Data processing and analysis

Data processing and analysis is one of the most important stages of research work, as it is at this stage that the insights and conclusions that shape the results of the research are obtained.

To ensure the reliability and reproducibility of results, researchers should pay attention to the implementation of good data management principles when processing and analysing data. This section of the guide summarises various principles and good practices that will help data processing and analysis to be carried out in a reproducible way.

Researchers must ensure that their approach to data processing and analysis is structured, understandable and in line with established laws, regulations and ethical principles. This includes documenting data processing and analysis so that everyone can clearly understand and trace the actions taken with the data from the moment it was collected to the generation and interpretation of the results.

Particular attention should be paid to the handling and management of sensitive data, as the security of sensitive data is not only a legal issue but also an ethical obligation that builds trust between researchers and the research participants from whom the sensitive data were collected. Researchers must handle such data in accordance with the requirements of the General Data Protection Regulation (GDPR).

Topics in data processing and analysis:

• Documenting data processing and analysis
• Quantitative data processing and analysis
• Qualitative data processing and analysis
• Processing of sensitive data
• Handling sensitive data in the data lifecycle
• Pseudonymisation
  • Key tables
  • Hash functions
  • Encryption
• Anonymisation
  • Data deletion
  • Generalisation of data
  • Microaggregation
  • Data mixing
  • Adding noise
  • Anonymisation tools and their use