Bit by Bit: Social Research in the Digital Age

Matthew J. Salganik

An innovative and accessible guide to doing social research in the digital age

Hardcover

Price:: $28.95/£25.00
ISBN:
Published:: Aug 6, 2019
Copyright:: 2050
Pages:: 448
Size:: 7 x 9 in.
Illus:: 79 b/w illus. 28 tables. 2 maps.

Paperback

Price:: $28.95/£25.00
ISBN:
Published:: Aug 6, 2019
Copyright:: 2050
Pages:: 448
Size:: 7 x 9 in.
Illus:: 79 b/w illus. 28 tables. 2 maps.

ebook

Price:: $28.95/£25.00
ISBN:
Published:: Aug 6, 2019
Copyright:: 2050
Pages:: 448
Size:: 7 x 9 in.
Illus:: 79 b/w illus. 28 tables. 2 maps.

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The rapid spread of social media, smartphones, and other digital wonders enables us to collect and process data about human behavior on a scale never before imaginable, offering entirely new approaches to core questions about social behavior. Bit by Bit is the key to unlocking these powerful methods. In this authoritative and accessible book, Matthew Salganik explains how the digital revolution is transforming the way social scientists observe behavior, ask questions, run experiments, and engage in mass collaborations. Featuring a wealth of real-world examples and invaluable advice on how to tackle the thorniest ethical challenges, Bit by Bit is the essential guide to doing social research in this fast-evolving digital age.

Matthew Salganik: The Open Review of Bit by Bit, Part 1—Better books

Matthew Salganik: The Open Review of Bit by Bit, Part 2—Higher sales

Matthew Salganik: The Open Review of Bit by Bit, Part 3—Increased access to knowledge

Awards and Recognition

Winner of the 2019 PROSE Award in Textbook / Social Sciences, Association of American Publishers
Winner of the AAPOR Book Award, American Association for Public Opinion Research

PREFACE
CHAPTER 1 • INTRODUCTION
- 1.1 An ink blot
- 1.2 Welcome to the digital age
- 1.3 Research design
- 1.4 Themes of this book
- 1.5 Outline of this book
  - What to read next
CHAPTER 2 • OBSERVING BEHAVIOR
- 2.1 Introduction
- 2.2 Big data
- 2.3 Ten common characteristics of big data
  - 2.3.1 Big
  - 2.3.2 Always-on
  - 2.3.3 Nonreactive
  - 2.3.4 Incomplete
  - 2.3.5 Inaccessible
  - 2.3.6 Nonrepresentative
  - 2.3.7 Drifting
  - 2.3.8 Algorithmically confounded
  - 2.3.9 Dirty
  - 2.3.10 Sensitive
- 2.4 Research strategies
  - 2.4.1 Counting things
  - 2.4.2 Forecasting and nowcasting
  - 2.4.3 Approximating experiments
- 2.5 Conclusion
  - Mathematical notes
  - What to read next
  - Activities
CHAPTER 3 • ASKING QUESTIONS
- 3.1 Introduction
- 3.2 Asking versus observing
- 3.3 The total survey error framework
  - 3.3.1 Representation
  - 3.3.2 Measurement
  - 3.3.3 Cost
- 3.4 Who to ask
- 3.5 New ways of asking questions
  - 3.5.1 Ecological momentary assessments
  - 3.5.2 Wiki surveys
  - 3.5.3 Gamification
- 3.6 Surveys linked to big data sources
  - 3.6.1 Enriched asking
  - 3.6.2 Amplified asking
- 3.7 Conclusion
  - Mathematical notes
  - What to read next
  - Activities
CHAPTER 4 • RUNNING EXPERIMENTS
- 4.1 Introduction
- 4.2 What are experiments?
- 4.3 Two dimensions of experiments: lab–field and analog–digital.
- 4.4 Moving beyond simple experiments
  - 4.4.1 Validity
  - 4.4.2 Heterogeneity of treatment effects
  - 4.4.3 Mechanisms
- 4.5 Making it happen
  - 4.5.1 Use existing environments
  - 4.5.2 Build your own experiment
  - 4.5.3 Build your own product
  - 4.5.4 Partner with the powerful
- 4.6 Advice
  - 4.6.1 Create zero variable cost data
  - 4.6.2 Build ethics into your design: replace, refine, and reduce
- 4.7 Conclusion
  - Mathematical notes
  - What to read next
  - Activities
CHAPTER 5 • CREATING MASS COLLABORATION
- 5.1 Introduction
- 5.2 Human computation
  - 5.2.1 Galaxy Zoo
  - 5.2.2 Crowd-coding of political manifestos
  - 5.2.3 Conclusion
- 5.3 Open calls
  - 5.3.1 Netflix Prize
  - 5.3.2 Foldit
  - 5.3.3 Peer-to-Patent
  - 5.3.4 Conclusion
- 5.4 Distributed data collection
  - 5.4.1 eBird
  - 5.4.2 PhotoCity
  - 5.4.3 Conclusion
- 5.5 Designing your own
  - 5.5.1 Motivate participants
  - 5.5.2 Leverage heterogeneity
  - 5.5.3 Focus attention
  - 5.5.4 Enable surprise
  - 5.5.5 Be ethical
  - 5.5.6 Final design advice
- 5.6 Conclusion
  - What to read next
  - Activities
CHAPTER 6 • ETHICS
- 6.1 Introduction
- 6.2 Three examples
  - 6.2.1 Emotional Contagion
  - 6.2.2 Tastes, Ties, and Time
  - 6.2.3 Encore
- 6.3 Digital is different
- 6.4 Four principles
  - 6.4.1 Respect for Persons
  - 6.4.2 Beneficence
  - 6.4.3 Justice
  - 6.4.4 Respect for Law and Public Interest
- 6.5 Two ethical frameworks
- 6.6 Areas of difficulty
  - 6.6.1 Informed consent
  - 6.6.2 Understanding and managing informational risk
  - 6.6.3 Privacy
  - 6.6.4 Making decisions in the face of uncertainty
- 6.7 Practical tips
  - 6.7.1 The IRB is a floor, not a ceiling
  - 6.7.2 Put yourself in everyone else’s shoes
  - 6.7.3 Think of research ethics as continuous, not discrete
- 6.8 Conclusion
  - Historical appendix
  - What to read next
  - Activities
CHAPTER 7 • THE FUTURE
- 7.1 Looking forward
- 7.2 Themes of the future
  - 7.2.1 The blending of readymades and custommades
  - 7.2.2 Participant-centered data collection
  - 7.2.3 Ethics in research design
- 7.3 Back to the beginning
ACKNOWLEDGMENTS
REFERENCES
INDEX

"An enticing and important field guide to the new frontier of digital social research."—Beth Simone Noveck, Forbes

"Salganik is one of the first natural-born computational social scientists. . . . The text is clearly written—even breezy, in parts. It puts the reader in the shoes of the researcher: What decisions were made, why, and were those the best choices? It is suitable for an advanced undergraduate or graduate class in methodology, with a rigorous, mathematical appendix and a range of useful problems at the conclusion of each chapter."—David Lazer, Science

"This is a book to return to time and again. . . . Bit by Bit should be widely read by those engaging in social research, as well as beyond."—Farida Vis, Times Higher Education

"A tremendously useful introduction for data scientists to the pressing questions of the social sciences, and for social scientists to the mindset and toolset of data science. With skill and scholarly insight, Salganik addresses the ethical questions that arise at the intersection of data science and social science."—Chris Wiggins, Columbia University