Ieduplicates

ieduplicates is the second command in the Stata package created by DIME Analytics, iefieldkit. ieduplicates identifies duplicates in ID variables that uniquely identify every observation in a dataset. It then exports them to an Excel file that the research team can use to resolve these duplicates. The research team should run ieduplicates with each new batch of incoming data to ensure high quality data before cleaning and analysis.

Read First

• Stata coding practices.
• iefieldkit.
• ieduplicates identifies duplicates in ID variables, and then iecompdup resolves these issues.
• To install ieduplicates, type ssc install ieduplicates in Stata.
• To install all the commands in the iefieldkit package, type ssc install iefieldkit in Stata.
• For instructions and available options, type help ieduplicates.

Overview

The ieduplicates and iecompdup commands are meant to help research teams deal with duplicate observations in primary data. These commands are designed to identify and resolve duplicate instances of an ID variable in raw survey data, and ensure that each observation is uniquely and fully identified. The commands combine four key tasks to resolve duplicate values:

1. Identifying duplicate entries.
2. Comparing observations with the same ID value.
3. Tracking and documenting changes to the ID variable.
4. Applying the necessary corrections to the data.

In any data, certain key variables of an observation should be unique by construction, to allow researchers to identify them during further analysis. For example, suppose you select household_id as the unique ID variable. Now suppose you pick the observation with household_id= 123456 for back checks, but the dataset has two observations with household_id = 123456. In this case, it is important to resolve these duplicate observations before performing the back check.

When you run ieduplicates for the first time, it will create a duplicate correction template. This template will list all observations that contain duplicate values of an ID variable that should be unique. In the example above, after creating this template, ieduplicates will, by default, display a message pointing out that household_id does not uniquely and fully identify the data. It will also stop your code, and require you to fill the correction template before you can move on.

Syntax

ieduplicates id_varname using "filename.xlsx" 
    , uniquevars(varlist)
    [force keepvars(varlist) tostringok droprest nodaily 
    duplistid(string) datelisted(string) datefixed(string) correct(string)
    drop(string) newid(string) initials(string) notes(string) listofdiffs(string)]

Implementation

1. Run ieduplicates on the raw data. If there are no duplicates, then you are done and can skip the rest of this list.
2. If there are duplicates, use iecompdup on any duplicates identified.
3. Enter the corrections identified with iecompdup to the duplicates in the report outputted by ieduplicates.
4. After entering the corrections, save the report in the same location with the same name.
5. Run ieduplicates again. The corrections you have entered is now applied and only duplicates that are still not resolved are removed this time.

Repeat these steps with each new round of data: DIME Analytics recommends repeating these steps each day that a research team has new data. In doing so, make sure to not overwrite the original raw data with the dataset from which ieduplicates has removed duplicates, as this would result in lost data. Instead, save the dataset with removed duplicates under a different name.

Specifications

ieduplicates requires that you specify the ID variable, a file path to the file where the report will be saved, and a unique variable. See the below example for reference:

ieduplicates HHID using ''C:\myIE\Documentation\DupReport.xlsx'',  uniquevars(KEY)

idvar

ieduplicates only allows a single ID variable. In the above example, this is HHID. If you currently have two or more variables that identify the observation in the dataset, DIME Analytics suggests creating a single ID variable. This variable could be either string or numeric.

using

ieduplicates stores the report in the file specified after using. In the above example, this is "C:\myIE\Documentation\DupReport.xlsx". The report is outputted as an Excel sheet so that even team members who do not know Stata can read and correct it. The command also creates a folder called Daily in the same folder as the Excel file. In the Daily folder, ieduplicates saves a back-up report each day in case someone accidentally deletes the main report or any of its contents. To restore a report, simply copy it out of the Daily folder and remove the date from the name. If two different reports are generated the same day, with different outputs, the second report will include a timestamp in the name.

uniquevars

ieduplicates uses the unique variable specified within uniquevars() to apply corrections and assign the correct variable to the correct observation. In the above example, this is KEY. While the unique identifier can consist of multiple variables, most data collection tools assign a unique ID to each observation on their server. In SurveyCTO survey data, for example, this variable is called KEY.

Using the Report

The outputted report provides an excellent format in which research teams can resolve duplicate problems. The report has a correct, drop and newID column. If you want to keep one duplicate and drop another one because they are double recordings of the same observation, then write yes in the correct column for the observation you want to keep, and yes in the drop column for the one you want to drop. If you want to keep one duplicate and assign a new ID to another duplicate, then write yes in the correct column for the observation you want to keep, and a new ID value in the newID column for the observation to which you want to assign a new ID. You can also combine these two methods if you have many duplicates with the same ID.

Always indicate which observation to keep. After entering your corrections, save the file and run ieduplicates again.

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This article is part of the topic ietoolkit

Additional Resources

• DIME Analytics’ Real Time Data Quality Checks