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Ieduplicates

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ieduplicates is a command in the iefieldkit package that identifies duplicate values in ID variables. ID variables are variables that uniquely identify every observation in a dataset, for example, household_id. It then exports the duplicates to an Excel file that the research team can use to investigate and resolve the duplicated ID values. 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 data set is uniquely and fully identified. The commands combine four key tasks to resolve duplicate values:

  1. Identifying and reporting on observations with duplicate ID values.
  2. Comparing observations with duplicated ID values by listing variables where they have different values.
  3. Documenting and applying solutions to ID variable duplicates.
  4. Returning the data without unresolved duplicates until they are solved

In many data activities, especially during data collection, the research team needs to be able to uniquely identify the data set using an ID variable, for example in back checks. Suppose household_id is the unique ID variable and household_id==123456 have been selected for back checks but two observations have the value 123456 in household_id. In this case, it is important to resolve these duplicate observations before performing the back check as the result of the back check on a misidentified observation might cause too much confusion if shared with the field team. It is better to first solve the duplicate, and that is why ieduplicates return the data set without duplicates until they are solved.

When you run ieduplicates for the first time, it will create a duplicates 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 listing the two observations with household_id==123456, ieduplicates will display a message pointing out that household_id does not uniquely and fully identify the data. The default behavior is that it will also stop your code, and require you to fill the correction template before you can move on (this default behavior can be change using the option force).

If there are no observations which have duplicate values of household_id, for instance, ieduplicates will display a message saying the data set is uniquely and fully identified on the basis of household_id. In such a case, there will be no output, and this command will leave the data unchanged.

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 a single uniquely and fully identifying ID variable. This variable is expected to be an ID variable where no duplicates should be allowed. If there are two or more variables that identify the observations in the dataset, then you should create a single ID variable where each value is expected to be unique for the dataset. This variable could be either a string or a number.
  • "filename.xlsx": This provides the name of the Excel file generated by ieduplicates listing the duplicates. 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" is the absolute file path for the file called "DupReport.xlsx" in the folder "C:/myIE/Documentation". 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 to uniquely identify each observation in the dataset. The command must have a way to uniquely identify the observations when applying the solution to the duplicates, and the duplicated ID variable can per definition not be used for that. Most data collection tools generates a variable suitable for this. For example, SurveyCTO creates this variable automatically, and names it "KEY".

Implementation

The following is the process for using ieduplicates:

  1. Run ieduplicates on the raw data. If there are no duplicate observations, then you are done. Skip the rest of the steps. If there are duplicates, the command will output an Excel file which contains the duplicates correction template. It will display a message with a link to this file, and stop the code from moving forward. It will also show a message listing the duplicate ID values.
  2. Open the duplicates correction template. This template will list each duplicate entry of the ID variable, and information about each observation. It also contains 5 blank columns - correct, drop, newid, initials, and notes. Use these columns to make corrections, and include comments to document the corrections.
  3. Use iecompdup for more information. Sometimes the template is not enough to solve a particular issue. In such cases, run the iecompdup command on the same dataset.
  4. Overwrite the previous file. After entering all the corrections to the template, save the Excel file in the same location with the same name.
  5. Run ieduplicates again. This will apply the corrections you made in the previous steps. Now if you use the force option, it will only remove duplicates not yet resolved.
  6. Do not overwrite the orginal raw data. Save the resulting dataset under a different name.
  7. Repeat these steps with each new round of data.

Sample template

A sample duplicates correction template created by ieduplicates is displayed below.

Figure 1: Duplicates Correction Template

Duplicates Correction Template

The ieduplicates exports the duplicates correction template to the Excel file, based on the following 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)]
  • force: Removes all observations that contain duplicate values of id_varname from the data. As a result, it keeps only uniquely and fully identified observations. The force option is required so that you know that ieduplicates is making changes to your dataset. Do not overwrite the original raw data with the one that this command generates, otherwise you will lose the original data. Save this new dataset with a different name.
  • duplistid: Uniquely identifies each of the duplicate observations in the duplicates correction template. It does this by assigning 1 to the first instance of a duplicate value, 2 to the second instance, and so on.
  • datelisted: Indicates the date on which the observation was first included in the template.
  • listofdiffs: Lists the variables in the data set that are different for the observations that have duplicate values of the ID variable. You can also rename these columns by specifying the new column name under their respective options.
  • correct, drop, newID, initials, and notes: Creates 5 blank columns which you can fill to make corrections where needed, and complete the template.

This completed template then acts like a permanent documentation of how the research team resolved duplicate ID variables in the raw data. There are three options for resolving duplicate observations. They appear in the form of correct, drop, and newID columns in the template. Consider the following examples to understand how to fill these columns:

  • drop: If you want to keep one of the duplicate observations and drop another, then write “correct” in the correct column for the observation you want to keep, and “drop” in the drop column for the observation you want to drop. Make sure you mention the correct value under the key column for the observations you want to keep, and the ones that you want to drop from the dataset. This is important because SurveyCTO creates a unique key for every observation, even if two or more observations have duplicate variable IDs by mistake.
  • newID: If you want to keep one of the duplicates and assign a new unique ID to another one, write “correct” in the correct column for the observation you want to keep, and the new corrected ID value in the newID column for the observation to which you want to assign the new unique ID.
  • Combine both methods: You can also combine these two methods if you have more than 2 duplicate observations. Note that you must always indicate which observation you want to keep for each group of duplicate observations.

After entering your corrections, save the file and run ieduplicates again to apply the corrections to the dataset. Since ieduplicates should be used frequently as new data comes in from the field, the command also manages a subfolder called /Daily/ in the same folder which contains the main Excel file. ieduplicates uses this subfolder to save a backup version (along with the date) for every time the template is updated. If you do not wish to use this feature, use the nodaily option which prevents the creation of backups.

Note: If two different templates are generated on the same day, it saves the second with an additional time stamp on the name. This is especially useful in case the main corrections template, or any of its contents get deleted. You can restore a backup version by simply copying it out of the /Daily/ folder and remove the date from the name.

Related Pages

Click here for pages that link to this topic.
This page is part of the topic iefieldkit. Also see iecompdup.

Additional Resources

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