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Research Data Management

Data Quality

Ensure the quality of the research data by its accuracy, completeness, consistency, reliability, timeliness, and relevance by implementing a quality assurance program before, during and after data collection.

DataOne describe quality assurance and quality control as

"Quality assurance and quality control are phrases used to describe activities that prevent errors from entering or staying in a data set. These activities ensure the quality of the data before it is collected, entered, or analyzed, as well as actively monitoring and maintaining the quality of data throughout the study. "


 

Quality assurance

Quality assurance in research involves implementing processes and protocols to ensure the accuracy, reliability, and integrity of research findings. It encompasses various practices aimed at minimizing errors, biases, and inconsistencies throughout the research process. This includes rigorous experimental design, meticulous data collection and analysis, thorough documentation of methodologies, transparent reporting of results, peer review, and adherence to ethical standards.

Quality assurance also involves ongoing monitoring and evaluation to identify and address any issues that may arise during the research process, ultimately enhancing the credibility and validity of research outcomes.

Quality assurance involves:

  • Making variables explicit to avoid ambiguity e.g., units of measurement, definitions, is a variable type numeric, text, or a category
  • Record component variables instead of compound variables e.g., BMI vs height, weight, age
  • Ensuring all data collectors understand the requirements e.g., co-designing the questionnaire or inducting collectors to the
  • Designing spreadsheets or questionnaires to that limit responses to categories or ranges

Quality control

Quality control in research involves the systematic monitoring and evaluation of research processes and outputs to ensure compliance with established standards and requirements. It encompasses activities aimed at detecting and correcting errors, inconsistencies, and deviations from predefined criteria throughout the research lifecycle. This includes verifying the accuracy of data collection methods, assessing the reliability of experimental procedures, scrutinizing the validity of analytical techniques, and validating the integrity of research findings.

Quality control also involves implementing preventive measures to minimize the occurrence of errors and improve the overall quality of research outputs. By maintaining high standards of quality control, researchers can enhance the trustworthiness and reproducibility of their work. (USGS.gov https://www.usgs.gov/data-management/quality-control-qc-detecting-and-repairing-data-issues-recommended-practices)

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