Difference between revisions of "Duplicates and Survey Logs"

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<code>  
 
<code>  
ssc install ietoolkit </br>
+
ssc install ietoolkit       </br>
 
ieduplicates ''ID_varname'' </br>
 
ieduplicates ''ID_varname'' </br>
 
</code>
 
</code>
 +
 +
This identifies the duplicates in the ID variable and exports them to an Excel file which is also used to correct duplicates in Stata. Field supervisors without knowledge of Stata can make the corrections in the Excel file and the duplicates will be corrected the next time you run the code.
  
 
==Comparing Server Data to Field Logs ==
 
==Comparing Server Data to Field Logs ==

Revision as of 15:17, 26 January 2017

Read First

  • To complete that the survey data is complete, both these steps must be done. Skipping one might result in an incomplete data set.

Data Duplicates

Before analyzing the outcomes of quality checks or sometimes even before running real time quality checks, we need to check for duplicates in the data. Duplicates are common in ODK/SurveyCTO and need to be removed before starting other data quality checks.

  • The data should be downloaded daily and checked for duplicates daily.
  • This cannot be done later because it is much easier to solve the problem when the field team remembers the interview. Other quality checks depend on uniquely identifying ID variables.


To remove duplicates, you can use the DIME's Stata command ieduplicates which can be found in the ietoolkit Stata package.

ssc install ietoolkit
ieduplicates ID_varname

This identifies the duplicates in the ID variable and exports them to an Excel file which is also used to correct duplicates in Stata. Field supervisors without knowledge of Stata can make the corrections in the Excel file and the duplicates will be corrected the next time you run the code.

Comparing Server Data to Field Logs

Comparing server data to field logs makes sure that all the data collected during the survey has made it to your server. This can be done in a few steps:

  • Gener

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This article is part of the topic Monitoring Data Quality

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