by Matthew J. Salganik, Princeton University Press, 2017
Very positive reviews by…
- Times Higher Education, Farida Vis, 11 January 2011
- Science Magazine, David Lazar, 2 January 2018
- Princeton University Press Blog, author interview, 7 December 2017
Selected quotes:
“Overall, the book relies on a repeated narrative device, imagining how a social scientist and a data scientist might approach the same research opportunity. Salganik suggests that where data scientists are glass-half-full people and see opportunities, social scientists are quicker to highlight problems (the glass-half-empty camp). He is also upfront about how he has chosen to write the book, adopting the more optimistic view of the data scientist, while holding on to the caution expressed by social scientists”
“Salganik argues that data scientists most often work with “readymades”, social scientists with “custommades”, illustrating the point through art: data scientists are more like Marcel Duchamp, using existing objects to make art; meanwhile, social scientists operate in the custom-made style of Michelangelo, which offers a neat fit between research questions and data, but does not scale well. The book is thus a call to arms, to encourage more interdisciplinary research and for both sides to see the potential merits and drawbacks of each approach. It will be particularly welcome to researchers who have already started to think along similar lines, of which I suspect there are many”
Illustrates important ideas with examples of outstanding research
Combines ideas from social science and data science in an accessible style and without jargon
Goes beyond the analysis of “found” data to discuss the collection of “designed” data such as surveys, experiments, and mass collaboration
Features an entire chapter on ethics
Includes extensive suggestions for further reading and activities for the classroom or self-study
Matthew J. Salganik is professor of sociology at Princeton University, where he is also affiliated with the Center for Information Technology Policy and the Center for Statistics and Machine Learning. His research has been funded by Microsoft, Facebook, and Google, and has been featured on NPR and in such publications as the New Yorker, the New York Times, and the Wall Street Journal.
Contents
Preface
1 Introduction
2 Observing Behavior
3 Asking Questions
4 Running Experiments
5 Creating Mass Collaboration
6 Ethics
7 The Future
Acknowledgments
References
Index
More detailed contents page available via Amazon Look Inside
PS: See also this Vimeo video presentation by Salganik: Wiki Surveys – Open and Quantifiable Social Data Collection plus this PLOS paper on the same topic.
