Stata Coding Practices

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Researchers use Stata in all stages of an impact evaluation (or study), such as sampling, randomizing, monitoring data quality, cleaning, and analysis. Good Stata coding practices, packages, and commands are a critical component of high quality reproducible research. These practices also allow the impact evaluation team (or research team) to save time and energy, and focus on other aspects of study design.

Read First

iefieldkit

DIME has developed the iefieldkit package for Stata to simplify the process of primary data collection. The package currently supports supports three major components of this workflow (process) - survey design, survey completion, and data cleaning and data harmonization. iefieldkit uses four commands to simplify each of these tasks:

  • Before data collection. The ietestform command tests the collected data to make sure it follows best practices in naming, coding, and labeling. For instance, it does not let an enumerator move to the next field until they enter a response, thus ensuring that incomplete forms can not be submitted.
  • During data collection. The ieduplicates and iecompdup commands allow the research team to detect (identify) and resolve (deal with) duplicate entries in the data set. These commands were previously a part of the ietoolkit package, but are now part of the iefieldkit package.
  • After data collection. The iecodebook command provides a method for rapidly cleaning, harmonizing, and documenting data sets.

To install the iefieldkit package, type ssc install iefieldkit in your Stata command window. Note that some features of this package might require meta data (information) that is specific to SurveyCTO, but users can still test them in other cases.

ietoolkit

DIME has developed the ietoolkit package for Stata to simplify the process of data management and analysis in impact evaluations. Given below are the list of commands that are currently part of this package.

  • Data management.
    • iefolder sets up a standardized (common) structure for all folders that are shared as part of a project, that is the project folder. It creates master do-files that link to all sub-folders (folders within another folder), so that the project folder is automatically updated every time more data or files are shared from the field teams. This command helps create reproducible research.
    • iegitaddmd allows members of the research team to share a template (outline) folder for a new project on GitHub even if it is empty. This command creates a placeholder that can be updated later when a file is added to that folder. For example, templates often include an output folder where the results of data analysis will be stored. This folder remains empty until the data set is cleaned to prepare it for analysis. Using this command, two people, say A and B, can still share this folder with each other on GitHub.
    • ieboilstart standardizes the version, capacity (in terms of the number of observations it can store in memory), and other Stata settings for all users in a project. This command should be run (typed) at the top of all do-files that are shared between members of the research team. Such a code is called a boilerplate code, since it standardizes the code at the beginning for all do-files.

An example of a code that uses these commands is given below:

ieboilstart, version(14.0) //Standardizes the version for everyone.
global folder "C:/Users/username/DropBox/ProjectABC" 
iefolder new project, projectfolder("$folder") //Sets up the main structure
iegitaddmd, folder ("$folder") //Makes sure users can share the main folder on GitHub even if it is empty
  • Data analysis.
    • iematch is a command which can be used for matching observations in one group to observations in another group which are the closest in terms of a particular characteristic.
      For example, consider a study which is designed to evaluate the impact of randomly providing cash transfers to half the workers in a firm. The research team can use iematch to match and compare wages of women in the treatment group (which received the cash transfers) with observations in a control group (which did not receive the cash transfers).
    • iebaltab runs balance tests, and produces balance tables which show the difference in means for one or more treatment groups. It can be used to check if there are statistically significant differences between the treatment and control groups. In case there are significant differences in the means, iebaltab even displays an error message that suggests that results from such data can be wrongly interpreted.
    • iedropone drops only a specific number of observations, and makes sure that no additional observations are dropped.
    • ieboilsave performs checks to ensure that best practices are followed before saving a data set.
    • ieddtab runs difference-in-difference regressions and displays the result in well-formatted tables.
    • iegraph produces graphs of results from regression models that researchers commonly use during impact evaluations.

To install the ietoolkit, type ssc install ietoolkit in your Stata command window.

File Paths

DIME Analytics suggests the following guidelines for specifying file paths in Stata:

  • Double quotes ("). Always enclose file paths in double quotes (") . For example, "$maindir".
  • Forward slashes (/). Always use forward slashes (/) to specify folder hierarchies, that is, the exact location of a folder inside another folder, and so on. For example, "C:/Users/username/Documents". This is important because Mac and Linux computers cannot read file paths with back slashes(\).
  • File extension. Always include the file extension in the file path, such as .dta, .do, or .csv. This helps to avoid ambiguity (or doubt) if another file with the same name exists.
  • Absolute. File paths must be absolute, that is, all file paths must begin from the root folder of the computer, for example, C:/ on a PC or /Users/ on a Mac. This ensures that users are always specifying the correct file and the correct folder. While relative (non-absolute) file paths are common in many other programming languages, Stata does not allow users to specify relative file paths.
  • Dynamic. A file path is said to non-dynamic if it uses cd to specify the path. File paths should however always be dynamic. This means that users must use globals (global macros) that are located in the master do-files to specify file paths. These file paths are called dynamic because users can change the file path as and when required, simply by changing only the name of the folder global in the master do-file. They also allow users to create more than one folder global using global macros, which is not the possible when working with non-dynamic file paths.
  • Never use cd since there can be cases where a user accidentally overwrites a file in the project folder which the cd initially referred to. It is always better to useabsolute and dynamic file paths, since there is no risk of files getting saved in the wrong project folder (as long as the global macro has a unique name).

Examples

  • Dynamic and absolute file path.
  global myDocs "C:/Users/username/Documents" 
  global myProject "${myDocs}/MyProject"
  use "${myProject}/MyDataset.dta"
  • Non-absolute, non-dynamic file path.
  cd "C:/Users/username/Documents/MyProject"
  use MyDataset.dta
  • Absolute, but non-dynamic file path.
  cd "C:/Users/username/Documents/MyProject" 
  use "C:/Users/username/Documents/MyProject/MyDataset.dta"

Exporting Tables

Tables play a crucial role in representing the results of a study in an easy-to-understand format. However, it is common to copy-and-paste results from Stata, and format them in a word-processing software, which affects the reproducibility of research. DIME Analytics has therefore created the following resources for exporting tables in Stata:

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