
Data Management for Social Scientists
From Files to Databases
Nils B. Weidmann(Author)
Cambridge University Press
Published on 9. March 2023
Book
Hardback
242 pages
978-1-108-84567-0 (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
Edition type
New edition
Product notice
sewn/stitched
Cloth over boards
Illustrations
Worked examples or Exercises
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 14 mm
Weight
494 gr
ISBN-13
978-1-108-84567-0 (9781108845670)
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Schweitzer Classification
Other editions
Additional editions

E-Book
03/2023
Cambridge University Press
€33.99
Available for download

Book
03/2023
Cambridge University Press
€35.00
Shipment within 15-20 days

Nils B. Weidmann
Data Management for Social Scientists
E-Book
02/2023
Cambridge University Press
€33.99
Available for download
Person
Nils B. Weidmann is Professor of Political Science at the University of Konstanz.
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.