Kieran Healy on how to create effective graphics from data

Kieran Healy’s accessible primer, Data Visualization: A Practical Introduction explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way. Check out a few particularly successful examples from Healy:

1. Age distribution of U.S. Representatives, 1945-2019.




3. Men and Women in the House of Representatives



This is the “Freshman Class”. This chart shows the proportion of those first-timers who have been women. The incoming Democratic freshman class has a record number of women in it. 

4. White Guys Named John vs African Americans in Congress

A slightly frivolous way to make a serious point. For most of the past seventy five years, there have been more white men named “John” in Congress than there have been African American representatives.

5. Mean Age of Congressional Members


6. Business & Law in the House of Representatives


7. Men and Women elected to Congress

Winning Party by District


8. U.S. Representatives by Race

Brian Kernighan on Millions, Billions, Zillions













Matthew Salganik: Invisibilia, the Fragile Families Challenge, and Bit by Bit


This week’s episode of Invisibilia featured my research on the Fragile Families Challenge. The Challenge is a scientific mass collaboration that combines predictive modeling, causal inference, and in-depth interviews to yield insights that can improve the lives of disadvantaged children in the United States. Like many research projects, the Fragile Families Challenge emerged from a complex mix of inspirations. But, for me personally, a big part of the Fragile Families Challenge grew out of writing my new book Bit by Bit: Social Research in the Digital Age. In this post, I’ll describe how Bit by Bit helped give birth to the Fragile Families Challenge.

Bit by Bit is about social research in the age of big data. It is for social scientists who want to do more data science, data scientists who want to do more social science, and anyone interested in the combination of these two fields. Rather than being organized around specific data sources or machine learning methods, Bit by Bit progresses through four broad research designs: observing behavior, asking questions, running experiments, and creating mass collaboration. Each of these approaches requires a different relationship between researchers and participants, and each enables us to learn different things.

As I was working on Bit by Bit, many people seemed genuinely excited about most of the book—except the chapter on mass collaboration. When I talked about this chapter with colleagues and friends, I was often greeted with skepticism (or worse). Many of them felt that mass collaboration simply had no place in social research. In fact, at my book manuscript workshop—which was made up of people that I deeply respected—the general consensus seemed to be that I should drop this chapter from Bit by Bit.  But I felt strongly that it should be included, in part because it enabled researchers to do new and different kinds of things. The more time I spent defending the idea of mass collaboration for social research, the more I became convinced that it was really interesting, important, and exciting. So, once I finished up the manuscript for Bit by Bit, I set my sights on designing the mass collaboration that became the Fragile Families Challenge.

The Fragile Families Challenge, described in more detail at the project website and blog, should be seen as part of the larger landscape of mass collaboration research. Perhaps the most well known example of a mass collaboration solving a big intellectual problem is Wikipedia, where a mass collaboration of volunteers created a fantastic encyclopedia that is available to everyone.

Collaboration in research is nothing new, of course. What is new, however, is that the digital age enables collaboration with a much larger and more diverse set of people: the billions of people around the world with Internet access. I expect that these new mass collaborations will yield amazing results not just because of the number of people involved but also because of their diverse skills and perspectives. How can we incorporate everyone with an Internet connection into our research process? What could you do with 100 research assistants? What about 100,000 skilled collaborators?

As I write in Bit by Bit, I think it is helpful to roughly distinguish between three types of mass collaboration projects: human computation, open call, and distributed data collectionHuman computation projects are ideally suited for easy-task-big-scale problems, such as labeling a million images. These are projects that in the past might have been performed by undergraduate research assistants. Contributions to human computation projects don’t require specialized skills, and the final output is typically an average of all of the contributions. A classic example of a human computation project is Galaxy Zoo, where a hundred thousand volunteers helped astronomers classify a million galaxies. Open call projects, on the other hand, are more suited for problems where you are looking for novel answers to clearly formulated questions. In the past, these are projects that might have involved asking colleagues. Contributions to open call projects come from people who may have specialized skills, and the final output is usually the best contribution. A classic example of an open call is the Netflix Prize, where thousands of scientists and hackers worked to develop new algorithms to predict customers’ ratings of movies. Finally, distributed data collection projects are ideally suited for large-scale data collection. These are projects that in the past might have been performed by undergraduate research assistants or survey research companies. Contributions to distributed data collection projects typically come from people who have access to locations that researchers do not, and the final product is a simple collection of the contributions. A classic example of a distributed data collection is eBird, in which hundreds of thousands of volunteers contribute reports about birds they see.

Given this way of organizing things, you can think of the Fragile Families Challenge as an open call project, and when designing the Challenge, I draw inspiration from the other open call projects that I wrote about such as the Netflix Prize, Foldit, and Peer-to-Patent.

If you’d like to learn more about how mass collaboration can be used in social research, I’d recommend reading Chapter 5 of Bit by Bit or watching this talk I gave at Stanford in the Human-Computer Interaction Seminar. If you’d like to learn more about the Fragile Families Challenge, which is ongoing, I’d recommend our project website and blog.  Finally, if you are interested in social science in the age of big data, I’d recommend reading all of Bit by Bit: Social Research in the Digital Age.


