Leseprobe
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Bit by Bit
Matthew J. Salganik
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Sozialwissenschaften, Recht, Wirtschaft / Methoden der empirischen und qualitativen Sozialforschung
Beschreibung
An innovative and accessible guide to doing social research in the digital age
In just the past several years, we have witnessed the birth and rapid spread of social media, mobile phones, and numerous other digital marvels. In addition to changing how we live, these tools enable 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—a landmark book that will fundamentally change how the next generation of social scientists and data scientists explores the world around us.
Bit by Bit is the essential guide to mastering the key principles of doing social research in this fast-evolving digital age. In this comprehensive yet accessible book, Matthew Salganik explains how the digital revolution is transforming how social scientists observe behavior, ask questions, run experiments, and engage in mass collaborations. He provides a wealth of real-world examples throughout and also lays out a principles-based approach to handling ethical challenges.
Bit by Bit is an invaluable resource for social scientists who want to harness the research potential of big data and a must-read for data scientists interested in applying the lessons of social science to tomorrow’s technologies.
- 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
Kundenbewertungen
Estimation, External validity, Voting, Cover letter, Statistical significance, Big data, OkCupid, Multiple choice, Fat Head, Renaissance Technologies, New Math, Study heterogeneity, Judea Pearl, Business ethics, Economics, Confounding, Public interest, Online panel, Natural experiment, Usage data, Calculation, Variance reduction, Randomized controlled trial, Bitcoin, Motherhood penalty, Participant, Second source, Snapchat, Belmont Report, Galaxy Zoo, No Free Lunch (organization), Research ethics, Mass collaboration, Telephone interview, Post hoc analysis, Data science, Questionnaire, Interviewer effect, Political consulting, Amazon Mechanical Turk, Differential privacy, Social desirability bias, Field experiment, Polymath Project, Bioethics, Sampling (statistics), Variable cost, Foldit, Ignorability, Full disclosure (computer security), Early adopter, Audit study, Pasteur's quadrant, Scientific misconduct, Twitter, Internal validity, Microsoft Research, Observational study, Kaggle, Statistical conclusion validity, Reinventing Discovery, Margin of error, Meta-analysis, Hawthorne effect, Result, Machine learning, Mass surveillance, Respondent, Instrumental variable, Website