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= Guidelines =
== Pre- analysis plans ==
== Code replication ==
== Data publication ==
== Software for reproducible research ==
GitHub] - [https:// services. github. com/on- demand/intro-to- github/ introductory training] available through GitHub Services
Additional Resources =
From the Abul Latif Jameel Poverty Action Lab ( JPAL)
povertyactionlab.org/research -resources/ transparency-and -reproducibility Transparency and Reproducibility]
* [https://www..org/research/and and
From Innovations for Policy Action ( IPA)
http://www. poverty- action.org/ sites/ default/ files/ publications/ IPA%27s%20Best%20Practices%20for%20Data%20and%20Code%20Management_Nov2015. pdf Reproducible Research: Best Practices for Data and Code Management]
http://www. poverty-action. org/ sites/ default/ files/ Guidelines- for-data- publication.pdf Guidelines for data publication]
dataverse. harvard. edu/ dataverse/ socialsciencercts Randomized Control Trials in the Social Science Dataverse]
://www.-.org/ // //.
-data -.pdf ]
Center for Open Science
* [ https:/ /cos. io/our-services/top-guidelines/ Transparency and Openness Guidelines] , summarized in a [https://osf.io/pvf56/?_ga=1.225140506.1057649246.1484691980 1-Page Handout]
Berkeley Initiative for Transparency in the Social Sciences
* [http://www.bitss.org/education/manual-of-best-practices/ Manual of Best Practices in Transparent Social Science Research]
Berkeley Initiative for Transparency in the Social Sciences [http://www.bitss.org/education/manual-of-best-practices/ Manual of Best Practices in Transparent Social Science Research
Reproducible research is the system of documenting and publishing results of an impact evaluation. At the very least, reproducibility allows other researchers to analyze the same data to get the same results as the original study, which strengthens the conclusions of the original study. It is important to push researchers towards publishing reproducible research because the path to research findings is just as important as the findings themselves.
Replication and Reproducibility
Replication is a process where different researchers conduct the same study independently in different samples and find similar conclusions. It adds more validity to the conclusions of an empirical study. However, in most field experiments, the research team cannot create the same conditions for replication. Different populations can respond differently to the same treatment, and replication is often too expensive.
In such cases, the researchers should still try to achieve reproducibility. There are four key elements of reproducible research - data documentation, data publication, code publication, and output publication.
Data documentation deals with all aspects of an impact evaluation - sampling, data collection, cleaning, and analysis. Proper documentation not only produces reproducible data for publication in the future , but also ensures high quality data in the present. For example, a field coordinator (FC) may notice that some respondents do not understand a questionnaire because of reading difficulties. If the field coordinator (FC) does not document this issue, the research assistant will not flag these observations during data cleaning. And if the research assistant does not document why the observations were flagged, and what the flag means, it will affect the results of the analysis.
Accordingly, in the lead up to, and during data collection, the research team should follow these guidelines for data documentation.
- Comments. Use comments in your code to document the reasons for a particular line or group of commands. In Stata, for instance, use
* to insert comments.
- Folders. Create separate folders to store all documentation related to the project in separate files. For example, in Github, the research team can store notes about each folder and its contents under README.md.
- Consult data collection teams. Throughout the process of data cleaning, take extensive inputs from the people who are responsible for collecting data. This could be a field team, a government ministry responsible for administrative data, or a technology firm that handles remote sensing data.
- Exploratory analysis. While cleaning the data set, look for issues such as outliers, and data entry errors like missing or duplicate values. Record these observations for use during the process of variable construction and analysis.
- Feedback. When researchers submit codes for review, or release data on a public platform (such as the Microdata Catalog), others may provide feedback, either positive or negative. It is important to document these comments as well, as this can improve the quality of the results of the impact evaluation.
- Corrections. Include records of any corrections made to the data, as well as to the code. For example, based on feedback, the research team may realize that they forgot to drop duplicated entries. Publish these corrections in the documentation folder, along with the communications where theses issues were reported.
