What are the most common data quality issues in program evaluation and how can you address them?

1 Data collection errors

Data collection errors can occur when data sources, methods, or instruments are inappropriate, inaccurate, or inconsistent. For instance, unreliable or outdated data sources may be used, a biased or unrepresentative sample may be selected, a poorly designed survey or interview may be applied, or the data may be recorded or transcribed incorrectly. To avoid or reduce data collection errors, it is important to set clear data needs and sources that align with evaluation questions and indicators. Additionally, a suitable data collection method should be chosen that matches the data type, population, and context. Furthermore, data collection instruments should be designed and tested to guarantee they are valid, reliable, and user-friendly. Likewise, data collectors should be trained and supervised to ensure they follow the same protocols and standards. Finally, the data should be checked and verified for completeness, accuracy, and consistency.

2 Data entry errors

Data entry errors can occur when transferring data from the original source to a database or spreadsheet, such as typos, omissions, duplications, or misalignments. To prevent or minimize these issues, you should use a standardized and structured data entry format that aligns with your data collection instruments and indicators. Automating the data entry process with tools like optical character recognition (OCR), barcode scanners, or mobile applications can also be beneficial. Additionally, quality control measures such as validation rules, error checks, and double entry should be implemented and the data should be reviewed and corrected for any discrepancies or anomalies.

3 Data integration errors

Data integration errors occur when data from different sources, formats, or systems are combined or merged. For instance, you may use data from surveys, databases, or partners, but encounter incompatible formats, inconsistent definitions, or conflicting values. To reduce the chances of data integration errors, it is important to establish a common data model and framework that defines the data elements, formats, and standards. Additionally, a consistent and transparent data integration method should be employed to maintain the integrity and quality of the data. Any data conflicts or discrepancies can be resolved through rules, algorithms, or manual interventions. Lastly, it is essential to document and communicate the data integration process and results.