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Research Data Management

Quality Control

Quality control is a fundamental step in research, which ensures the integrity of the data and could affect its use and reuse and is required in order to identify potential problems.

It is therefore essential to outline how data collection will be controlled at various stages (data collection,digitisation or data entry, checking and analysis). Consider the following quality control measures:

Data collection:

  • Outline the number of measurements/samples/procedures repeated
  • Outline instrument calibration tests & data set or samples used for calibration
  • Outline standardized controls (e.g. sample controls)
  • Use of standardized protocols and methods with clear instructions and documentation

Data entry:

  • Decide a method for documentation i.e. Electronic lab notebooks vs paper
  • Outline the non-digital data structure and strategy for digitization
  • Collect and create metadata throughout the data collection and handling process
  • Use controlled vocabularies
  • Outline how the data/samples/variables are labelled
  • Document terminology used
  • Describe how to flag/tag questionable data
  • Ensure data and time is represented in a machine readable format and valid
  • Set up validation rules or input masks in data entry software
  • Data Analysis and checking:
  • Outline software/code used for analysis
  • Outline strategy for data transfer and controls (e.g. checksum)
  • Outline how the data will be cross-checked and validated
  • Assign person/expert for quality assurance and data checks and/or peer review
  • Outline database structure to organise data and data files
  • Document any modifications and outline versioning strategy to avoid duplicate error checking
  • Check and flag questionable data
  • Verify your analysis by using a random data set/samples compare to original data
  • Double check the code for any errors and ensure appropriate documentation
  • Use statistical analysis to detect erroneous and/or anomalous values

Qualitative data:

For qualitative data such as interviews:

  • Outline guided interview questions
  • Make use of software tools such as text to speech
  • Control the quality of audio/video/transcripts files
  • Refer to the UK data archive guidelines.

Examples: