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Publisher:
Cambridge University Press
Online publication date:
March 2023
Print publication year:
2023
Online ISBN:
9781108990424
Creative Commons:
Creative Common License - CC Creative Common License - BY Creative Common License - NC
This content is Open Access and distributed under the terms of the Creative Commons Attribution licence CC-BY-NC 4.0 https://creativecommons.org/creativelicenses

Book 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.

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Contents

  • Frontmatter
    pp i-iv
  • Dedication
    pp v-vi
  • Contents
    pp vii-x
  • Preface
    pp xi-xiv
  • Part I - Introduction
    pp 1-2
  • 1 - Motivation
    pp 3-13
  • 2 - Gearing Up
    pp 14-22
  • 3 - Data = Content + Structure
    pp 23-36
  • Part II - Data in Files
    pp 37-38
  • 4 - Storing Data in Files
    pp 39-58
  • 5 - Managing Data in Spreadsheets
    pp 59-73
  • 6 - Basic Data Management in R
    pp 74-86
  • 7 - R and the tidyverse
    pp 87-100
  • Part III - Data in Databases
    pp 101-102
  • 8 - Introduction to Relational Databases
    pp 103-120
  • 9 - Relational Databases and Multiple Tables
    pp 121-134
  • 10 - Database Fine-Tuning
    pp 135-144
  • Part IV - Special Types of Data
    pp 145-146
  • 11 - Spatial Data
    pp 147-165
  • 12 - Text Data
    pp 166-186
  • 13 - Network Data
    pp 187-206
  • Part V - Conclusion
    pp 207-208
  • 14 - Best Practices in Data Management
    pp 209-218
  • Bibliography
    pp 219-222
  • Index
    pp 223-228

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