Coming up in my graduate program, there was a major split between the researchers who used quantitative methods (i.e., surveys and experiments) vs. those who used qualitative methods (i.e., interviews and focus groups). In part, I think this reflected a bias towards numbers in scientific fields, and in part a level of respect for the complexity of doing qualitative work well. Because of my program’s slant, I was trained only in quantitative work, but as I transitioned from a larger research university to Elon, I found that I simply couldn’t answer the kinds of questions I wanted to answer with a large survey. I took a week-long course through the University of Michigan and UNC Chapel Hill to help me grow my skillset, and I highly recommend their program. I have continued to refine my qualitative analysis skills, but I have at times been overwhelmed by the variety of analytical options! I am writing this post to offer a relatively straightforward process that has been gaining in popularity that I find to be accessible and helpful for basic qualitative work.

A note before beginning: the type of analysis I’ll be talking about is called thematic analysis. It may not be the right move for your study, and it’s important to consider the options before designing your project. For example, among the gold standard options for qualitative analysis is grounded theory, which is often used for projects with rich and intensive data coming from a typically small sample and involves a very close and detailed look into data without a specific hypothesis or a priori direction (see Savin-Baden and Major 2013 for an overview). The work that I do and that I see in working with Center for Engaged Learning researchers often includes some a priori notions and larger samples, so the simpler thematic analysis is often appropriate in our context.

So what to do if you are newer to qualitative methods and you have data that you already have some ideas about? Thematic analysis is a great choice. In a 2006 article that has become a standard in the social sciences, Braun and Clarke note that thematic analysis has been widely used, but poorly defined. In their article, they clarify best practices and outline a number of straightforward steps for analytical success. My goal in this post is to give enough of an overview to orient a new qualitative analyst and outline the basic tenets of their model for those looking for a clear process.

Braun and Clarke (2006) define thematic analysis as “a method for identifying, analysing and reporting patterns (themes) within data. It minimally organizes and describes your data set in (rich) detail.” The method is inherently atheoretical, but it can be used alongside the researcher’s theoretical model of choice, and with any sample size. While some qualitative studies focus on count or frequency of responses as the way of indicating importance of content, thematic analysis focuses solely on meaning and narrative as the outcomes of interest. Thematic analysis can be used with whole datasets or to focus in on one question or topic. Despite its primary use with deductive approaches, meaning coming into analysis with preset ideas or hypothesis, it can also be used with inductive or data-driven approaches.

The researchers outline a 6-phase process of analysis:

A diagram showing the "Steps of thematic analysis": 1. Familiarize yourself with your data; 2. Generate initial codes; 3. Search for themes; 4. Review themes; 5. Define and name themes; 6. Produce the report

The initial read-through is a critical step. Reading and re-reading the data and starting to make notes orient you to the data and allow your brain to start processing the rich spectrum of information you have obtained. Transcriptions can be tedious, but new technology is making things easier. I particularly like, but you can also conduct interviews or focus groups online and use Zoom transcriptions. Considering analysis before starting collection is essential!

The initial coding phase is, in my experience, the most time-consuming but also the most fulfilling. Braun and Clarke suggest that “codes identify a feature of the data that appears interesting to the analyst, and refer to ‘the most basic segment, or element, of the raw data or information that can be assessed in a meaningful way regarding the phenomenon’ (Boyatzis, 1998, 63)”. The coding process will depend upon whether the researcher is letting the data be the guide or if they are coming in with specific questions, and it will also depend on whether or not you are coding the entire dataset. Coding can be done manually or by using software such as NVivo or Dedoose—institutional access to coding programs will vary. In coding, it is always possible to combine codes later, so every piece of data that might be of interest should be analyzed and noted. It is also important in this step not to ignore data that does not immediately fit.

The third and fourth phases involve searching for and refining themes. This process includes starting with the full long list of codes and organizing them into categories that reflect the nature of your data. There are many creative ways of doing this. Many folks like to use concept maps (Maietta et al. 2021) to organize their codes into themes. I prefer to write out codes on index cards and then physically categorize the content. I especially enjoy doing this when I work with collaborators and students so that we can visualize our work together. Clear main themes will emerge, but subthemes may also be present—organization is at the will of the data and the discretion of the research team. After the initial themes have been defined, you should review each and remove those that may not have sufficient support and combine themes with similar content areas. This will be an iterative process.

Last, it is time to name the themes and write up the findings. The name of the theme should reflect its essence rather than paraphrase a topic (Braun and Clarke 2006). For example, a student and I are currently doing a qualitative study of disclosure experiences after sexual assault. Students frequently said that supporting a survivor of assault really depends on whether or not the survivor is ready to move forward with reporting. You might summarize this as “it depends” or a literal interpretation, but we called this “tentative justice,” which captured more of the spirit of what the participants said. After naming, the thematic map should be complete, and then you can craft your narrative of how the themes fit together and what story they tell about the population or phenomenon you are studying. In this phase it is helpful to review examples of write-ups of similar studies.

As with all qualitative work, it is important to consider your positionality. Braun and Clarke suggest considering and identifying whether you are a “cultural member” or a “cultural commentator” on the population under study. Acknowledging your role, position, and bias are essential to this type of interpretative work.

I find this process to be relatively manageable and an excellent method for capturing the spirit of a sizable quantity of data while still retaining an individualistic lens. See the full article from Braun and Clarke for full information, and review the Savin-Baden qualitative handbookfor more detail on qualitative methods overall.

Works Cited

Boyatzis, Richard. 1998. Transforming Qualitative Information: Thematic Analysis and Code Development. Sage.

Braun, Virginia, and Victoria Clarke. 2006. “Using Thematic Analysis in Psychology.” Qualitative Research in Psychology 3 (2): 77-101.

Maietta, Raymond, Paul Mihas, Kevin Swartout, Jeff Petruzzelli, and Alison Hamilton. 2021. “Sort and Sift, Think and Shift: Let the Data Be Your Guide, an Applied Approach to Working With, Learning From, and Privileging Qualitative Data.” The Qualitative Report 26 (6): 2045-2060.

Savin-Baden, Maggie, and Claire Major. 2013. Qualitative Research: The Essential Guide to Theory and Practice. London, UK: Routledge.

CJ Eubanks Fleming is an Associate Professor of Psychology at Elon University, where she serves the Faculty Fellow for Internships in the College of Arts and Sciences. In this role she evaluates department- and university-level data regarding internship outcomes, shares internship best practices with faculty, and serves as a liaison between faculty/ students and the university’s career center. She also serves as a seminar leader for the 2022-2024 research seminar on Work-Integrated Learning.

How to Cite this Post

Fleming, CJ. 2023. “Qualitative Methods for the Quantitatively Inclined.” Center for Engaged Learning (blog), Elon University. July 18, 2023.