Data cleaning is a crucial step in the assessment process, ensuring that collected data is accurate, consistent, and ready for analysis. Effective data cleaning helps to identify and correct errors, remove inconsistencies, and protect respondent confidentiality, ultimately supporting robust and reliable results.
Purpose of Data Cleaning
Detect and correct errors or inconsistencies in the dataset
Address missing or outlier values
Standardize data formats and coding
Prepare the dataset for analysis and reporting
Ensure respondent anonymity and data protection
Key Steps in Data Cleaning
Conduct initial checks for completeness and logical consistency
Apply validation rules and cross-checks based on the questionnaire and sampling design
Address missing data and outliers according to established protocols
Document all cleaning steps and decisions for transparency and reproducibility
If the dataset is to be shared externally, remove PIIs and create anonymous IDs to protect respondent confidentiality
Best Practices
Follow the Data Quality Guidance for detailed recommendations and checklists throughout the data cleaning process
Use automated scripts to standardize and streamline cleaning of expenditure data
Involve technical experts and supervisors in reviewing and validating cleaned datasets
Resources
The Data Quality Guidance is the key resource for cleaning WFP food security data
The CFSVA chapter on data cleaning & management provides guidance on managing and cleaning data in large-scale food security assessments.
3 Steps to High Quality Data gives rapid practical steps for ensuring data quality from collection through cleaning.
For more information, please contact the Assessments and Targeting Unit in HQ VAM at global.assessmentandtargeting@wfp.org.