Data-level documentation

Data-level, or object-level, documentation provides information at the level of variables in a database or individual objects such as interview transcripts or pictures. Data-level information can be embedded in data files, such as variable, value and code labels in an SPSS file or headers in a interview transcript. 

Quantitative data
Qualitative data

For qualitative textual data, background and contextual information and participant details of interviews, observations or diaries can be described at the beginning of a file as a header or summary page. Clear speech demarcation and the use of speaker tags are crucial in interview transcripts. Examples can be seen in our model transcription template.

Data list

For qualitative data collections, such as interview or image collections, an important piece of data documentation is the data list which accompanies the data collection in our catalogue. The list provides information for users that enables them to easily identify and locate relevant transcripts or items within a data collection. Each item in the list should have a unique identifier. The list provides key biographical characteristics and features of interviewees, and details for the interview, for example:

  • interview ID
  • age
  • gender
  • occupation, organisation
  • location
  • place of interview
  • date of interview
  • transcript file name
  • recording file name

The list should indicate where parts of the data are missing, such as partial or missing transcripts. Pseudonyms can be used to anonymise participants - see our guidelines on anonymising qualitative data. Identifiers used should be consistent so links can be made between interview transcripts, interview recordings, field notes, etc.

You can use our data listing template to develop a data list for your collection of qualitative research data, and consult an example data list.

Documenting data using NVivo software

We have developed best practice guidelines and recommendations to document and annotate qualitative data when using NVivo9 as a software package to organise, code and analyse your data.

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