Usual practices of data management for quantitative research in a low- and middle-income country

Data Management (DM) activity is essential during the life-cycle of research as it pertains to the collection, validation, and reporting of data to support generating credible evidence.  DM is usually initiated by the development of questionnaires or case report forms (CRF) and preparing electronic data entry templates. When developing questionnaires to collect data, individuals must be responsible for ensuring that accurate data is captured from CRF.  

In Bangladesh, data are generally collected on printed CRFs, which are transferred into electronic entry templates to create a database. Data are checked following a list of criteria for its validation before electronic data entry. Upon review by data collection and management teams, the final versions of the CRF are authorized by the researchers and statisticians. The DM team is then responsible for building and testing the database. Proper documentation of this process is necessary to keep track of any changes made in the database. Once built and tested with the final CRF, it is ready for live data capture. 

When the data from the printed CRF is being entered, the DM team checks 10% of the CRFs randomly after a few entries. If the data is acceptable, the process continues. However, if any dissimilarity is found, then immediate troubleshooting of the problems is necessary.  Automatic and scheduled backup systems run during the process of data entry. For quality assurance, the DM team will initiate regular queries for any missing data, incomplete responses, skipped patterns, and outliers. After finishing the whole data entry process, the DM team ensures that the ‘cleanest’ data is provided for statistical analysis and archiving for future uses. To ensure the reusability of the data, a data dictionary (code book) is created, which is also referred to as data bank or data center.  

Several issues affect all stages of quality data management system. The research team identifies several cross-cutting issues such as purpose and value, privacy, data ownership, security, standards, and data quality. As interoperability and reusability of data are highly dependent on data management, it is important to emphasize having a well-trained data management team in every research institute.