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. |
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Bjärkefur, Kristoffer. Development Research in Practice : The DIME Analytics Data Handbook. 1st ed. Washington, D. C. : World Bank Publications, 2021. {copy}2021. 1 online resource (231 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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. Description based on publisher supplied metadata and other sources. Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. Data curation. Business--Data processing--Management. Economic development--Research--Methodology. Electronic books. Cardoso de Andrade, Luíza. Daniels, Benjamin. Jones, Maria Ruth. Print version: Bjärkefur, Kristoffer Development Research in Practice Washington, D. C. : World Bank Publications,c2021 9781464816949 ProQuest (Firm) https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=6724973 Click to View |
language |
English |
format |
eBook |
author |
Bjärkefur, Kristoffer. |
spellingShingle |
Bjärkefur, Kristoffer. Development Research in Practice : The DIME Analytics Data Handbook. 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. |
author_facet |
Bjärkefur, Kristoffer. Cardoso de Andrade, Luíza. Daniels, Benjamin. Jones, Maria Ruth. |
author_variant |
k b kb |
author2 |
Cardoso de Andrade, Luíza. Daniels, Benjamin. Jones, Maria Ruth. |
author2_variant |
d a l c dal dalc b d bd m r j mr mrj |
author2_role |
TeilnehmendeR TeilnehmendeR TeilnehmendeR |
author_sort |
Bjärkefur, Kristoffer. |
title |
Development Research in Practice : The DIME Analytics Data Handbook. |
title_sub |
The DIME Analytics Data Handbook. |
title_full |
Development Research in Practice : The DIME Analytics Data Handbook. |
title_fullStr |
Development Research in Practice : The DIME Analytics Data Handbook. |
title_full_unstemmed |
Development Research in Practice : The DIME Analytics Data Handbook. |
title_auth |
Development Research in Practice : The DIME Analytics Data Handbook. |
title_new |
Development Research in Practice : |
title_sort |
development research in practice : the dime analytics data handbook. |
publisher |
World Bank Publications, |
publishDate |
2021 |
physical |
1 online resource (231 pages) |
edition |
1st ed. |
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. |
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Electronic books. |
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Electronic books. |
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C. :</subfield><subfield code="b">World Bank Publications,</subfield><subfield code="c">2021.</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">{copy}2021.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (231 pages)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="588" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources.</subfield></datafield><datafield tag="590" ind1=" " ind2=" "><subfield code="a">Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. </subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Data curation.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Business--Data processing--Management.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Economic development--Research--Methodology.</subfield></datafield><datafield tag="655" ind1=" " ind2="4"><subfield code="a">Electronic books.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cardoso de Andrade, Luíza.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Daniels, Benjamin.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jones, Maria Ruth.</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Print version:</subfield><subfield code="a">Bjärkefur, Kristoffer</subfield><subfield code="t">Development Research in Practice</subfield><subfield code="d">Washington, D. C. : World Bank Publications,c2021</subfield><subfield code="z">9781464816949</subfield></datafield><datafield tag="797" ind1="2" ind2=" "><subfield code="a">ProQuest (Firm)</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=6724973</subfield><subfield code="z">Click to View</subfield></datafield></record></collection> |