Searching for themes

Braun and Clarke (2006) suggest that searching for themes starts when all data have been initially coded. The process involves sorting and collating codes and their associated data

extracts into initial or potential themes based on the researcher’s analytic reflection. A theme can be described as “a phrase or sentence describing more subtle and tacit processes” (Rossman and Rallis 2003, p.282), or as a cluster of linked categories. Braun and Clarke (2006, p.82) suggest that a theme represents

“something important about the data in relation to the research question, and represents some level of patterned response or meaning within the data set.”

An important question addressed by Braun and Clarke (2006) is the size of a pattern or theme to be considered as such. Given the nature of qualitative research, there is no minimum size to serve as a cut-off point. As a result:

“A theme might be given considerable space in some data items, and little or none in others, or it might appear in relatively little of the data set. So, researcher judgement is necessary to determine what a theme is. […] Furthermore, the ‘keyness’ of a theme is not necessarily dependent on quantifiable measures / but rather on whether it captures something important in relation to the overall research question” (Braun and Clarke 2006, p.82).

Theme identification can be performed at two levels: semantic (or manifest, or explicit) and latent (Boyatzis 1998; Braun and Clarke 2006). Semantic level theme identification focuses on what has been said, on what is visible and apparent in the data. Latent level focuses beyond what was said, that is on the underlying ideas, in an effort to interpret and provide meanings of the phenomenon under study. Although “a thematic analysis typically focuses exclusively or primarily on one level” (Braun and Clarke 2006, p.84), an attempt was made in this study to provide themes from both levels. This required extensive reading of the transcripts and continuous reviewing of codes, categories and themes.

MS Excel proved a very helpful tool in the search for themes. The researcher used Excel’s elaborate sorting functions to produce numerous sorts of the data extracts. Similar codes were grouped and regrouped together in a continuous process. Braun and Clarke (2006) suggest that the search for themes can be facilitated with some sort of visual representations such as tables or mind maps. The Excel spread sheets were already in table format, and were therefore printed in A3 size paper. Highlighters were used during reading and re-reading in an effort to identify and review themes. However, due to the existence of numerous columns in the Excel spread sheets they proved not that practical for data visualization: Printouts had to be either several pages wide, or in case the whole spread sheet was forced to fit into one A3 page the resulting font size was so small to read. At the same time, the idea of mind maps seemed attractive given the researcher’s prior professional experience in using mind mapping software during the analysis of brainstorming sessions.

In an effort to improve data visualization and therefore to facilitate identification of themes the researcher employed mind mapping. This decision was influenced by Burgess-Allen and Owen-Smith (2010) who proposed the use of mind mapping techniques for rapid qualitative data analysis. In general, a mind map is

“a diagram used to represent concepts, ideas, or tasks linked to and arranged radially [sic] around a central key word or idea. Primarily branches represent the major ideas or themes around the central topic, and secondary branches tend to include more concrete illustrative examples” (Burgess-Allen and Owen-Smith 2010, p.407).

Although Burgess-Allen and Owen-Smith (2010) propose thematic mapping as a tool for the analysis of qualitative data, their approach is completely different from the one followed in this study: They propose mind mapping as part of the focus group discussions, and later during the analysis as an alternative method to thematic analysis. Although this approach was not adopted in the present study, the researcher used the same technique as a visual tool to facilitate theme identification, theme reviewing, and report writing.

Mindjet MindManager Pro software was used to develop the mind maps. Each of the nine MS Excel spread sheets was reviewed carefully and the initial codes have been copied into the mind map: For each coded data extract an entry in the mid map was created consisting of an identification code in the form of FGX-YYY where FGX the number of the FG discussion, and YYY the section number of the specific data extract. This was made (a) to facilitate reference to the original data extract; but also (b) to enable trail of evidence. A selected number of data extracts were also copied into the mind map to provide an illustration of the code. Figure 4.6 provides the final version of one of the mind maps created for the analysis of the impacts of social networking sites holiday photos during the dreaming stage of the travel process. This was indeed a very time consuming and elaborate process since data transfer between MS Excel and Mindjet MindManager Pro had to be made manually via simple copy and paste of the data extracts and typing of additional information. However, the effort paid off since the mind map software provided an excellent workspace where data extracts and codes could be easily moved between codes, categories and themes. A total of 6 mind maps were prepared to assist in data analysis. Working versions of the mind maps are included in Appendix 6. Each map was printed on an A3 paper and studied in parallel with the transcripts and the Excel files. This process involved continuous loops between codes, categories and themes to the transcript, the audio files, the Excel and the mind maps. Data extracts were reviewed continually in relation to the new categories and themes and had to be moved also in the mind map.

Figure 4.6: A sample mind map created during the searching for themes phase

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