Development Research in Practice

Page 157

and, for each output it creates, explicitly loads data before analyzing them. This setup encourages data manipulation to be done earlier in the workflow (that is, in separate cleaning and construction scripts). It also prevents the common problem of having analysis scripts that depend on other analysis scripts being run before them. Such dependencies tend to require manual instructions so that all necessary chunks of code are run in the right order. Coding each task so that it is completely independent of all other code, except for the master script, is recommended. It is possible to go so far as to code every output in a separate script, but the key is to make sure that it is clear which data sets are used for each output and which code chunks implement each piece of analysis (see box 6.5 for an example of an analysis script structured like this). BOX 6.5  WRITING ANALYSIS CODE: A CASE STUDY FROM THE DEMAND FOR SAFE SPACES PROJECT The Demand for Safe Spaces team split the analysis scripts into one script per output and reloaded the analysis data before each output. This process ensured that the final exhibits could be generated independently from the analysis data. No variables were constructed in the analysis scripts: the only transformation performed was to subset the data or aggregate them to a higher unit of observation. This transformation guaranteed that the same data were used across all analysis scripts. The following is an example of a short analysis do-file: 1 /**************************************************************************************** 2 *

Demand for "Safe Spaces": Avoiding Harassment and Stigma

*

3 ***************************************************************************************** 4

OUTLINE:

PART 1: Load data

5

PART 2: Run regressions

6

PART 3: Export table

7

REQUIRES: ${dt_final}/platform_survey_constructed.dta

8

CREATES:

9

WRITEN BY:

${out_tables}/priming.tex Luiza Andrade

10 11 ***************************************************************************************** 12 *

PART 1: Load data

13 ****************************************************************************************/ 14 15

use "${dt_final}/platform_survey_constructed.dta", clear

16 17 /**************************************************************************************** 18 *

PART 2: Run regressions

19 ****************************************************************************************/ 20 21

reg scorereputation i.q_group, robust

22

est sto priming1

23

(Box continues on next page)

CHAPTER 6: CONSTRUCTING AND ANALYZING RESEARCH DATA

137


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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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