Data Management
 


Data Management

Data Management is the base of any research project which comprises all the disciplines related to managing data as a valuable resource and ensures the data quality on each level of the research project from collection to analysis. Good quality data will produce good quality research and help decision makers to implement the research conclusions.

The Data Management section of Hypertension project is responsible for proper collection, entering, cleaning and processing of information gathered during the project.

Major Tasks

The following major tasks are performed by the Data Management section:

  • Participate in developing data collection tools;
  • Train the data collection team how to collect the information and use of tools;
  • after data collection from the field on daily bases, train and supervise the data editing team at office level;
  • Control the flow of data collection tools from field to storage;
  • Create data entry programmes by using appropriate software according to the data collection tools;
  • Keep confidentiality and security of data movement;
  • Hire contract based professional data entry operators;
  • Train data entry operators on developed data entry programme;
  • Supervise double data entry done by different data entry operators;
  • Develop the data validation tools;
  • Validate the double data entry and generate the errors list;
  • Supervise the field team for correction of data entry errors;
  • Merge back the corrected data entry errors in the database;
  • After merge back, 10 per cent forms or 8000 fields are randomly selected for calculating data entry error rate which should be < 0.3 per cent or < 3/1000 field errors;
  • Logical Cleaning (Internal Validity Checking): to discover inconsistencies, and other anomalies in the data and performing data cleansing activities to improve the data quality;
  • To do data manipulation according to required analysis and statistical software;
  • Provide assistance in data analysis to data analysis team.










Data Collection and Editing Process Data Entry and Cleaning Process
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