Development Research in Practice

Page 161

BOX 6.7  VISUALIZING DATA: A CASE STUDY FROM THE DEMAND FOR SAFE SPACES PROJECT The Demand for Safe Spaces team defined the settings for graphs in globals in the master analysis script. Using globals created a uniform visual style for all graphs produced by the project. These globals were then used across the project when creating graphs like the following: twoway (bar cum x, color(${col_aux_light})) (lpoly y x, color(${col_mixedcar})) (lpoly z x, color(${col_womencar})), ${plot_options}. 1 /************************************************************************* 2 *

Set plot options

3 *************************************************************************/ 4 5

set scheme s2color

6 7

global grlabsize

4

8

global col_mixedcar

`" "18 148 144" "'

9

global col_womencar

purple

10

global col_aux_bold

gs6

11

global col_aux_light

gs12

12

global col_highlight

cranberry

13

global col_box

gs15

14

global plot_options

graphregion(color(white))

///

15

bgcolor(white)

///

16

ylab(, glcolor(${col_box})) ///

17

xlab(, noticks)

18

global lab_womencar

Reserved space

19

global lab_mixedcar

Public space

For the complete do-file, visit the GitHub repository at https://git.io/JtgeT.

Creating reproducible tables and graphs Many outputs are created during the course of a project, including both raw outputs, such as tables and graphs, and final products, such as presentations, papers, and reports. During exploratory analysis, the team will consider different approaches to answer research questions and present answers. Although it is best to be transparent about different specifications tried and tests performed, only a few will ultimately be considered “main results.” These results will be exported from the statistical software. That is, they will be saved as tables and figures in file formats that the team can interact with more easily. For example, saving graphs as image files allows the team to review them quickly and to add them as exhibits to other documents. When these code outputs are first being created, it is necessary to agree on where to store them, what software and formats to use, and how to keep track of them. This discussion will save time CHAPTER 6: CONSTRUCTING AND ANALYZING RESEARCH DATA

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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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