Data management for social scientists: from files to databases
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cambridge
Cambridge University Press
[2023]
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| Series: | Methodological tools in the social sciences
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | xiii, 227 Seiten Illustrationen, Diagramme, Karten |
| ISBN: | 9781108845670 9781108964784 |
Staff View
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Record in the Search Index
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| adam_text | Contents page xi Preface PART I i 2 3 Motivation i. i Data Processing and the Research Cycle 1.2 What We Do (and Don’t Do) in this Book 1.3 Why Focus on Data Processing? 1.4 Data in Files vs. Data in Databases i. 5 Target Audience, Requirements and Software 1.6 Plan of the Book Gearing Up 2.1 R and RStudio 2.2 Setting Up the Project Environment for Your Work 2.3 The PostgreSQL Database System 2.4 Summary and Outlook Data = Content + Structure 3.1 What Is Data? 3.2 Data Content and Structure 3.3 Tables, Tables, Tables 3.4 The Structure of Tables Matters 3.5 Summary and Outlook PART II 4 INTRODUCTION 3 4 5 7 9 11 11 14 14 16 20 22 23 23 24 26 30 35 DATA IN FILES Storing Data in Files 4.1 Text and Binary Files 4.2 File Formats for Tabular Data vii 39 40 43
viii 5 6 7 Contents 4.3 Transparent and Efficient Use of Files 4.4 Summary and Outlook 54 57 Managing Data in Spreadsheets 5.1 Application: Spatial Inequality 5.2 Spreadsheet Tables and (the Lack of) Structure 5.3 Retrieving Data from a Table 5.4 Changing Table Structure and Content 5.5 Aggregating Data from a Table 5.6 Exporting Spreadsheet Data 5.7 Results: Spatial Inequality 5.8 Summary and Outlook Basic Data Management in R 6.1 Application: Inequality and Economic Performance in the US 6.2 Loading the Data 6.3 Merging Tables 6.4 Aggregating Data from a Table 6.5 Results: Inequality and Economic Performance in the US 6.6 Summary and Outlook R and the tidyverse 7.1 Application: Global Patterns of Inequality across Regime Types 7.2 A New Operator: The Pipe 7.3 Loading the Data 7.4 Merging the WID and Polity IV Datasets 7.5 Grouping and Aggregation 7.6 Results: Global Patterns of Inequality across Regime Types 7.7 Other Useful Functions in the tidyverse 7.8 Summary and Outlook 59 60 63 64 66 67 70 70 71 74 75 76 79 82 84 85 87 PART III 88 89 90 92 93 96 97 99 DATA IN DATABASES 8 Introduction to Relational Databases 8.1 Database Servers and Clients 8.2 SQL Basics 8.3 Application: Electoral Disproportionality by Country 8.4 Creating a Table with National Elections 8.5 Computing Electoral Disproportionality 8.6 Results: Electoral Disproportionality by Country 8.7 Summary and Outlook 103 105 108 109 110 115 117 118 9 Relational Databases and Multiple Tables 9.1 Application: The Rise of Populism in Europe 9.2 Adding the Tables 9.3 Joining the Tables 121 122 123 125
Contents 9.4 9.5 9.6 9.7 Merging Data from the PopuList Maintaining Referential Integrity Results: The Rise of Populism in Europe Summary and Outlook 10 Database Fine-Tuning 10. x Speeding Up Data Access with Indexes 10.2 Collaborative Data Management with Multiple Users 10.3 Summary and Outlook PART IV 127 129 131 132 135 136 140 143 SPECIAL TYPES OF DATA ii Spatial Data 11.1 What Is Spatial Data? 11.2 Application: Patterns of Violence in the Bosnian Civil War 11.3 Reading and Visualizing Spatial Data in R 11.4 Spatial Data in a Relational Database 11.5 Results: Patterns of Violence in the Bosnian Civil War 11.6 Summary and Outlook 12 Text Data 12.i What Is Textual Data? 12.2 Application: References to (In)equality in UN Speeches 12.3 Working with Strings in (Base) R 12.4 Natural Language Processing with quanteda 12.5 Using PostgreSQL to Manage Documents 12.6 Results: References to (In)equality in UN Speeches 12.7 Summary and Outlook 13 Network Data 13.1 What Is Network Data? 13.2 Application: Trade andDemocracy 13.3 Exploring Network Data in R with igraph 13.4 Network Data in a Relational Database 13.5 Results: Trade and Democracy 13.6 Summary and Outlook PART V 14 ix 147 147 150 151 158 163 164 166 167 169 170 175 179 183 184 187 187 190 191 197 204 205 CONCLUSION Best Practices in Data Management 14.i Two General Recommendations 14.2 Collaborative Data Management 14.3 Disseminating Research Data and Code 14.4 Summary and Outlook 209 209 212 214 216 Bibliography 219 Index 223
The data revolution offers many new opportunities for research in the social sciences. Increasingly, traces of social and political interactions can be recorded di gitally, leading to wist amounts of new data that become available for research. This poses new challenges for the way we organize and process research data. This book covers the entire range of data management techniques, from flat files to database management systems. It demonstrates how established techniques and technolo gies from computer science can be applied in social science projects, drawing on a wide range of different applied examples. The book covers simple tools such as spreadsheets as well as file-based data storage and processing, and then moves on to more powerful data management software such as relational databases. In the final part of the book, it deals with advanced topics such as spatial data, text as data, and network data.
