There are many articles in Research Design Review concerning qualitative data analysis. And, like most articles on this subject matter, there is invariably a direct or indirect reference to themes, i.e., an analysis process that works toward deriving themes from the data. With so much of the focus on developing themes in qualitative data analysis, it is appropriate to consider the all-important question, What is a theme?

To answer that question, it may be easier to begin with what is not a theme. A theme is not a product of word frequency counts. For example, if the word “communication” comes up frequently by participants in a focus group study pertaining to what patients need from their physicians, “communication” is not a useful theme that the researcher draws from the data. If an in-depth interview (IDI) study is conducted with older adults concerning their experiences during the pandemic and the word “technology” or words “technological devices” are often used in response to the interviewer’s questions, these words do not constitute a theme.

Similarly, the dominance of participants’ comments within a particular topic area does not necessarily represent a theme to be derived from the data. The focus group participants may have talked a lot about some aspect of communication as it relates to the patient-physician relationship; however, “communication” may not be the appropriate theme. And, even though the IDI participants may have brought up the topic of technology in response to many of the interviewer’s questions, “technology” is not necessarily a theme to be derived from the analysis.

Using these examples, “communication” and “technology” are better classified as categories or “buckets” rather than themes. As discussed in “The Important Role of ‘Buckets’ in Qualitative Data Analysis”

These categories basically represent buckets of codes that are deemed to share a certain underlying construct or meaning. In the end, the researcher is left with any number of buckets filled with a few or many codes from which the researcher can identify patterns or themes in the data overall.

It is within and across the categories that researchers derive themes. To do that, the researcher approaches each bucket with the question, ‘In what context was this construct discussed?’ In other words, on what basis or meaning did participants talk about the broad constructs the researcher perceived in the data. It is only by digging into each bucket and then analyzing across buckets that the researcher will create rich themes leading to useful outcomes for the research sponsor. For example, when the researcher explores the “communication” bucket they may find that some participants were only focused on the mode of communication (e.g., email compared to phone communication) while others mostly talked about the frequency and speed of communication, and still other participants spoke entirely about the content of communication. The researcher may be tempted to assert that communication is the theme as defined by these various areas of emphasis but that is a weak approach compared to the rich insight that can be gained by extending the analysis across categories.

By looking across categorical buckets researchers derive a contextually driven understanding of the data. An example of this is provided in the article “Actively Conducting an Analysis to Construct an Interpretation.” This article demonstrates (by way of an IDI study with financial managers) the researcher’s creation of the theme “strong partnership” from two categories — partner and communication — derived from three codes in the partner category and two codes in communication (see the image above). As stated in that article

The theme “strong partnership” did not simply emerge from the data, it was not lying in the data waiting to be discovered. Rather, the researcher utilized their analytical skills, in conjunction with their constructed understanding of each participant’s contribution to the data, to create contextually sound, meaningful themes such as “strong partnership.” Then, with the depth of definition associated with each theme, the researcher looked within and across themes to build an interpretation of the research data targeted at the research objectives, and provided the users of the research with a meaningful path forward.