Data Management for Social Scientists
From Files to Databases
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Part of Methodological Tools in the Social Sciences
- Author: Nils B. Weidmann, Universität Konstanz, Germany
- Publication planned for: August 2022
- availability: Not yet published - available from August 2022
- format: Paperback
- isbn: 9781108964784
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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.
Read more- Provides a hands-on introduction to data processing in the social sciences
- Introduces concepts and tools from computer science tailored to a social science audience
- Covers three advanced types of social science data (spatial, text and network data) that are becoming increasingly important for research in the digital age
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×Product details
- Publication planned for: August 2022
- format: Paperback
- isbn: 9781108964784
- length: 200 pages
- dimensions: 229 x 152 mm
- availability: Not yet published - available from August 2022
Table of Contents
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.
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