Development Research in Practice : : The DIME Analytics Data Handbook.
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| Place / Publishing House: | Washington, D. C. : : World Bank Publications,, 2021. {copy}2021. |
| Year of Publication: | 2021 |
| Edition: | 1st ed. |
| Language: | English |
| Online Access: | |
| Physical Description: | 1 online resource (231 pages) |
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Table of Contents:
- Front Cover
- Contents
- Foreword
- Acknowledgments
- About the Authors
- Abbreviations
- Introduction
- How to read this book
- The DIME Wiki: A complementary resource
- Standardizing data work
- Standardizing coding practices
- The team behind this book
- Looking ahead
- References
- Chapter 1 Conducting reproducible, transparent, and credible research
- Developing a credible research project
- Conducting research transparently
- Analyzing data reproducibly and preparing a reproducibility package
- Looking ahead
- References
- Chapter 2 Setting the stage for effective and efficient collaboration
- Preparing a collaborative work environment
- Organizing code and data for replicable research
- Preparing to handle confidential data ethically
- Looking ahead
- References
- Chapter 3 Establishing a measurement framework
- Documenting data needs
- Translating research design to data needs
- Creating research design variables by randomization
- Looking ahead
- References
- Chapter 4 Acquiring development data
- Acquiring data ethically and reproducibly
- Collecting high-quality data using electronic surveys
- Handling data securely
- Looking ahead
- References
- Chapter 5 Cleaning and processing research data
- Making data "tidy"
- Implementing data quality checks
- Processing confidential data
- Preparing data for analysis
- Looking ahead
- References
- Chapter 6 Constructing and analyzing research data
- Creating analysis data sets
- Writing analysis code
- Creating reproducible tables and graphs
- Increasing efficiency of analysis with dynamic documents
- Looking ahead
- References
- Chapter 7 Publishing reproducible research outputs
- Publishing research papers and reports
- Preparing research data for publication
- Publishing a reproducible research package
- Looking ahead
- References.
- Chapter 8 Conclusion
- Bringing it all together
- Where to go from here
- Appendix A: The DIME Analytics Coding Guide
- Appendix B: DIME Analytics resource directory
- Appendix C: Research design for impact evaluation
- Boxes
- Box I.1 The Demand for Safe Spaces case study
- Box 1.1 Summary: Conducting reproducible, transparent, and credible research
- Box 1.2 Registering studies: A case study from the Demand for Safe Spaces project
- Box 1.3 Writing preanalysis plans: A case study from the Demand for Safe Spaces project
- Box 1.4 Preparing a reproducibility package: A case study from the Demand for Safe Spaces project
- Box 2.1 Summary: Setting the stage for effective and efficient collaboration
- Box 2.2 Preparing a collaborative work environment: A case study from the Demand for Safe Spaces project
- Box 2.3 Organizing files and folders: A case study from the Demand for Safe Spaces project
- Box 2.4 DIME master do-file template
- Box 2.5 Writing code that others can read: A case study from the Demand for Safe Spaces project
- Box 2.6 Writing code that others can run: A case study from the Demand for Safe Spaces project
- Box 2.7 Seeking ethical approval: An example from the Demand for Safe Spaces project
- Box 2.8 Obtaining informed consent: A case study from the Demand for Safe Spaces project
- Box 2.9 Ensuring the privacy of research subjects: An example from the Demand for Safe Spaces project
- Box 3.1 Summary: Establishing a measurement framework
- Box 3.2 Developing a data linkage table: An example from the Demand for Safe Spaces project
- Box 3.3 Creating data flowcharts: An example from the Demand for Safe Spaces project
- Box 3.4 An example of uniform-probability random sampling
- Box 3.5 An example of randomized assignment with multiple treatment arms
- Box 3.6 An example of reproducible randomization.
- Box 4.1 Summary: Acquiring development data
- Box 4.2 Determining data ownership: A case study from the Demand for Safe Spaces project
- Box 4.3 Piloting survey instruments: A case study from the Demand for Safe Spaces project
- Box 4.4 Checking data quality in real time: A case study from the Demand for Safe Spaces project
- Box 5.1 Summary: Cleaning and processing research data
- Box 5.2 Establishing a unique identifier: A case study from the Demand for Safe Spaces project
- Box 5.3 Tidying data: A case study from the Demand for Safe Spaces project
- Box 5.4 Assuring data quality: A case study from the Demand for Safe Spaces project
- Box 5.5 Implementing de-identification: A case study from the Demand for Safe Spaces project
- Box 5.6 Correcting data points: A case study from the Demand for Safe Spaces project
- Box 5.7 Recoding and annotating data: A case study from the Demand for Safe Spaces project
- Box 6.1 Summary: Constructing and analyzing research data
- Box 6.2 Integrating multiple data sources: A case study from the Demand for Safe Spaces project
- Box 6.3 Creating analysis variables: A case study from the Demand for Safe Spaces project
- Box 6.4 Documenting variable construction: A case study from the Demand for Safe Spaces project
- Box 6.5 Writing analysis code: A case study from the Demand for Safe Spaces project
- Box 6.6 Organizing analysis code: A case study from the Demand for Safe Spaces project
- Box 6.7 Visualizing data: A case study from the Demand for Safe Spaces project
- Box 6.8 Managing outputs: A case study from the Demand for Safe Spaces project
- Box 7.1 Summary: Publishing reproducible research outputs
- Box 7.2 Publishing research papers and reports: A case study from the Demand for Safe Spaces project
- Box 7.3 Publishing research data sets: A case study from the Demand for Safe Spaces project.
- Box 7.4 Releasing a reproducibility package: A case study from the Demand for Safe Spaces project
- Figures
- Figure I.1 Overview of the tasks involved in development research data work
- Figure B2.3.1 Folder structure of the Demand for Safe Spaces data work
- Figure B3.3.1 Flowchart of a project data map
- Figure B4.4.1 A sample dashboard of indicators of progress
- Figure 4.1 Data acquisition tasks and outputs
- Figure 5.1 Data-cleaning tasks and outputs
- Figure 6.1 Data analysis tasks and outputs
- Figure 7.1 Publication tasks and outputs
- Figure 8.1 Research data work outputs.