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El. knyga: Bit by Bit: Social Research in the Digital Age

4.08/5 (169 ratings by Goodreads)
  • Formatas: 448 pages
  • Išleidimo metai: 10-Sep-2024
  • Leidėjas: Princeton University Press
  • Kalba: eng
  • ISBN-13: 9780691270869
Kitos knygos pagal šią temą:
Bit by Bit: Social Research in the Digital AgeDidesnis paveikslėlis
  • Formatas: 448 pages
  • Išleidimo metai: 10-Sep-2024
  • Leidėjas: Princeton University Press
  • Kalba: eng
  • ISBN-13: 9780691270869
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An innovative and accessible guide to doing social research in the digital age

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.

Recenzijos

"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 xv
Chapter 1 Introduction
1(12)
1.1 An ink blot
1(1)
1.2 Welcome to the digital age
2(3)
1.3 Research design
5(1)
1.4 Themes of this book
6(3)
1.5 Outline of this book
9(4)
What to read next
11(2)
Chapter 2 Observing Behavior
13(72)
2.1 Introduction
13(1)
2.2 Big data
14(3)
2.3 Ten common characteristics of big data
17(24)
2.3.1 Big
17(4)
2.3.2 Always-on
21(2)
2.3.3 Nonreactive
23(1)
2.3.4 Incomplete
24(3)
2.3.5 Inaccessible
27(2)
2.3.6 Nonrepresentative
29(4)
2.3.7 Drifting
33(2)
2.3.8 Algorithmically confounded
35(2)
2.3.9 Dirty
37(2)
2.3.10 Sensitive
39(2)
2.4 Research strategies
41(20)
2.4.1 Counting things
41(5)
2.4.2 Forecasting and nowcasting
46(4)
2.4.3 Approximating experiments
50(11)
2.5 Conclusion
61(24)
Mathematical notes
62(8)
What to read next
70(7)
Activities
77(8)
Chapter 3 Asking Questions
85(62)
3.1 Introduction
85(2)
3.2 Asking versus observing
87(2)
3.3 The total survey error framework
89(10)
3.3.1 Representation
91(3)
3.3.2 Measurement
94(4)
3.3.3 Cost
98(1)
3.4 Who to ask
99(8)
3.5 New ways of asking questions
107(10)
3.5.1 Ecological momentary assessments
108(3)
3.5.2 Wiki surveys
111(4)
3.5.3 Gamification
115(2)
3.6 Surveys linked to big data sources
117(13)
3.6.1 Enriched asking
118(4)
3.6.2 Amplified asking
122(8)
3.7 Conclusion
130(17)
Mathematical notes
130(6)
What to read next
136(5)
Activities
141(6)
Chapter 4 Running Experiments
147(84)
4.1 Introduction
147(2)
4.2 What are experiments?
149(2)
4.3 Two dimensions of experiments: lab-field and analog-digital
151(7)
4.4 Moving beyond simple experiments
158(16)
4.4.1 Validity
161(6)
4.4.2 Heterogeneity of treatment effects
167(2)
4.4.3 Mechanisms
169(5)
4.5 Making it happen
174(14)
4.5.1 Use existing environments
175(3)
4.5.2 Build your own experiment
178(4)
4.5.3 Build your own product
182(1)
4.5.4 Partner with the powerful
183(5)
4.6 Advice
188(14)
4.6.1 Create zero variable cost data
190(6)
4.6.2 Build ethics into your design: replace, refine, and reduce
196(6)
4.7 Conclusion
202(29)
Mathematical notes
203(6)
What to read next
209(11)
Activities
220(11)
Chapter 5 Creating Mass Collaboration
231(50)
5.1 Introduction
231(2)
5.2 Human computation
233(13)
5.2.1 Galaxy Zoo
234(7)
5.2.2 Crowd-coding of political manifestos
241(3)
5.2.3 Conclusion
244(2)
5.3 Open calls
246(10)
5.3.1 Netflix Prize
246(3)
5.3.2 Foldit
249(3)
5.3.3 Peer-to-Patent
252(2)
5.3.4 Conclusion
254(2)
5.4 Distributed data collection
256(9)
5.4.1 eBird
257(2)
5.4.2 PhotoCity
259(3)
5.4.3 Conclusion
262(3)
5.5 Designing your own
265(6)
5.5.1 Motivate participants
265(1)
5.5.2 Leverage heterogeneity
266(1)
5.5.3 Focus attention
267(1)
5.5.4 Enable surprise
267(1)
5.5.5 Be ethical
268(1)
5.5.6 Final design advice
269(2)
5.6 Conclusion
271(10)
What to read next
272(5)
Activities
277(4)
Chapter 6 Ethics
281(74)
6.1 Introduction
281(2)
6.2 Three examples
283(5)
6.2.1 Emotional Contagion
284(1)
6.2.2 Tastes, Ties, and Time
285(1)
6.2.3 Encore
286(2)
6.3 Digital is different
288(6)
6.4 Four principles
294(7)
6.4.1 Respect for Persons
295(1)
6.4.2 Beneficence
296(2)
6.4.3 Justice
298(1)
6.4.4 Respect for Law and Public Interest
299(2)
6.5 Two ethical frameworks
301(2)
6.6 Areas of difficulty
303(18)
6.6.1 Informed consent
303(4)
6.6.2 Understanding and managing informational risk
307(7)
6.6.3 Privacy
314(3)
6.6.4 Making decisions in the face of uncertainty
317(4)
6.7 Practical tips
321(3)
6.7.1 The IRB is a floor, not a ceiling
321(1)
6.7.2 Put yourself in everyone else's shoes
322(2)
6.7.3 Think of research ethics as continuous, not discrete
324(1)
6.8 Conclusion
324(31)
Historical appendix
325(6)
What to read next
331(7)
Activities
338(17)
Chapter 7 The Future
355(6)
7.1 Looking forward
355(1)
7.2 Themes of the future
355(3)
7.2.1 The blending of readymades and custommades
355(1)
7.2.2 Participant-centered data collection
356(1)
7.2.3 Ethics in research design
357(1)
7.3 Back to the beginning
358(3)
Acknowledgments 361(6)
References 367(46)
Index 413
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.
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