Computational approaches offer exciting opportunities for us to do social science differently. This beginner’s guide discusses a range of computational methods and how to use them to study the problems and questions you want to research.

It assumes no knowledge of programming, offering step-by-step guidance for coding in Python and drawing on examples of real data analysis to demonstrate how you can apply each approach in any discipline.

The book also:

Considers important principles of social scientific computing, including transparency, accountability and reproducibility.
Understands the realities of completing research projects and offers advice for dealing with issues such as messy or incomplete data and systematic biases.
Empowers you to learn at your own pace, with online resources including screencast tutorials and datasets that enable you to practice your skills and get up to speed.

For anyone who wants to use computational methods to conduct a social science research project, this book equips you with the skills, good habits and best working practices to do rigorous, high quality work.

�ber die Autorin bzw. den Autor:

John McLevey is an Associate Professor in the Department of Knowledge Integration at the University of Waterloo (ON, Canada). He is also appointed to the Departments of Sociology & Legal Studies and Geography and Environmental Management, is a Policy Fellow at the Balsillie School of International Affairs, and a Member of the Cybersecurity and Privacy Institute at the University of Waterloo. His work is funded by research grants from the Social Science and Humanities Research Council of Canada (SSHRC) and an Early Researcher Award from the Ontario Ministry of Research and Innovation.

His current research project focuses on disinformation, censorship, and political deliberation in the public sphere across a wide variety of national contexts and political regimes. He wrote Doing Computational Social Science (SAGE Publishing, 2021) from his experiences as a researcher and advisor, as well as teaching courses in computational social science, data science, and research methods to students from diverse disciplinary backgrounds at the undergraduate and graduate level.

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