- 1. What is data analysis?
- 2. A grounded theory approach to data analysis
- 3. Processing your data in preparation for analysis
- 4. Coding interview transcripts and other data
- 5. Analysis in a collaborative context
- 6. Critically evaluating your data: reflexive data analysis
- 7. Summary
- 8. Feedback Survey
Data analysis involves the processing and interpretation of the data that you collect in your fieldwork in order to draw out key themes and insights for answering your research question.
Key sources of data include your research diary, fieldnotes and interview transcripts.
There are many different approaches to data analysis. The approach you use is guided by your methodology, methods and the type of data that you collect in the field. Grounded theory is one of the most common approaches to data analysis that is used in qualitative research, where theory is generated through a close analysis of data using procedures such as coding and constant comparison of data with theory.
Data needs to be processed in preparation for analysis. Processing data generated from interviews includes the turning interview recordings into written interview transcripts, and annotating transcripts.
Coding is a form of data analysis that involves labelling and organizing data to extract themes. Codes can be grouped into categories containing master and subcodes and can be organized in a code book listing all your codes. A code book can be used to re-code your data.
Research projects that are interdisciplinary, comparative, or participatory in nature may involve collaborative data analysis. Collaborative analysis requires planning to ensure that analysis is undertaken in a consultative and consistent way within the timetable of the research project.
Elements of developing a critical and reflexive data analysis practice include:
(a) Fact checking and contextualizing data.
(b) Dealing with recalcitrant data and recognizing cognitive bias.
(c) Sitting with data.
(d) Listening to silences, recognizing limitations and revision.