DataWork Survey Round

The DataWork Survey Round folder is a key component of the DataWork folder, DIME's standardized folder template for organizing data work in a project folder. The DataWork Survey Round folder is useful throughout all stages of a project.

Read First

• The Stata command iefolder creates the DataWork Survey Round folder as a component of the DataWork folder, which is typically housed on Box or Dropbox.
• The DataWork Survey Round folder organizes all data-related elements of the project: questionnaires, datasets, do-files, documentation of analysis, graphs, tables, survey logs, etc.
• The DataWork Survey Round folder facilitates efficiency and saves files in a manner understandable both to the current research team and to people visiting the project folder years after the project has ended.
• The DataWork Survey Round folder consists of five sub-folders: the DataSets folder, the Dofiles folder, the Outputs folder, the Documentation folder and the Questionnaire folder.

Overview

The Survey Round folder contains the following sub-folders: the DataSets folder, the Dofiles folder, the Outputs folder, the Documentation folder and the Questionnaire folder. The remainder of this page explains each element within the folder.

DataSets Folder

Image 1. Example of a DataSets folder. (Click to enlarge.)

The DataSets folder contains three sub-folders in which datasets are stored: Deidentified, Intermediate and Final (Image 1).

It is not recommended to create a folder for raw datasets in the DataSets folder: raw data almost always includes identifying information and the DataSets folder is not in the encrypted branch of the DataWork folder. Instead, save raw data in the DataWork Survey Encrypted Data folder. The Raw Data section describes in more detail best practices for raw data.

De-identified Folder

The De-identified folder houses de-identified raw data. Working with the raw, de-identified data rather than raw, encrypted data makes the work flow easier and smoother for the entire research team.

Intermediate Folder

The Intermediate folder is a work-in-progress folder containing all datasets that belong neither in the Raw nor Final folder. Raw datasets on which simple changes have been made belong in the Intermediate folder. There are no specific rules how this folder should be organized. However, it is a good idea to keep it organized in sub-folders and to name files according to naming conventions.

Final Folder

The Final folder contains the clean datasets with final variables constructed. This folder should only contain one version of each dataset. If your project has various datasets for different units of observation, (i.e. student dataset, school dataset, teacher dataset), then the Final folder should contain a sub-folder for each dataset.

The Final folder is the folder most likely to be visited several years after the project has ended by someone with very little knowledge of the project. This folder should therefore be one of the folders organized to the most level of detail.

Dofiles Folder

Image 2. Example of a DataSets folder. (Click to enlarge.)

Each Survey Round folder contains a Dofiles folder. The top level of this folder should contain only a master do-file and sub-folders. The master do-file maps out all do-files and datasets relevant to this survey round.

The sub-folders in the Dofiles folder should be organized according to task (i.e. import, cleaning, analysis), with the relevant do-files saved within its respective task folder. Each sub-folder should have a task level master do-file. Make sure to use naming conventions and version control when creating and updating these files and folders.

Task-level master do-files have little technical purpose as the round master do-file can technically call each individual do-file. However, the task-level master do-files document data work and make it significantly easier for someone unfamiliar with the code to understand the process.

Outputs Folder

The Outputs folder houses the survey round outputs. It consists of sub-folders. The exact sub-folders depend on the output and the method used to create outputs. If, for example, both tables and graphs are outputted, then create Table and Graph sub-folders. Each sub-folder should contain Raw and Formatted folders. For example, if single graphs are outputted to disk and later combined to one cumulative graph file, save the former as Raw and the latter as Formatted. See best practices of exporting analysis for additional recommendations.

The Outputs folder is one of the few places where it may be good practice to save multiple versions of the same file, since it is common to compare different versions of the analysis. If you save multiple versions of the same file, do not use the naming convention of table_v1, table_v2, etc. Instead, name files by date (i.e. table_Apr30, table_Jun6). If the outputted file size is significant in terms of disk space, do not save multiple versions of the same file: this will make it difficult for people with slow internet connection to access the folder via syncing services like DropBox.

Documentation Folder

This Documentation folder contains documentation for the analysis including any duplicate reports, survey logs, etc. While there is no strict format for this folder, it is good practice to save any documentation for this survey round in this folder. This could include anything from formal documentation to email conversations. You never know what you and your project team will need to know about this round in the future.

Questionnaire Folder

The Questionnaire folder contains all documentation related to the data collection. While questionnaire is the most important document to include in this folder, it is also important to save any other material that documents the survey. This includes material from the enumerator training, survey manuals used in the field, the survey firm TOR, and anything else that provides the project team with a better qualitative understanding of the data.

Encrypted Round Folder

Image 3. Example of a DataSets folder. (Click to enlarge.)

The Encrypted Round folders are created by iefolder at the same time as a round folder is created, but they are created in the encrypted branch of DataWork instead of inside the round folder. The encrypted round folders are separated from the other round folders as they likely include identifying data.

Raw Data

Any data downloaded from the server used to collect data should be saved in the Raw Identified Data folder. This includes data downloaded from the internet, data received from data collection, and data received from other projects.

The data in this folder should be exactly as you got it: absolutely no changes should be made to it. Even simple changes like correcting known errors, changing variable names, changing format, or changing file names should never be done to any files in this folder. The only exception to this rule is if the file name needs to be changed in order to be imported.

If there are known errors in your raw datasets, import the raw data set, write a do-file that corrects the errors and save the corrected data in the Intermediate folder. The datset in the Raw folder with known errors should remain unaltered. If corrections to the datasets are not properly documented, then the research team will not fully understand the quality of neither the data nor the research.

Import Dofiles

The do-files used to import, edit and de-identify the raw data likely include identifying information. Therefore, they should be housed in the Encrypted folder as well.

Running these do-files files should import the data, address any immediate issues such as missing IDs or duplicates, de-identify it and save the data set in the Intermediate folder, which is nested in the DataSets folder in the unencrypted branch of the DataWork.

Back to Parent

This article is part of the topic DataWork Folder.