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

The basic syntax for ieduplicates is as follows:

ieduplicates id_varname using "filename.xlsx"
    , uniquevars(varlist) 

As inputs, ieduplicates requires the following :

• id_varname: This is the name of the single, unique ID variable. This variable must be such that it would be an unacceptable duplicate in the dataset, and so must never be repeated. If there are two or more variables that identify the observation in the dataset, you should create a single ID variable that is unique for the dataset. This variable could be either a string or a number. For example, household_id.
• "filename.xlsx": This provides the name of the Excel file in which ieduplicates will display the duplicates correction template. The file name in this case is specified with the help of using, and must include an absolute file path. For example, "C:/myIE/Documentation/DupReport.xlsx". Since the output is an Excel sheet, even those members of the research team who do not know Stata can read the report, and make corrections.
• uniquevars( ): Finally, ieduplicates uses one or multiple variables specified within ( ) to uniquely identify each observation in the dataset. However, most data collection tools only use one variable for this purpose. For example, SurveyCTO creates this variable automatically, and names it "KEY".

Functionality

When ieduplicates runs in a data set, it identifies all observations with duplicated values with regard to the variable specified as id varname. If there are no duplicates, the command will display a message saying the data set is uniquely and fully identified by this variable. In this case, no output will be saved, and data will be left unchanged. If there are duplicates, the command exports an Excel sheet, saved to the file specified after using, with information on these observations, and stops your code with a message listing the values in the ID variable that are repeated.

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.

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