Bit by Bit is now available for pre-order

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I’m very happy to announce that Bit by Bit is now available for pre-order. If you order it right now, you should have it around Thanksgiving (November 23, 2017). Bit by Bit 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 hybrid of these two fields.

Here are links where you can pre-order the book:
• Amazon: https://www.amazon.com/Bit-Social-Research-Digital-Age/dp/0691158649/
• Barnes & Noble: https://www.barnesandnoble.com/w/bit-by-bit-matthew-salganik/1125483924
• IndieBound: https://www.indiebound.org/book/9780691158648
• Princeton University Press: http://press.princeton.edu/titles/11057.html

Also, at the end of this post is some information from my publisher about the book, including a 25% off coupon and information about how you can request an exam copy.

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meetup about teaching computational social science at ASA

Please join me for an informal meetup about teaching computational social science Monday, August 14 at 3pm.  We will meet at the Princeton University Press booth in the exhibit hall at ASA.  The purpose of the meetup is for people teaching computational social science—or thinking about teaching it—to share experiences and troubleshoot common problems.  The number and variety of courses on computational social science is growing rapidly, and I think that we can all benefit from hearing about the exciting things that people are doing.  I look forward to seeing you in Montreal.

Making sense of the rlnorm() function in R

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Post by Malte Möser and Matthew Salganik

There’s an activity in Bit by Bit: Social Research in the Digital Age that requires generating random draws from a log-normal distribution.  Unfortunately, the rlnorm() function in R doesn’t work exactly how many people expect.  So, we wanted to write a little post about it.  That way, if you are working on the activity—which is about power analysis—you can focus on power analysis and not the rlnorm() function.

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Open Review leads to better books

Originally posted on Freedom to Tinker

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My book manuscript, Bit by Bit: Social Research in the Digital Age, is now in Open Review. That means that while the book manuscript goes through traditional peer review, I also posted it online for a parallel Open Review. During the Open Review everyone—not just traditional peer reviewers—can read the manuscript and help make it better.

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I think that the Open Review process will lead to better books, higher sales, and increased access to knowledge.  In this blog post, I’d like to describe the feedback that I’ve received during the first month of Open Review and what I’ve learned from the process.

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Bit by Bit: Social Research in the Digital Age is now in Open Review

Bit by Bit: Social Research in the Digital Age is a book for social scientists who want to do more data science and data scientists who want to do more social science.  I’m happy to announce that the entire manuscript is now in Open Review.  That means that you can read it and help make it better by adding annotations.

Here’s how the book starts:

In the summer of 2009, mobile phones were ringing all across Rwanda. In addition to the millions of calls between family, friends, and business associates, about 1,000 Rwandans received a call from Joshua Blumenstock and his colleagues. The researchers were studying wealth and poverty by conducting a survey of people who had been randomly sampled from a database of 1.5 million customers from Rwanda’s largest mobile phone provider. Blumenstock and colleagues asked the participants if they wanted to participate in a survey, explained the nature of the research to them, and then asked a series of questions about their demographic, social, and economic characteristics.

Everything I have said up until now makes this sound like a traditional social science survey. But, what comes next is not traditional, at least not yet. They used the survey data to train a machine learning model to predict someone’s wealth from their call data, and then they used this model to estimate the wealth of all 1.5 million customers. Next, they estimated the place of residence of all 1.5 million customers by using the geographic information embedded in the call logs. Putting these two estimates together—the estimated wealth and the estimated place of residence—Blumenstock and colleagues were able to produce high-resolution estimates of the geographic distribution of wealth across Rwanda. In particular, they could produce an estimated wealth for each of Rwanda’s 2,148 cells, the smallest administrative unit in the country.

It was impossible to validate these estimates because no one had ever produced estimates for such small geographic areas in Rwanda. But, when Blumenstock and colleagues aggregated their estimates to Rwanda’s 30 districts, they found that their estimates were similar to estimates from the Demographic and Health Survey, the gold standard of surveys in developing countries. Although these two approaches produced similar estimates in this case, the approach of Blumenstock and colleagues was about 10 times faster and 50 times cheaper than the traditional Demographic and Health Surveys. These dramatically faster and lower cost estimates create new possibilities for researchers, governments, and companies (Blumenstock, Cadamuro, and On 2015).

In addition to developing a new methodology, this study is kind of like a Rorschach inkblot test; what people see depends on their background. Many social scientists see a new measurement tool that can be used to test theories about economic development. Manydata scientists see a cool new machine learning problem. Many business people see a powerful approach for unlocking value in the digital trace data that they have already collected. Many privacy advocates see a scary reminder that we live in a time of mass surveillance. Many policy makers see a way that new technology can help create a better world. In fact, this study is all of those things, and that is why it is a window into the future of social research.

If you want to see why I think that study is a window into the future of social research,  check out the rest of the book: http://www.bitbybitbook.com.

Planning a book manuscript workshop

I recently finished a manuscript workshop for my book-in-progress, Bit by Bit: Social Research in the Digital Age.  The book is for social scientists that want to do more data science and data scientists that want to do more social science.  I’m very grateful to everyone that participated in the workshop; I know that it will make my book much better.  The goal of this blog post is to write down everything that I learned planning and participating in workshop in order to make it easier for others in the future.

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