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Data Management for Social Scientists: From Files to Databases: Methodological Tools in the Social Sciences

Autor Nils B. Weidmann
en Limba Engleză Paperback – 8 mar 2023
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.
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Specificații

ISBN-13: 9781108964784
ISBN-10: 1108964788
Pagini: 200
Dimensiuni: 152 x 229 x 13 mm
Greutate: 0.33 kg
Ediția:Nouă
Editura: Cambridge University Press
Colecția Cambridge University Press
Seria Methodological Tools in the Social Sciences

Locul publicării:New York, United States

Cuprins

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.

Notă biografică


Descriere

Equips social scientists with the tools and techniques to conduct quantitative research in the age of big data.