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| adam_txt |
Contents page xi Preface PART I i 2 3 Motivation i. i Data Processing and the Research Cycle 1.2 What We Do (and Don’t Do) in this Book 1.3 Why Focus on Data Processing? 1.4 Data in Files vs. Data in Databases i. 5 Target Audience, Requirements and Software 1.6 Plan of the Book Gearing Up 2.1 R and RStudio 2.2 Setting Up the Project Environment for Your Work 2.3 The PostgreSQL Database System 2.4 Summary and Outlook Data = Content + Structure 3.1 What Is Data? 3.2 Data Content and Structure 3.3 Tables, Tables, Tables 3.4 The Structure of Tables Matters 3.5 Summary and Outlook PART II 4 INTRODUCTION 3 4 5 7 9 11 11 14 14 16 20 22 23 23 24 26 30 35 DATA IN FILES Storing Data in Files 4.1 Text and Binary Files 4.2 File Formats for Tabular Data vii 39 40 43
viii 5 6 7 Contents 4.3 Transparent and Efficient Use of Files 4.4 Summary and Outlook 54 57 Managing Data in Spreadsheets 5.1 Application: Spatial Inequality 5.2 Spreadsheet Tables and (the Lack of) Structure 5.3 Retrieving Data from a Table 5.4 Changing Table Structure and Content 5.5 Aggregating Data from a Table 5.6 Exporting Spreadsheet Data 5.7 Results: Spatial Inequality 5.8 Summary and Outlook Basic Data Management in R 6.1 Application: Inequality and Economic Performance in the US 6.2 Loading the Data 6.3 Merging Tables 6.4 Aggregating Data from a Table 6.5 Results: Inequality and Economic Performance in the US 6.6 Summary and Outlook R and the tidyverse 7.1 Application: Global Patterns of Inequality across Regime Types 7.2 A New Operator: The Pipe 7.3 Loading the Data 7.4 Merging the WID and Polity IV Datasets 7.5 Grouping and Aggregation 7.6 Results: Global Patterns of Inequality across Regime Types 7.7 Other Useful Functions in the tidyverse 7.8 Summary and Outlook 59 60 63 64 66 67 70 70 71 74 75 76 79 82 84 85 87 PART III 88 89 90 92 93 96 97 99 DATA IN DATABASES 8 Introduction to Relational Databases 8.1 Database Servers and Clients 8.2 SQL Basics 8.3 Application: Electoral Disproportionality by Country 8.4 Creating a Table with National Elections 8.5 Computing Electoral Disproportionality 8.6 Results: Electoral Disproportionality by Country 8.7 Summary and Outlook 103 105 108 109 110 115 117 118 9 Relational Databases and Multiple Tables 9.1 Application: The Rise of Populism in Europe 9.2 Adding the Tables 9.3 Joining the Tables 121 122 123 125
Contents 9.4 9.5 9.6 9.7 Merging Data from the PopuList Maintaining Referential Integrity Results: The Rise of Populism in Europe Summary and Outlook 10 Database Fine-Tuning 10. x Speeding Up Data Access with Indexes 10.2 Collaborative Data Management with Multiple Users 10.3 Summary and Outlook PART IV 127 129 131 132 135 136 140 143 SPECIAL TYPES OF DATA ii Spatial Data 11.1 What Is Spatial Data? 11.2 Application: Patterns of Violence in the Bosnian Civil War 11.3 Reading and Visualizing Spatial Data in R 11.4 Spatial Data in a Relational Database 11.5 Results: Patterns of Violence in the Bosnian Civil War 11.6 Summary and Outlook 12 Text Data 12.i What Is Textual Data? 12.2 Application: References to (In)equality in UN Speeches 12.3 Working with Strings in (Base) R 12.4 Natural Language Processing with quanteda 12.5 Using PostgreSQL to Manage Documents 12.6 Results: References to (In)equality in UN Speeches 12.7 Summary and Outlook 13 Network Data 13.1 What Is Network Data? 13.2 Application: Trade andDemocracy 13.3 Exploring Network Data in R with igraph 13.4 Network Data in a Relational Database 13.5 Results: Trade and Democracy 13.6 Summary and Outlook PART V 14 ix 147 147 150 151 158 163 164 166 167 169 170 175 179 183 184 187 187 190 191 197 204 205 CONCLUSION Best Practices in Data Management 14.i Two General Recommendations 14.2 Collaborative Data Management 14.3 Disseminating Research Data and Code 14.4 Summary and Outlook 209 209 212 214 216 Bibliography 219 Index 223
