Data Management for Social Scientists
From Files to Databases
Published on 15. March 2023
978-1-108-99766-9 (ISBN)
Description
The 'data revolution' offers many new opportunities for research in the social sciences. Increasingly, social and political interactions can be recorded digitally, leading to vast amounts of new data available for research. This poses new challenges for organizing and processing research data. This comprehensive introduction covers the entire range of data management techniques, from flat files to database management systems. It demonstrates how established techniques and technologies from computer science can be applied in social science projects, drawing on a wide range of different applied examples. This book covers simple tools such as spreadsheets and file-based data storage and processing, as well as more powerful data management software like relational databases. It goes on to address advanced topics such as spatial data, text as data, and network data. This book is one of the first to discuss questions of practical data management specifically for social science projects. This title is also available as Open Access on Cambridge Core.
More details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Illustrations
Worked examples or Exercises
File size
14,77 MB
ISBN-13
978-1-108-99766-9 (9781108997669)
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Schweitzer Classification
Other editions
Additional editions
Book
03/2023
Cambridge University Press
€108.50
Shipment within 15-20 days
Book
03/2023
Cambridge University Press
€35.00
Shipment within 15-20 days
Person
Content
Part I. Introduction: 1. Motivation; 2. Gearing up; 3. Data = content + structure; Part II. Data in Files: 4. Storing data in files; 5. Managing data in spreadsheets; 6. Basic data management in R; 7. R and the tidyverse; Part III. Data in Databases: 8. Introduction to relational databases; 9. Relational databases and multiple tables; 10. Database fine-tuning; Part IV. Special Types of Data: 11. Spatial data; 12. Text data; 13. Network data; Part V. Conclusion: 14. Best practices in data management.
