Ieduplicates

ieduplicates is a Stata command that identifies duplicates in ID variables and exports them to an Excel file that research teams can use to correct the duplicates. Research teams should run ieduplicate with each new batch of incoming data to ensure high quality data before cleaning and analysis. This page describes how to use the command, how to implement it with iecompdup, and how to effectively use the outputted report.

Read First

• While ieduplicates identifies duplicates in ID variables, iecompdup resolves duplicate issues.
• For detailed instructions on how to implement the command and its options in Stata, type help ieduplicates in Stata.
• This command is part of the package ietoolkit. To install all commands in this package, including ieduplicates, type ssc install ietoolkit in Stata.

Overview

ieduplicates is a Stata command that identifies duplicates in ID variables and exports them to an Excel file that research teams can use to correct the duplicates. The command should be run directly after importing raw data from, for example, a server used in survey data collection. ieduplicate outputs a report of all duplicates and removes the duplicates from the dataset until they are resolved. It does so to ensure that other quality checks requiring unique IDs do not use erroneous data. For example, if household_id=123456 was selected for back checks, but the dataset has two observations with household_id=123456, then it is best to resolve that duplicate before running the backcheck test on either observation.

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 folder to which to output the report, and a unique variable. See the below example for reference:

ieduplicates HHID, folder(C:\myImpactEvaluation\duplicateReports) 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.

folder

ieduplicates stores the report in the folder specified within folder(). In the above example, this is C:\myImpactEvaluation\duplicateReports. 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 which it 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, then the first one will be overwritten by the second.

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.

Back to Parent

This article is part of the topic ietoolkit

Additional Resources

• DIME Analytics’ Real Time Data Quality Checks