The "data revolution'' offers many new opportunities for research in the social sciences. Increasingly, traces of social and political interactions can be recorded di gitally, leading to wist amounts of new data that become available for research. This poses new challenges for the way we organize and process research data. This book covers the entire range of data management techniques, from flat files to database management systems. It demonstrates how established techniques and technolo gies from computer science can be applied in social science projects, drawing on a wide range of different applied examples. The book covers simple tools such as spreadsheets as well as file-based data storage and processing, and then moves on to more powerful data management software such as relational databases. In the final part of the book, it deals with advanced topics such as spatial data, text as data, and network data. |
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| spelling | Weidmann, Nils B. 1976- Verfasser (DE-588)1072067153 aut Data management for social scientists from files to databases Nils B. Weidmann (University of Konstanz, Germany) Cambridge Cambridge University Press [2023] © 2023 xiii, 227 Seiten Illustrationen, Diagramme, Karten txt rdacontent n rdamedia nc rdacarrier Methodological tools in the social sciences Datenmanagement (DE-588)4213132-7 gnd rswk-swf Forschungsdaten (DE-588)1098579690 gnd rswk-swf Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Informatik (DE-588)4026894-9 gnd rswk-swf Datenbank (DE-588)4011119-2 gnd rswk-swf Social sciences / Research Social sciences / Data processing Datenmanagement (DE-588)4213132-7 s Forschungsdaten (DE-588)1098579690 s Sozialwissenschaften (DE-588)4055916-6 s Datenbank (DE-588)4011119-2 s Informatik (DE-588)4026894-9 s DE-604 Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033831744&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033831744&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Weidmann, Nils B. 1976- Data management for social scientists from files to databases Datenmanagement (DE-588)4213132-7 gnd Forschungsdaten (DE-588)1098579690 gnd Sozialwissenschaften (DE-588)4055916-6 gnd Informatik (DE-588)4026894-9 gnd Datenbank (DE-588)4011119-2 gnd |
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| title | Data management for social scientists from files to databases |
| title_auth | Data management for social scientists from files to databases |
| title_exact_search | Data management for social scientists from files to databases |
| title_exact_search_txtP | Data management for social scientists from files to databases |
| title_full | Data management for social scientists from files to databases Nils B. Weidmann (University of Konstanz, Germany) |
| title_fullStr | Data management for social scientists from files to databases Nils B. Weidmann (University of Konstanz, Germany) |
| title_full_unstemmed | Data management for social scientists from files to databases Nils B. Weidmann (University of Konstanz, Germany) |
| title_short | Data management for social scientists |
| title_sort | data management for social scientists from files to databases |
| title_sub | from files to databases |
| topic | Datenmanagement (DE-588)4213132-7 gnd Forschungsdaten (DE-588)1098579690 gnd Sozialwissenschaften (DE-588)4055916-6 gnd Informatik (DE-588)4026894-9 gnd Datenbank (DE-588)4011119-2 gnd |
| topic_facet | Datenmanagement Forschungsdaten Sozialwissenschaften Informatik Datenbank |
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