What is it?
With a thematic (map) analysis you can analyze qualitative data from user studies, such as interviews, focus groups, workshops, diary studies, or contextual inquiries. The analysis can be applied on data with behavioral elements or attitudes (thoughts, believes, and reported needs etc.). During the analysis you will iteratively read the data and interpret it. Thereby, emerging significant data segments will be summarized into keywords or keyphrases. Those keywords or keyphrases are then used to derive underlying “themes”. Themes are reoccurring thought patterns or concepts in the data. The derived themes can be visualized in a mind-map. In general, mind-maps can be used throughout the analysis process to help your interpretation.
What is the outcome?
Outcome of the analysis is a set of themes. Those themes can then be interpreted further in a report – described in relation to the research question and be compared to literature. The below described approach of a thematic map analysis focuses on a visual approach to derive the themes and present them in a mind-map.
In general, you do not need much equipment for this analysis. The selected tools depend on the data, availability, and personal preferences. Minimal equipment is the collected data, pen and paper. For smaller amounts Word or another text editor can be used for support. 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. Later steps of the analysis, that bring keywords / keyphrases into themes, can be supported by mind-mapping software (such as Vue (free), MS Visio, XMind, or Mindjet). Alternatively, the themes can be developed on paper. I used paper and VUE.
How to do it:
The following is a step wise approach to generate themes from qualitative data. The process is adapted from a paper by Braun and Clarke (2008), but I left out step 3) from their paper 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. Often words are skipped 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 and add a note with the dedicated keyword / or keyphrase next to each pattern. Keywords / keyphrases 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. Different colours help 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. In a text editor, similarly, 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 / keyphrases, and start merging them hierarchically. Do this iteratively and refine the hierarchy. Take a day break before you conduct the next step of the analysis. This analysis step could be conducted in a mind-map. 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 and use them in the report-text to support your themes. A report needs to state what the derived themes are. They can be interpretations from statements or they can be on the level of extracts of the data (without interpretation).
Speaking from my personal experience, initially I found it difficult to get into 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 and regularly revisit it to focus on information in the data that is related. It is ok to keep the initial mind-map 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.
I found a mind-map suitable as a visualization for the analysis process. Beside, it was useful to generate a table with each scheme in a column and in the rows below supporting extracts from the data. That helped to organize and data and to verify that themes are linked in the data appropriately.
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 / keyphrases 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.
(1) Braun, V. & Clarke, V., 2008. Using thematic analysis in psychology. Qualitative research in psychology, 3(2), pp. 77-101.
(2) 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.
(3) 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
(4) Clarke, V., 2017. What is thematic analysis? (online) https://www.youtube.com/watch?v=4voVhTiVydc
(5) Gibbs, G. R., 2010. Coding part 2: Thematic coding. (online) http://www.youtube.com/watch?v=B_YXR9kp1_o