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Exporting Analysis

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After analyzing data and before disseminating results, research teams must export analyses. This article discusses formats for exporting, instructs how to make replicable tables and graphs via Stata and LaTeX, outlines the four levels of replicability, and comments on version control.

Read First

  • For full replicability, ensure that all results (i.e. tables, graphs, etc.) are generated by code and exported in final form to the final report. For good replicability, minimal changes should be made.
  • Never manually copy and paste from a Stata or R window to a file saved as disc. This is sloppy and poor practice and never considered replicable.
  • Stata’s estout command package has a wide and useful range of capabilities for producing replicable summary statistic and regression tables.
  • When trying multiple analytical approaches, make sure to use version control via code and/or results.

Formatting

Formatting requirements depend on the audience. For example, best practices for communicating results to project beneficiaries or government counterparts are different than those for communicating results to the academic research community.

Policy Output

Fact sheets can effectively disseminate RCT results to government counterparts and local communities. While regression tables formatted according to journal standards will obviously not work well in this context, data visualizations could help to portray findings and takeaways.

Academic Output

For academic output, make sure to follow established guidelines, such as those in American Economic Review’s style guide and in ShareLaTeX's collection of style guides. Note that the best tool for excellent-looking and easily-reproducible tables is LaTeX, or its web-based collaboration tool, Overleaf. LaTeX has many features that allow you to produce tables that looks exactly like the tables published in top journals. For more details on how to use LaTeX, see DIME’s LaTeX Training, which has multiple stages that target the absolute beginner as well as the experienced user.

Exporting Tables

To create replicable, produce and complete all tables by code in Stata or R. For example, extra statistics such as test statistics and means should be added before exporting the table. If multiple tables should be combined to one table, then this should be done by code as well. estout, which is a package of commands that also includes esttab, eststo, estadd and estpost, provides the functionality to accomplish this. While the estout commands may be a bit difficult to get started with, they have a wide and useful range of capabilities. estadd, for example, allows users to add additional statistics to a table (i.e. the predicted Y, the mean of sub-samples, the N of sub-samples or any number that you have calculated).

To begin with estout, the easiest way might be to copy the code used to generate a table that you like. Recycling code is actually the most common way to use the estout commands even for users familiar with the package.

Levels of Replicability

We all know that all our work and outputs should be replicable, but exactly how replicable does something need to be? For example, if a report has a table outputted by code but formatted manually, is the report replicable? This section outlines the four levels of replicability: full replicability, good replicability, basic replicability and no replicability. Full replicability is the ideal and no replicability is never acceptable.

Full Replicability

In full replicability, all results are generated by code and exported in final form to the final report. No formatting or any other type of editing occurs between running the code and the tables appearing in the report.

While new software tools are emerging, LaTeX very effectively creates fully replicable outputs. DIME Analytics highly recommends LaTeX as it is more comprehensive and more supported by online resources than any new competing tools. To import tables and graphs to a report written in LaTeX, they must be exported in LaTeX or LaTeX-readable format. For graphs, this requires a .png format; in Stata, simply add the .png extension to the file name specified in save(). For tables, this requires a .tex format; in Stata, if exporting tables through the estout family, simply change the table format to .tex. For more options, see Additional Resources.

Good Replicability

In good replicability, all results are produced by code and saved to files on disk. No copying and pasting of results are needed between files. However, formatting and other minor changes may be needed, and/or the final tables may need to be copied and pasted into the final report. All DIME projects should aim to reach good replicability at least. While full replicability is objectively better, its model may not work for all teams due to, for example, external collaborators that are not able or willing to work in the tools required for full replicability.

To create graphs of good replicability, simply use thesave() option included in Stata's graph commands. However, if several graphs are supposed to be combined into one graph, it is not good replicability to combine them manually or to simply put them in the report next to each other. For good replicability, these graphs should be combined in the code. In Stata, graph combine can accomplish this.

Basic Replicability

In basic replicability, the code generates and outputs all graphs and tables to the project folder, though additional changes are made to the outputs. For example, the research team may copy or paste between files to create the tables, or apply some very basic math to the outputted files. This is not best practice, but it is the minimum acceptable level of replicability.

To create basically replicable graphs, simply use thesave() option included in Stata's graph commands. This even satisfies good replicability for many graphs. For tables, there is no single built-in option to reach basic replicability. Common commands to output results include outreg and estout, outlined above. To test that all tables and graphs are exported with basic replicability, move all tables and graph to a separate folder and run the code again. Make sure that all tables and graph files are re-created again in the folder and that it is possible to make the minor manual actions required to generate the final tables and graphs from these files.

No replicability

Any outputs (i.e. tables, graphs, etc.) that require manual copying and pasting from a Stata or R window to a file saved as disc are never considered replicable. This level of replicability is never acceptable for published outputs, regardless of the size or importance of the report. While no replicability may be acceptable during, for example, exploratory analysis, as soon as output is produced for someone else - even within the team - it should be done with a higher level of replicability. Since analysis should eventually be shown to someone, we strongly recommend that you aim for a higher level of replicability from the start since it will save you time later.

Version control

Very often, research teams try multiple approaches before the Principal Investigator decides which to use for the final analysis. This should be minimized as it could otherwise be regarded as p-hacking. Nonetheless, typically it is to some extent a necessary process. During this phase, ensure version control, or a way to go back to previous results in order to compare the different versions. Version control can occur via code, results or both. Version control via code and GitHub is the only way to achieve version control in the full extent of the term. Version control via results is not version control in the full extent of the definition of the term, but it satisfies the basic need discussed here. If using version control via results, make sure to date the files.

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This article is part of the topic Data Analysis

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