UID:
edoccha_9960785748702883
Format:
1 online resource (231 pages)
Edition:
1st ed.
ISBN:
1-4648-1695-6
Content:
Development Research in Practice leads the reader through a complete empirical research project, providing links to continuously updated resources on the DIME Wiki as well as illustrative examples from the Demand for Safe Spaces study. The handbook is intended to train users of development data how to handle data effectively, efficiently, and ethically. "In the DIME Analytics Data Handbook, the DIME team has produced an extraordinary public good: a detailed, comprehensive, yet easy-to-read manual for how to manage a data-oriented research project from beginning to end. It offers everything from big-picture guidance on the determinants of high-quality empirical research, to specific practical guidance on how to implement specific workflows-and includes computer code! I think it will prove durably useful to a broad range of researchers in international development and beyond, and I learned new practices that I plan on adopting in my own research group.†? -Marshall Burke, Associate Professor, Department of Earth System Science, and Deputy Director, Center on Food Security and the Environment, Stanford University "Data are the essential ingredient in any research or evaluation project, yet there has been too little attention to standardized practices to ensure high-quality data collection, handling, documentation, and exchange. Development Research in Practice: The DIME Analytics Data Handbook seeks to fill that gap with practical guidance and tools, grounded in ethics and efficiency, for data management at every stage in a research project. This excellent resource sets a new standard for the field and is an essential reference for all empirical researchers.†? -Ruth E. Levine, PhD, CEO, IDinsight "Development Research in Practice: The DIME Analytics Data Handbook is an important resource and a must-read for all development economists, empirical social scientists, and public policy analysts. Based on decades of pioneering work at the World Bank on data collection, measurement, and analysis, the handbook provides valuable tools to allow research teams to more efficiently and transparently manage their work flows-yielding more credible analytical conclusions as a result.†? -Edward Miguel, Oxfam Professor in Environmental and Resource Economics and Faculty Director of the Center for Effective Global Action, University of California, Berkeley "The DIME Analytics Data Handbook is a must-read for any data-driven researcher looking to create credible research outcomes and policy advice. By meticulously describing detailed steps, from project planning via ethical and responsible code and data practices to the publication of research papers and associated replication packages, the DIME handbook makes the complexities of transparent and credible research easier.†? -Lars Vilhuber, Data Editor, American Economic Association, and Executive Director, Labor Dynamics Institute, Cornell University.
Note:
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.
Language:
English
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