3. Process

3. Process

In this chapter, we focus on the data operations needed to prepare your data files for analysis and data sharing. Throughout the different phases of your project, your data files will be edited numerous times. During this process, it is crucial to maintain the authenticity of research information contained in the data and prevent it from loss or deterioration.

However, we will start with the topics of data entry and coding as the first steps of your work with your data files. Finally, you will learn about the importance of a comprehensive approach to data quality.

If you wish to print out this chapter, you can download it here.

Main take-aways

After completing your journey through this chapter you should:

  • Be familiar with strategies to minimise errors during the processes of data entry and data coding;
  • Understand why the choice of file format should be planned carefully;
  • Be able to manage the integrity and authenticity of your data during the research process;
  • Understand the importance of a systematic approach to data quality;
  • Be able to answer the DMP questions which are listed at the end of this chapter and adapt them to your own DMP.

The content of this chapter was inspired by research data management manuals, guidelines, online courses and methodological texts published by several data organisations and experts, in particular the information provided by the UK Data Service (2017a), the “Guide to Social Science Data Preparation and Archiving” by the US-based data organisation ICPSR (2012), the online course Research Data MANTRA (Research Data Service, University of Edinburgh, 2022), A guide into research data management by Corti, Van den Eynden, Bishop and Woollard (2014), Krejčí's "Introduction to the Management of Social Survey Data" (Krejčí, 2014), Gibbs (2007) and Data Management Guidelines produced and published by the Finnish Social Science Data Archive (Finnish Social Science Data Archive, 2017).

Main authors of this chapter

Jindrich Krejčí, Czech Social Science Data Archive (CSDA)
Johana Chylikova, Czech Social Science Data Archive (CSDA)