{HighFrequencyChecks} can be used to detect programming errors, surveyor errors, data fabrication, poorly understood questions, and other issues happening during Household Survey data collection.
The results of these checks can also be useful in improving the survey, identifying enumerator effects, and assessing the reliability of your outcome measures. It allows teams to catch survey issues and collection mistakes in time to correct them and ensure high-quality data. Such checks are based are among best practices from both the World Bank.
This package brings a series of convenience functions to monitor data quality during the data collection when running a survey with KoboToolbox (or any xlsform compatible platform). it is an adaptation in R of the Stata package from Innovations for Poverty Action.
Those can be performed periodically during the data collection process to check for possible errors and provide meaningful inputs to the enumerators. All these functions do not have to be ran at the same period of time. They are packaged together within a configurable report designed to help data supervisor to perform their duties.
The packages includes a series of controls calling for:
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Corrective actions:
- Correct set-up of data collection devices and encoding of the forms
- Data collected according the sampling plan
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Pro-active actions:
- Ensuring enumerators rigorous work standards
- Promoting enumerators productivity
Please check the tutorial here
This package is available as a shinyApp here: https://rstudio.unhcr.org/HighFrequencyChecks/
Install from github with
install.packages("pak")
pak::pkg_install("edouard-legoupil/HighFrequencyChecks")
Contributions to the packages are welcome. Please, follow the code of conduct.
If you encounter a bug or have idea for a new feature or check, please fill a ticket on github.
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Innovations for Poverty Action: https://www.povertyactionlab.org/resource/data-quality-checks
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Data Cleaning: https://www.acaps.org/fileadmin/user_upload/acaps_technical_brief_data_cleaning_april_2016_0.pdf
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JIPS Essential Toolkit, "How do we process and prepare data for analysis": https://jet.jips.org/wp-content/uploads/Guidance-Data-Processing-Phase5-JET.pdf
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REACH Data Cleaning Minimum Standards Checklist - https://www.reachresourcecentre.info/wp-content/uploads/2020/03/IMPACT_Memo_Data-Cleaning-Min-Standards-Checklist_28012020-1.pdf & Guidelines: https://www.reachresourcecentre.info/wp-content/uploads/2022/05/IMPACT_Data-Cleaning-Guidelines_FINAL_To-share-11.pdf