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E-raamat: Data Management for Social Scientists: From Files to Databases

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(Universität Konstanz, Germany)
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Data Management for Social Scientists: From Files to DatabasesSuurem pilt
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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. This title is also available as Open Access on Cambridge Core.

Much training in quantitative social science focuses on data analysis and fails to equip researchers with the skills to prepare the data required for this. This book is a comprehensive introduction to simple and advanced tools for data management, drawing on established concepts and techniques from computer science.

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Equips social scientists with the tools and techniques to conduct quantitative research in the age of big data.
Preface xi
PART I INTRODUCTION
1 Motivation
3(11)
1.1 Data Processing and the Research Cycle
4(1)
1.2 What We Do (and Don't Do) in this Book
5(2)
1.3 Why Focus on Data Processing?
7(2)
1.4 Data in Files vs. Data in Databases
9(2)
1.5 Target Audience, Requirements and Software
11(1)
1.6 Plan of the Book
12(2)
2 Gearing Up
14(9)
2.1 R and RStudio
14(2)
2.2 Setting Up the Project Environment for Your Work
16(4)
2.3 The PostgreSQL Database System
20(2)
2.4 Summary and Outlook
22(1)
3 Data = Content + Structure
23(16)
3.1 What Is Data?
23(1)
3.2 Data Content and Structure
24(2)
3.3 Tables, Tables, Tables
26(4)
3.4 The Structure of Tables Matters
30(5)
3.5 Summary and Outlook
35(4)
PART II DATA IN FILES
4 Storing Data in Files
39(20)
4.1 Text and Binary Files
40(3)
4.2 File Formats for Tabular Data
43(11)
4.3 Transparent and Efficient Use of Files
54(3)
4.4 Summary and Outlook
57(2)
5 Managing Data in Spreadsheets
59(15)
5.1 Application: Spatial Inequality
60(3)
5.2 Spreadsheet Tables and (the Lack of) Structure
63(1)
5.3 Retrieving Data from a Table
64(2)
5.4 Changing Table Structure and Content
66(1)
5.5 Aggregating Data from a Table
67(3)
5.6 Exporting Spreadsheet Data
70(1)
5.7 Results: Spatial Inequality
70(1)
5.8 Summary and Outlook
71(3)
6 Basic Data Management in R
74(13)
6.1 Application: Inequality and Economic Performance in the US
75(1)
6.2 Loading the Data
76(3)
6.3 Merging Tables
79(3)
6.4 Aggregating Data from a Table
82(2)
6.5 Results: Inequality and Economic Performance in the US
84(1)
6.6 Summary and Outlook
85(2)
7 R and the tidyverse
87(16)
7.1 Application: Global Patterns of Inequality across Regime Types
88(1)
7.2 A New Operator: The Pipe
89(1)
7.3 Loading the Data
90(2)
7.4 Merging the WID and Polity IV Datasets
92(1)
7.5 Grouping and Aggregation
93(3)
7.6 Results: Global Patterns of Inequality across Regime Types
96(1)
7.7 Other Useful Functions in the tidyverse
97(2)
7.8 Summary and Outlook
99(4)
PART III DATA IN DATABASES
8 Introduction to Relational Databases
103(18)
8.1 Database Servers and Clients
105(3)
8.2 SQL Basics
108(1)
8.3 Application: Electoral Disproportionality by Country
109(1)
8.4 Creating a Table with National Elections
110(5)
8.5 Computing Electoral Disproportionality
115(2)
8.6 Results: Electoral Disproportionality by Country
117(1)
8.7 Summary and Outlook
118(3)
9 Relational Databases and Multiple Tables
121(14)
9.1 Application: The Rise of Populism in Europe
122(1)
9.2 Adding the Tables
123(2)
9.3 Joining the Tables
125(2)
9.4 Merging Data from the PopuList
127(2)
9.5 Maintaining Referential Integrity
129(2)
9.6 Results: The Rise of Populism in Europe
131(1)
9.7 Summary and Outlook
132(3)
10 Database Fine-Tuning
135(12)
10.1 Speeding Up Data Access with Indexes
136(4)
10.2 Collaborative Data Management with Multiple Users
140(3)
10.3 Summary and Outlook
143(4)
PART IV SPECIAL TYPES OF DATA
11 Spatial Data
147(19)
11.1 What Is Spatial Data?
147(3)
11.2 Application: Patterns of Violence in the Bosnian Civil War
150(1)
11.3 Reading and Visualizing Spatial Data in R
151(7)
11.4 Spatial Data in a Relational Database
158(5)
11.5 Results: Patterns of Violence in the Bosnian Civil War
163(1)
11.6 Summary and Outlook
164(2)
12 Text Data
166(21)
12.1 What Is Textual Data?
167(2)
12.2 Application: References to (In)equality in UN Speeches
169(1)
12.3 Working with Strings in (Base) R
170(5)
12.4 Natural Language Processing with quanteda
175(4)
12.5 Using PostgreSQL to Manage Documents
179(4)
12.6 Results: References to (In)equality in UN Speeches
183(1)
12.7 Summary and Outlook
184(3)
13 Network Data
187(22)
13.1 What Is Network Data?
187(3)
13.2 Application: Trade and Democracy
190(1)
13.3 Exploring Network Data in R with igraph
191(6)
13.4 Network Data in a Relational Database
197(7)
13.5 Results: Trade and Democracy
204(1)
13.6 Summary and Outlook
205(4)
PART V CONCLUSION
14 Best Practices in Data Management
209(10)
14.1 Two General Recommendations
209(3)
14.2 Collaborative Data Management
212(2)
14.3 Disseminating Research Data and Code
214(2)
14.4 Summary and Outlook
216(3)
Bibliography 219(4)
Index 223
Nils B. Weidmann is Professor of Political Science at the University of Konstanz.
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