Kvalitative data processing and analysis

Data preparation – transcription (if necessary): often, if the data is collected in audio or video format, it is recommended to transcribe it into text format for analysis. Transcription can be carried out by the researcher or by an external service provider. Transcription is increasingly being done automatically by various tools, but these tools are not always able to transcribe in English without errors. Consequently, researchers have to revise and correct the automatically transcribed texts at a later stage.
The transcription process should preferably be documented in a data management plan. It is recommended to indicate whether the transcription is verbatim or edited (i.e. whether any specific language deficiencies have been corrected).
Particular care should be taken when transcribing recordings containing sensitive information. In this case, it is advisable to avoid uploading audio or video files to online tools that do not have a clear data management policy. If transcription is outsourced, it is advisable to have a data processing agreement in place that addresses confidentiality and privacy issues.
In the transcription process, the personal details of participants (and other people mentioned) are often replaced by pseudonyms or other information that does not reveal personal details. From a personal data protection point of view, such transcripts (transcripts of audio or video recordings) should be considered less sensitive than audio or video recordings in which participants can be identified by their voice or appearance.
Software and tools: indicate the software and tools you used to process the qualitative data, e.g. Atlas.ti, MAXQDA, NVivo. These are qualitative data analysis software that help researchers to code, structure, interpret qualitative data.
Alternatively, data can be handled manually, coded and analysed Word/Excel documents. We recommend choosing tools that are accessible to the whole research team and from which data can be easily extracted in interoperable formats.
Document the actions taken on the data – ReadMe file, codebook or other documentation.
Analysis process: detailed descriptions of the analysis process.
  • Indicate which method of qualitative analysis you used, e.g. thematic, discourse, phenomenological or narrative analysis, and describe the specific steps and principles followed
  • Describe the coding scheme or categories you used to analyse the data and explain how the codes, categories or themes were identified (e.g. inductively from the data or deductively based on theory)
Reliability of the analysis: Use strategies appropriate to the chosen qualitative data analysis method to enhance the reliability of the analysis.
  • Reflexive journaling to critically assess how the researcher’s personal experiences, values, perceptions and socio-cultural background may influence the research process, interpretation of results and conclusions
  • If the data were coded by several people, describe whether and how inter-coder reliability was ensured, e.g. whether Cohen’s kappa was calculated, whether there were discussions about code discrepancies, etc.
Results and interpretationA: It is important to remember that when presenting results with quotes, it is necessary to double-check that the participants cannot be identified.

Kvalitative data processing and analysis

Data preparation – transcription (if necessary): often, if the data is collected in audio or video format, it is recommended to transcribe it into text format for analysis. Transcription can be carried out by the researcher or by an external service provider. Transcription is increasingly being done automatically by various tools, but these tools are not always able to transcribe in English without errors. Consequently, researchers have to revise and correct the automatically transcribed texts at a later stage.
The transcription process should preferably be documented in a data management plan. It is recommended to indicate whether the transcription is verbatim or edited (i.e. whether any specific language deficiencies have been corrected).
Particular care should be taken when transcribing recordings containing sensitive information. In this case, it is advisable to avoid uploading audio or video files to online tools that do not have a clear data management policy. If transcription is outsourced, it is advisable to have a data processing agreement in place that addresses confidentiality and privacy issues.
In the transcription process, the personal details of participants (and other people mentioned) are often replaced by pseudonyms or other information that does not reveal personal details. From a personal data protection point of view, such transcripts (transcripts of audio or video recordings) should be considered less sensitive than audio or video recordings in which participants can be identified by their voice or appearance.
Software and tools: indicate the software and tools you used to process the qualitative data, e.g, Atlas.ti, MAXQDA, NVivo. These are qualitative data analysis software that help researchers to code, structure, interpret qualitative data.
Alternatively, data can be handled manually, coded and analysed Word/Excel documents. We recommend choosing tools that are accessible to the whole research team and from which data can be easily extracted in interoperable formats.
Document the actions taken on the data – ReadMe file, codebook or other documentation.
Analysis process: detailed descriptions of the analysis process.
  • Indicate which method of qualitative analysis you used, e.g. thematic, discourse, phenomenological or narrative analysis, and describe the specific steps and principles followed
  • Describe the coding scheme or categories you used to analyse the data and explain how the codes, categories or themes were identified (e.g. inductively from the data or deductively based on theory)
Reliability of the analysis: Use strategies appropriate to the chosen qualitative data analysis method to enhance the reliability of the analysis.
  • Reflexive journaling to critically evaluate how the researcher's personal experiences, values, perceptions and socio-cultural background may influence the research process, interpretation of results and conclusions
  • If the data were coded by several people, describe whether and how inter-coder reliability was ensured, e.g. whether Cohen's kappa was calculated, whether there were discussions about code discrepancies, etc.
Results and interpretationA: It is important to remember that when presenting results with quotes, it is necessary to double-check that the participants cannot be identified.