Jerry Z. Muller on The Tyranny of Metrics


What’s the main idea?

We increasingly live in a culture of metric fixation: the belief in so many organizations that scientific management means replacing judgment based upon experience and talent with standardized measures of performance, and then rewarding or punishing individuals and organizations based upon those measures. The buzzwords of metric fixation are all around us: “metrics,” “accountability,” “assessment,” and “transparency.” Though often characterized as “best practice,” metric fixation is in fact often counterproductive, with costs to individual satisfaction with work, organizational effectiveness, and economic growth.

The Tyranny of Metrics treats metric fixation as the organizational equivalent of The Emperor’s New Clothes. It helps explain why metric fixation has become so popular, why it is so often counterproductive, and why some people have an interest in pushing it. It is a book that analyzes and critiques a dominant fashion in contemporary organizational culture, with an eye to making life in organizations more satisfying and productive.

Can you give a few examples of the “tyranny of metrics?”

Sure. In medicine, you have the phenomenon of “surgical report cards” that purport to show the success rates of surgeons who perform a particular procedure, such as cardiac operations. The scores are publicly reported. In an effort to raise their scores, surgeons were found to avoid operating on patients whose complicated circumstances made a successful operation less likely. So, the surgeons raised their scores. But some cardiac patients who might have benefited from an operation failed to get one—and died as a result. That’s what we call “creaming”—only dealing with cases most likely to be successful.

Then there is the phenomenon of goal diversion. A great deal of K-12 education has been distorted by the emphasis that teachers are forced to place on preparing students for standardized tests of English and math, where the results of the tests influence teacher retention or school closings. Teachers are instructed to focus class time on the elements of the subject that are tested (such as reading short prose passages), while ignoring those elements that are not (such as novels). Subjects that are not tested—including civics, art, and history—receive little attention.

Or, to take an example from the world of business. In 2011 the Wells Fargo bank set high quotas for its employees to sign up customers who were interested in one of its products (say, a deposit account) for additional services, such as overdraft coverage or credit cards. For the bank’s employees, failure to reach the quota meant working additional hours without pay and the threat of termination. The result: to reach their quotas, thousands of bankers resorted to low-level fraud, with disastrous effects for the bank. It was forced to pay a fortune in fines, and its stock price dropped.

Why is the book called The Tyranny of Metrics?

Because it helps explain and articulate the sense of frustration and oppression that people in a wide range of organizations feel at the diversion of their time and energy to performance measurement that is wasteful and counterproductive.

What sort of organizations does the book deal with?

There are chapters devoted to colleges and universities, K-12 education, medicine and health care, business and finance, non-profits and philanthropic organizations, policing, and the military. The goal is not to be definitive about any of these realms, but to explore instances in which metrics of measured performance have been functional or dysfunctional, and then to draw useful generalizations about the use and misuse of metrics.

What sort of a book is it? Does it belong to any particular discipline or political ideology?

It’s a work of synthesis, drawing on a wide range of studies and analyses from psychology, sociology, economics, political science, philosophy, organizational behavior, history, and other fields. But it’s written in jargon-free prose, that doesn’t require prior knowledge of any of these fields. Princeton University Press has it classified under “Business,” “Public Policy,” and “Current Affairs.” That’s accurate enough, but it only begins to suggest the ubiquity of the cultural pattern that the book depicts, analyzes, and critiques. The book makes use of conservative, liberal, Marxist, and anarchist authors—some of whom have surprising areas of analytic convergence.

What’s the geographic scope of the book?

In the first instance, the United States. There is also a lot of attention to Great Britain, which in many respects was at the leading edge of metric fixation in the government’s treatment of higher education (from the “Teaching Quality Assessment” through the “Research Excellence Framework”), health care (the NHS) and policing, under the rubric of “New Public Management.” From the US and Great Britain, metric fixation—often carried by consultants touting “best practice”—has spread to Continental Europe, the Anglosphere, Asia, and especially China (where the quest for measured performance and university rankings is having a particularly pernicious effect on science and higher education).

Is the book simply a manifesto against performance measurement?

By no means. Drawing on a wide range of case studies from education to medicine to the military, the book shows how measured performance can be developed and used in positive ways.

Who do you hope will read the book?

Everyone who works in an organization, manages an organization, or supervises an organization, whether in the for-profit, non-profit, or government sector. Or anyone who wants to understand this dominant organizational culture and its intrinsic weaknesses.

Jerry Z. Muller is the author of many books, including Adam Smith in His Time and Ours and Capitalism and the Jews. His writing has appeared in the New York Times, the Wall Street Journal, the Times Literary Supplement, and Foreign Affairs, among other publications. He is professor of history at the Catholic University of America in Washington, D.C., and lives in Silver Spring, Maryland.





Who is this book for?





Bit by Bit devotes a lot attention to ethics.  Why?






Was putting your book through Open Review scary?




