Development Research in Practice

Page 31

Chapter 1  Conducting reproducible, transparent, and credible research Policy decisions are made every day using the results of development research, and these decisions have wide-reaching effects on the lives of millions. As the emphasis on evidence-informed policy grows, so too does the scrutiny placed on research methods and results. Three major components make up this scrutiny: credibility, transparency, and reproducibility. These three components contribute to one simple idea: research should be high quality and well documented. Research consumers, including policy makers who use the evidence to make decisions, should be able to examine and recreate it easily. In this framework, it is useful to think of research as a public service that requires researchers as a group to be accountable for their methods. Accountability means acting collectively to protect the credibility of development research by following modern practices for research planning and documentation. Across the social sciences, the open science movement has been fueled by concerns regarding the proliferation of low-quality research practices; data and code that are inaccessible to the public; analytical errors in major research papers; and, in some cases, even outright fraud. Although the development research community has not yet experienced major scandals, improvements clearly are needed in how code and data are handled as part of research. Moreover, having common standards and practices for creating and sharing materials, code, and data with others will improve the value of the work that researchers do. This chapter outlines principles and practices that help research consumers to have confidence in the conclusions reached. Each of the three components—credibility, transparency, and reproducibility—is discussed in turn. The first section covers research credibility. It presents three popular methods of committing to the use of particular research questions or methods and avoiding potential criticisms of cherry-picking results: study registration, preanalysis plans, and registered reports. The second section discusses how to apply principles of transparency to all research processes, which allows research teams to be more efficient and research consumers to understand thoroughly and evaluate the quality of

Chapter 1:  Conducting reproducible, transparent, and credible research

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Appendix C: Research design for impact evaluation

33min
pages 215-231

Appendix A: The DIME Analytics Coding Guide

24min
pages 195-210

Appendix B: DIME Analytics resource directory

3min
pages 211-214

8.1 Research data work outputs

6min
pages 190-194

Chapter 8: Conclusion

1min
page 189

7.4 Releasing a reproducibility package: A case study from the Demand for Safe Spaces project

3min
pages 184-186

7.1 Summary: Publishing reproducible research outputs

8min
pages 172-175

7.3 Publishing research data sets: A case study from the Demand for Safe Spaces project

10min
pages 180-183

7.2 Publishing research papers and reports: A case study from the Demand for Safe Spaces project

8min
pages 176-179

Chapter 7: Publishing reproducible research outputs

1min
page 171

6.1 Data analysis tasks and outputs

3min
pages 168-170

6.8 Managing outputs: A case study from the Demand for Safe Spaces project

10min
pages 163-167

6.7 Visualizing data: A case study from the Demand for Safe Spaces project

4min
pages 161-162

6.6 Organizing analysis code: A case study from the Demand for Safe Spaces project

4min
pages 159-160

6.5 Writing analysis code: A case study from the Demand for Safe Spaces project

3min
pages 157-158

6.4 Documenting variable construction: A case study from the Demand for Safe Spaces project

4min
pages 155-156

6.3 Creating analysis variables: A case study from the Demand for Safe Spaces project

1min
page 154

6.2 Integrating multiple data sources: A case study from the Demand for Safe Spaces project

9min
pages 150-153

6.1 Summary: Constructing and analyzing research data

10min
pages 146-149

Chapter 6: Constructing and analyzing research data

1min
page 145

5.7 Recoding and annotating data: A case study from the Demand for Safe Spaces project

3min
pages 140-141

5.6 Correcting data points: A case study from the Demand for Safe Spaces project

4min
pages 138-139

5.5 Implementing de-identification: A case study from the Demand for Safe Spaces project

9min
pages 134-137

5.1 Summary: Cleaning and processing research data

7min
pages 122-124

5.4 Assuring data quality: A case study from the Demand for Safe Spaces project

7min
pages 131-133

5.3 Tidying data: A case study from the Demand for Safe Spaces project

7min
pages 128-130

5.2 Establishing a unique identifier: A case study from the Demand for Safe Spaces project

7min
pages 125-127

Chapter 5: Cleaning and processing research data

1min
page 121

B4.4.1 A sample dashboard of indicators of progress

12min
pages 113-117

4.4 Checking data quality in real time: A case study from the Demand for Safe Spaces project

2min
page 112

4.3 Piloting survey instruments: A case study from the Demand for Safe Spaces project

14min
pages 106-111

4.2 Determining data ownership: A case study from the Demand for Safe Spaces project

16min
pages 100-105

B3.3.1 Flowchart of a project data map

37min
pages 81-96

B2.3.1 Folder structure of the Demand for Safe Spaces data work

36min
pages 55-72

Chapter 4: Acquiring development data

5min
pages 97-99

Chapter 3: Establishing a measurement framework

18min
pages 73-80

Chapter 1: Conducting reproducible, transparent, and credible research

35min
pages 31-46

Chapter 2: Setting the stage for effective and efficient collaboration

18min
pages 47-54

I.1 Overview of the tasks involved in development research data work

18min
pages 22-30

Introduction

2min
page 21
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