Overview

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.

Full Product Details

Author:   John McLevey
Publisher:   SAGE Publications Ltd
Imprint:   SAGE Publications Ltd
Weight:   1.200kg
ISBN:  

9781526468185

ISBN 10:   1526468182
Pages:   688
Publication Date:   27 December 2021
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Introduction: Learning to do computational social science Part I: Foundations Chapter 1: Setting up your open source scientific computing environment Chapter 2: Python programming: The basics Chapter 3: Python programming: Data structures, functions and files Chapter 4: Collecting data from Application Programming Interfaces (APIs) Chapter 5: Collecting data from the web: Scraping Chapter 6: Processing structured data Chapter 7: Visualisation and exploratory data analysis Chapter 8: Latent factors and components Part II: Fundamentals of text analysis Chapter 9: Processing natural language data Chapter 10: Iterative text analysis Chapter 11: Exploratory text analysis Chapter 12: Text similarity and latent semantic space Part III: Fundamentals of network analysis Chapter 13: Social networks and relational thinking Chapter 14: Connection and clustering in social networks Chapter 15: Influence, inequality and power in social networks Chapter 16: Going viral: Modelling the epidemic spread of simple contagions Chapter 17: Not so fast: Modelling the diffusion of complex contagions Part IV: Research ethics and machine learning Chapter 18: Research ethics, politics and practices Chapter 19: Machine learning: Symbolic and connectionist Chapter 20: Supervised learning with regression and cross-validation Chapter 21: Supervised learning with tree-based models Chapter 22: Neural networks and deep learning Chapter 23: Developing neural network models with Keras and Tensorflow Part V: Bayesian machine learning and probabilistic programming Chapter 24: Statistical machine learning and generative models Chapter 25: Probability: A primer Chapter 26: Approximate posterior inference with stochastic sampling and MCMC Part VI: Bayesian data analysis and latent variable modelling with relational and text data Chapter 27: Bayesian regression models with probabilistic programming Chapter 28: Bayesian hierarchical regression modelling Chapter 29: Variational Bayes and the craft of generative topic modelling Chapter 30: Generative network analysis with Bayesian stochastic blockmodels Part VII: Embeddings, transformer models and named entity recognition Chapter 31: Can we model meaning?: Contextual representation and neural word embeddings Chapter 32: Named entity recognition, transfer learning and transformer models

Reviews

"This book adds two key components critical for social science students that are often lacking in other texts. The first is a discussion of key computing components starting at the most basic – often these steps are overlooked in texts written for computer science students. Second is the excellent grounding in and integration with social science theory and concepts. -- Lorien Jasny McLevey has provided us with a book that clearly imparts the technical skills of computational methods, but crucially he has done so in a way that accessibly embeds them in a social science context. Whilst we’ve previously been told ""how"" to do computational social science, McLevey expertly ensures we understand ""why"" too. -- James Allen-Robertson"

This book adds two key components critical for social science students that are often lacking in other texts. The first is a discussion of key computing components starting at the most basic - often these steps are overlooked in texts written for computer science students. Second is the excellent grounding in and integration with social science theory and concepts. -- Lorien Jasny McLevey has provided us with a book that clearly imparts the technical skills of computational methods, but crucially he has done so in a way that accessibly embeds them in a social science context. Whilst we've previously been told how to do computational social science, McLevey expertly ensures we understand why too. -- James Allen-Robertson

This book adds two key components critical for social science students that are often lacking in other texts. The first is a discussion of key computing components starting at the most basic - often these steps are overlooked in texts written for computer science students. Second is the excellent grounding in and integration with social science theory and concepts. -- Lorien Jasny

Author Information

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.

Tab Content 6

Author Website:  

Countries Available

All regions