devices and online accounts enables us to integrate a profound variety of data with our behavioral methods. 3) The global penetration of the internet is allowing researchers to recruit subjects from any where in the world. At Volunteer Science, we are building tools that harness these changes for researchers.
Working with Gallup, we have developed a way to recruit large samples of volunteers to participate in experiments across the globe. Over the course of two weeks, we spent $6,000 and recruited 20,000 volunteers to complete a demographic survey and two personality inventories. During a two hour window one morning, we spent $400 to recruit 900 paricipants who completing 63 group experiments. The era of cheap, global subject samples has arrived.
We are currently seeking funding to create a self-service interface for researchers and plan to publish a validation study of the method in the coming year.
Gathering data from the field is an essential part of social science research. This is just as true in the digital "field" - the online world people navigate to do everything from purchase a hotel to figure out who to vote for. Existing approaches like web scraping or analyzing digital archives miss much of what users experience because content changes regularly and is often personalized to the user. We thus need tools that can observe the digital field in real time as individuals experience it. For this, we built a browser plugin that can capture web pages and take screen shots from users as well as collect browser data. We've been using this to study price personalization by online retailers. But it can be used for any data collect in the digital field.
We're not currenlty collect data, but you can see the plugin here.
New methods of data analysis from machine learning are changing the way we measure everyday social identity like race, gender, and partisanship. My computational work focuses on using social theory to adapting these methods to produce new analyses of these constructs. My research on discrimination in a crowfunding market shows that using these methods in combination with existing measures produces new and profound insights into gender inequality. Building on this, I am generalizing my measure of gender to other domains, investigating what a theory-driven application of machine learning can tell us.
The influx of machine learning techniques into the social science research and the explosion of big, digital social data are rapidly transforming social science research across fields. There have been many incredible advances in knowledge driven by our newfound ability to perform certain kinds of studies more easily - natural experiments, data on social systems, and real time human behavior. However, as we take advantage of these new possibilities, we need to remain disciplined in the questions we ask, data and methods we use, and inferences we draw. There is a swath of new computational research that turns biased data and folk theory into research that serves to reproduce and extend institutions of inequality - a digital physiognomy. Computational and non-computational social scientists alike should understand the strengths and limits of these methods in our new age.
At Volunteer Science, we've run half a million study sessions since 2014. We've made our platform available so you can create your own study and recruit participants from around the world. If you have an idea for a study you'd like to run, reach out to me today.
You can start building your own online experiments right now by creating a research account on Volunteer Science. We've built a number of templates you can use to get started as well documentation, FAQs, and tutorials to help answer questions along the way. And if you need any help, please reach out to me. I do a lot of consulting on study design, building, and recruitment.
The studies I'm building, whether for organizational effectiveness, collecting big data, or just improving the offerings at Volunteer Science are all scientific web applications. These can include the browser plugins, web games, online markets, or mobile apps. These tools can not only incorporate high-resolution behavioral data, real-time web experiences, and individuals' existing digital data; but they can be deployed with anyone anywhere in the world. It is trivial to develop an app in a lab and then bring it into a field site, distant research lab, or special population. They are also easy to iterate on and replicate as you can simply re-run the application.
Web applications are all based on a relatively simple set of tools that anyone can learn to harness for their research. You can learn the basics of web design from any number of online courses. But to turn good web design into scientific web applications, you need to know how to put the elements of design together. I teach a two-day technical course on building scientific web applications. Reach out to me if you want me to teach it to you or your group.
At Volunteer Science, we're pushing the technical limits of social science every day, whether it's making new tools available to researchers, finding new ways to recruit subjects, or building our own new studies. We're looking for help with everything from backend and front end developers to operations and biz dev. Compensation is 100% equity for the right people. Take the plunge with us and join the most forward thinking social science research companies in the world.
Do you love science? Do you want to get more people engaged in science? Do you want to teach people about the newest research and ? Then you should join our ambassador program. We provide training in recruiting people to participate in studies on Volunteer Science and, in exchange, provide you with training in how to build your own audience and brand around social science research. You'll get early access to our studies and researchers, meet regularly with other science influencers, and work directly with us to push science forward. This is a volunteer position with the opportunity to participate in our subject recruitment bounty program for those who complete the training.
At Volunteer Science, we're constantly pubishing new studies from researchers all over the world looking for volunteers. We've had studies from researchers ranging from Johns Hopkins Medical Center and Harvard to Universite Toulouse and Gallup. They have recruited people to look for evidence if diabetes in eye exams, read and react to election scenarios, solve problems with other people, and play online team games. If you want to help develop our understanding of the human condition, head to Volunteer Science today and donate your time.
We're looking for organizations now who are interested in having their members participate in experiments that will help us diagnose and improve common team issues like sharing diverse information, improving intellectual safety, making collective decisions, and managing conflict. We would need your members to participate in online experiments together and, ideally, connect your members' participation data to organizational performance data (so we know our metrics can help you. If you're interested in participating and being the first to know how to make teams more successful, reach out to me.