- Confidential information. The research team must be careful not to include confidential information, or any information that is not securely stored.
There are various tools available for data documentation. GitHub and Open Science Framework (OSF) are two such tools.
- The Open Science Framework (OSF). It supports documentation by allowing users to store files and version histories, and collaborate using OSF Wiki pages.
- GitHub. This is a useful tool for managing tasks and responsibilities across the research team. Like OSF, Git also stores every version of every file. It supports documentation through Wiki pages and README.md.
Data publication is the public release of all data once the process of data collection and analysis is complete. Data publication must be accompanied by proper data documentation. Ideally, the research team should publish all data that is needed for others to reproduce every step of the original code, from cleaning to analysis. However, this may not always be feasible, since data often contains personally identifiable information (PII) and other confidential information.
The research team must keep the following things in mind to ensure that the data is well-organized before publishing:
- Cleaning. Ensure that the data has been cleaned and is well-labelled.
- Missing variables. Make sure the data contains all variables used during data analysis, and includes uniquely identifying variables.
- De-identification. Careful de-identification is important to maintain the privacy of respondents and to meet research ethics standards. The research team must carefully de-identify any sensitive or personally-identifying information (PII) such as names, locations, or financial records before release.
DIME Analytics has developed the following resources to help researchers store and organize data for public release.
Data publication tools
There are several free software tools that allow the research team to publicly release the data and the associated documentation, including GitHub and Open Science Framework, and Research Gate.
Each of these platforms can handle organized directories and can provide a static uniform resource locator (URL) which makes it easy to collaborate with other users.
- ResearchGate. It allows users to assign a digital object identifier (DOI) to published work, which they can then share with external researchers for review or replication.
- The Open Science Framework (OSF). It is an online platform which allows members of a research team to store all project data, and even publish reports using OSF preprints.
- DIME survey data. DIME also publishes and releases survey data through the Microdata Catalog. However, access to the data may be restricted, and some variables are not allowed to be published.
Code publication is another key element of reproducible research. Sometimes academic journals ask for reproducible code (and data) along with the actual academic paper. Even if they don't, it is a good practice to share codes and data with others. The research team should ensure that external researchers have access to, and can execute the same code and data that was used during the original impact evaluation. This can be made possible through proper documentation and management of data.
With careful coding, use of master do-files, and adherence to coding best practices the same data and code will yield the same results for any given person. Follow these guidelines when publishing the code:
- Master do-files. The master do-file should set the Stata seed and version to allow replicable sampling and randomization. By nature, the master do-file will run project do-files in a pre-specified order, which strengthens reproducibility. The master do-file can also be used to list assumptions of a study and list all data sets that are used in the study.
- Packages and settings. Install all necessary commands and packages in your master do-file itself. Specify all settings and sort observations frequently to minimize errors. DIME Analytics has created two packages to help researchers in producing reproducible research -
- Globals. Create globals (or global macros) for the root folder and all project folders. Globals should only be specified in the master do-file and can be used standardizing coefficients for the data set that will be used for analysis.
- Shell script. If you use different languages or software in the same project, consider using a shell script, which ensure that other users run the different languages or software in the correct order. This would mean that you have a script that you run in your command line that first execute a master script in one language and then a master script in another one or several other languages. This way the shell script becomes the "super master script" that execute several other master scripts in the correct order.
- Comments. Include comments (using
*) in your code frequently to explain what a line of code (or a group of commands) is doing, and why. For example, if the code drops observations or changes values, explain why this was necessary using comments. This ensures that the code is also easy to understand, and that research is transparent.
Code publication tools
There are several free software tools that allow the research team to publicly release the code, including GitHub and Jupyter Notebook. Users can pick any of these depending on how familiar they are with these tools. There are several pre-publication code review facilities as well.
