Analysing qualitative data
Qualitative data analysis involves the identification, examination, and interpretation of patterns and themes in data and determines how these patterns and themes help answer the research questions at hand.
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How do I analyse qualitative data?
Qualitative analysis focuses on cases (rather than variables). A case could be an individual person, a whole class of students or an instance of a particular phenomenon (e.g. bullying). Comparative analysis between and across cases can be a powerful way of identifying patterns in the data.
Questions that can be considered when analysing qualitative data include (but are not limited to):
- What patterns/common themes emerge around specific items in the data?
- How do these patterns (or lack thereof) help to shed light on the broader study question(s)?
- Are there any deviations from these patterns?
- If, yes, what factors could explain these atypical responses?
- What interesting stories emerge from the data?
- How can these stories help to shed light on the broader study question?
- Do any of the patterns/emergent themes suggest that additional data needs to be collected?
- Do the patterns that emerge support the findings of other corresponding qualitative and quantitative analyses that have been conducted?
Methods of analysing qualitative data usually include (but are not limited to):
- Documentation of the data and the process of data collection
- Organisation/categorisation of the data into concepts/themes
- Connection of the data to show how one concept/theme may influence another
- Testing theories, by evaluating alternative explanations and searching for negative cases.
The final report of any qualitative analysis may include a number of formats such as text, maps, charts, images and/or sound.
More information on analysing qualitative data, see this overview of qualitative data.
- Credibility – are the results of the research credible or believable from the perspective of the participant in the research?
- Transferability – has the researcher adequately described the research context and the assumptions that were central to the research?
- Dependability – have the research methods accounted for changes in the research setting and how these influenced the research?
- Confirmability – has the researcher acknowledged their own position in the research? Have the findings been triangulated with other data to confirm and strengthen the findings?
The quality of the data is usually also a reflection of the skills and rigour of the researcher. The researcher needs to be involved in every step of the analysis, be responsive, flexible and a good listener, and able to reflect on their own role in the research.