Development Research in Practice

Page 195

Appendix A The DIME Analytics Coding Guide

Most academic programs that prepare students for a career in the type of work discussed in this book spend a disproportionately small amount of time teaching students coding skills in relation to the share of professional time those students will spend writing code in their first few years after graduation. Recent masters-level graduates joining the DIME team have demonstrated very good theoretical knowledge but require a lot of training in practical skills. This is like an architecture graduate having learned how to sketch, describe, and discuss the concepts and requirements of a new building very well, but lacking the technical ability to contribute to a blueprint following professional standards that others can use and understand. The reasons for this disconnect are a topic for another book, but, in today’s data-driven world, people working in quantitative development research must be proficient collaborative programmers, which requires more than being able to compute correct numbers. This appendix starts by offering general and language-agnostic principles on how to write “good” code. Good code not only generates correct results but also is easily read and adapted by other professionals. The second section contains instructions on how to access and use the code examples provided in this book. The last section presents the DIME Analytics Stata Style Guide. Widely accepted and used style guides are common in most programming languages, but, as yet, there is no sufficiently encompassing style guide for Stata. The DIME Analytics Stata Style Guide is intended to increase the emphasis given to using, improving, sharing, and standardizing code style among the Stata community. It shares practices that greatly improve the quality of research projects coded in Stata. By applying the guidance provided in this appendix, readers will learn to write code that, like an architect’s blueprint, can be understood and used by everyone in the trade.

Writing good code Good code has two elements: (1) it is correct, in that it does not produce any errors and its outputs are the objects intended, and (2) it is useful and comprehensible to someone who has not seen it before (or even someone who sees it again weeks, months, or years later). Many researchers APPENDIX A: THE DIME ANALYTICS CODING GUIDE

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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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