- GitHub. It is a free version-control software. It is popular because users can store every version of every component of a project (like data and code) in repositories which can be accessed by everyone working in a project. With GitHub repositories, users can track changes to code in different programming languages, and create documentation explaining what changes were made and why. The research team can then simply share Git repositories with an external audience which allows others to read and replicate the code as well as the results of an impact evaluation.
- Jupyter Notebook. This is another platform where researchers can create and share code in different programming languages, including Python, R, Julia, and Scala.
- DIME Analytics has also created a sample peer code review form that researchers can refer to before publishing their code.
To learn more about how to use these tools, users can refer to the following resources:
The research output is not just a paper or report, but also includes the codes, data, and the documentation. Output publication is the final aspect of reproducible research after completing documentation and publication of data and codes. The research team can follow certain guidelines to ensure their research output is reproducible and transparent.
- Checklist. DIME Analytics has created a pre-publication reproducibility checklist for researchers.
- GitHub repos. GitHub repositories (or repos) allow researchers to track changes to the code, create messages explaining the changes, and make code publicly available for others to read and replicate.
- Dynamic documents. These are documents which allow researchers to write reports that can automatically display results after analysis. This reduces the amount of manual work, and there is also less room for error and manipulation of results.
There are a wide range of tools that are available for output publication. Each of them allows users to create dynamic documents and edit the reports using various programming languages like R, Stata, and Python.
- R. This language has a feature called R Markdown, which allows users to perform analysis using different programming languages, and print the results in the final document along with text to explain the results.
- Stata. New versions of Stata (version 15 onwards) allow users to create dynamic documents. The output is usually a PDF file, which contains text, tables and graphs. Whenever there are changes to raw data or in the analysis, the research team only needs to execute one do-file to create a new document. This improves reproducibility since users do not have to make changes manually every time.
- LaTeX. LaTeX is a widely used publication tool. It is a typesetting system that allows users to reference lines of code and outputs such as tables and graphs, and easily update them in a text document. Users can export the results into .tex format after analyzing the data in their preferred software – using stargazer in R, and packages like
outreg2 in Stata. Whenever there are new graphs and tables in the analysis, simply recompile the LaTeX document with the press of a button in order to include the new graphs and tables.
- Overleaf. Overleaf is a web-based platform that allows users to collaborate on LaTeX, and receive feedback from other researchers.
- Jupyter Notebook. Jupyter Notebook can create dynamic documents in various formats like HTML and LaTeX.
Click here for pages that link to this topic.
- Berkeley Initiative for Transparency in the Social Sciences (BITSS), Manual of Best Practices in Transparent Social Science Research
- Berkeley Initiative for Transparency in the Social Sciences (BITSS), Pre-Analysis Plan template
- Center for Open Science, Transparency and Openness Guidelines
- Coursera, Course on Reproducible Research in R
- Dataverse (Harvard), Randomized Control Trials in the Social Science Dataverse
- DIME Analytics (World Bank), Data Management and Cleaning
- DIME Analytics (World Bank), Coding for Reproducible Research
- DIME Analytics (World Bank), Intro to GitHub
- DIME Analytics (World Bank), Using GitHub
- DIME Analytics (World Bank), GitHub Flows
- DIME Analytics (World Bank), Management using GitHub Repositories
- DIME Analytics (World Bank), Initializing and Synchronizing a Git Repo with GitHub Desktop
- DIME Analytics (World Bank), Using Git Flow to Manage Code Projects with GitKraken
- DIME Analytics (World Bank), Stata Coding
- Data Colada, Tips for making research findable and reproducible
- Hua Peng (StataCorp), Incorporating Stata into Reproducible Documents
- Innovation for Poverty Action (IPA), Reproducible Research: Best Practices for Data and Code Management
- Innovation for Poverty Action (IPA), Guidelines for data publication
- J-PAL, Transparency and reproducibility
- Matthew Salganik (Princeton), Open and Reproducible Research: Goals, Obstacles and Solutions