Qualitative researchers often use Computer-Aided Qualitative-Data–Analysis software (CAQDAS) which helps researchers formally code transcripts and field notes. These softwares often provide visualization tools such as word trees or word clouds, which make it easier to identify themes. 

This method may be expensive, time-consuming, and difficult to use for synchronous collaboration. However, if you invest time into learning and using the software, you can achieve a thorough analysis.

Using software to conduct thematic analysis

This is a common method for researchers who use Grounded Theory. The idea is to annotate and highlighting data, and writing memos to capture the researcher’s ideas and thought processes. This process is cheap and flexible, but it may be difficult to do collaboratively. The main advantage is a more reflexive and transparent approach to interpreting data.

Journaling to conduct thematic analysis

Data is highlighted, cut out physically or digitally, and then iteratively assembled and reassembled. This is a cheap and flexible method, and tends to work best when done collaboratively. Depending on the variance in the data, this method can be more or less complicated. Expect to spend significantly more time when there are a lot of data.

Affinity-diagramming to conduct thematic analysis

University of Michigan - Ann Arbor

https://www.nngroup.com/articles/thematic-analysis/

How to Analyze Qualitative Data from UX Research: Thematic Analysis (Maria Rosala)

- Attitudinal – thoughts, beliefs, self-reported needs obtained from interviews, focus groups, diary studies
- Behavioral – observations of people’s behavior through contextual inquiry and other ethnographic methods

Two types of qualitative data in UX research

Three common methods of conducting thematic analysis

Two types of coding:
- Descriptive: They describe what the data is about
- Interpretive: They are an analytical reading of the data, adding the researcher’s interpretive lens to it

Coding in thematic analysis

- Large quantity of data → Superficial analysis
- Rich data → Analysis becomes a description of many details
- Contradicting data → Findings are not definitive
- No goals set for the analysis → Wasted time and misdirected analysis

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