What is it?
You can use thematic (map) analysis to analyze qualitative data from user studies, such as interviews, focus groups, workshops, diary studies, or contextual inquiries. It is possible to analyze data with behavioural elements or attitudes (thoughts, believes, and reported needs etc.) with a thematic map analysis. In the analysis process significant data segments are first identified and then summarized in keywords or key phrases. Further, in an iterative process those keywords or key phrases are then used to derive underlying “themes”. “Themes” are reoccurring thought patterns or concepts in the data. The “themes” derived from the analysis can be visualized in a mind-map.
What is the outcome?
Outcome of the analysis is a set of themes. The herein presented approach of a thematic map analysis focuses on a visual approach to derive the themes and present them in a mind-map in the end. Finally, the derived themes can then be interpreted further – set in context with the research question and be compared to literature.
Tools for the analysis depend on the data, available resources, and personal preferences. In general, this analysis does not need much equipment. The minimal required equipment is the data, pen and paper. Small data sets can also be analyzed with MS Word or another text editor, for example, Only Office or Libre Office. In contrast, for larger amounts of data it might be more efficient to use a tool (such as Nvivo or the R package RQDA (free)) to conduct the first steps of analysis. After initial analysis steps, keywords / key phrases are developed into themes. Those steps can be supported by mind-mapping software (such as Vue (free), MS Visio, XMind, or Mindjet). Alternatively, you can develop the themes on paper. I used paper and VUE.
How to do it:
The following section describes a stepwise approach to generate themes from qualitative data. Braun and Clarke (2008) described this process in their paper. However, I skipped step number 3 as I found it overlapping with the other steps.
- Transcribe the data (bring the audio recording into a written form).
- Familiarization with the data – Write your research question on a paper and put it visible in front of you. Read through the data. During this first familiarization reading, be conscious to just take in information. Avoid thinking about an interpretation. This is important to get into the mindset of the user / participant. Also refrain from making notes. For a good overview be conscious to read word by word. Unconscious readers often skip words while reading in trade of a faster reading process. However, small words are important and might change meaning of a sentence.
- Start coding – Read the data again, section by section. Have your research question in sight. Now, start thinking of reoccurring concepts. Mark the relevant thought patterns in the transcript. Next, add a note with the dedicated keyword / or key phrase next to each pattern. Keywords / key phrases are representative for a pattern. On paper phrases could be highlighted with a text marker and the keyword note could be added on the margin. Adding the keyword on the margin will make it easier to scan through the keywords and refine them. Use different colours to distinguish groups or keywords. You could also mark the text, cut it out, and then put all text snippets from one keyword on a heap. Similarly, in a text editor phrases can be marked in the text and keywords can be added as, e.g., comment. If you apply a visual approach, start writing all keywords on a paper, initially, this will be unsorted. Try to place related keywords together in the next revision.
- Review themes – Go through the data again, narrow down the keywords / key phrases, and start merging them hierarchically. Do this repeatedly and refine the hierarchy. Take a day break before you conduct the next step of the analysis. You could use a mind-map to visualize this step. If you started using a mind-map in step 2), you can now select a name for each set of related keywords. Otherwise, start to write keywords on a paper and place related keywords together. With each repetition of this step the mind-map will become more precise and the keywords will form into hierarchical ordered themes.
- Check the theme’s names – Check that each theme is named clearly, is representative of the data, is related to the research question, and that sub-themes and main themes fit together.
- Writing up the report – Carefully select extracts from the data that are most representative for your themes. Subsequently, use them in your report to support your themes. State clearly in your report what the derived themes are. They can be interpretations from statements or they can be extracts of the data (without interpretation).
Speaking from my personal experience, initially I found it difficult to begin the analysis process. My first mind-map covered a lot of information from the data. A lot of things seemed to be important. That is why it is important to keep the research question in sight. Regularly revisit your goal (research question) and focus on related information in the data. The initial mind-map broad can be broad. I found it easier to reduce information from a large mind-map. However, starting from a broad mind-map will take extra analysis iterations to reduce keywords and group them into themes.
For me, a mind-map was a suitable visualization for the analysis process. The map shows the progress and all information is available at a glance. Additionally to the mind-map I generated a table to track the progress. The table can show at a glance weather all themes are appropriately linked in the data. Thereby, each column presented a theme and supporting extracts from the data in the rows below.
If you have trouble identifying themes, have a look in this video. It is from Victoria Clarke. Check this video from Graham Gibbs if you want to improve your coding ability (assigning keywords / key phrases to text segments).
The thematic map analysis approach is described for example in a paper from Braun and Clarke (2008). The Nielsen Norman Group (NN/g) recently published an article about thematic analysis as well, by Maria Rosala.
- Braun, V. & Clarke, V., 2008. Using thematic analysis in psychology. Qualitative research in psychology, 3(2), pp. 77-101.
- Lynass, R., Pykhtina, O. & Cooper, M., 2012. A thematic analysis of young people’s experience of counselling in five secondary schools in the UK.
- Counselling and Psychotherapy Research: Linking research with practice, 12(1), pp. 53-62.
- Rosala, M., 2019. How to Analyze Qualitative Data from UX Research: Thematic Analysis. (online) https://www.nngroup.com/articles/thematic-analysis/?utm_source=Alertbox&utm_campaign=b16e093369-QualitativeDataAnalysis_Treemaps_20190930&utm_medium=email&utm_term=0_7f29a2b335-b16e093369-24404113
- Clarke, V., 2017. What is thematic analysis? (online) https://www.youtube.com/watch?v=4voVhTiVydc
- Gibbs, G. R., 2010. Coding part 2: Thematic coding. (online) http://www.youtube.com/watch?v=B_YXR9kp1_o