Thematic Analysis | A Step-by-Step Guide
Thematic analysis is a research method used to analyze qualitative (nonnumeric) data. The purpose of thematic analysis is to identify common patterns or ideas (themes) in a dataset.
Thematic analysis is a flexible approach to qualitative analysis that can be used across many fields, including healthcare, psychology, education, and consumer research.
- How do university students cope with stress during final exams?
- What are young adults’ experiences when buying their first home?
- What are commuters’ opinions about public transit in their city of residence?
- How do teachers and students perceive the role of technology in education?
What is thematic analysis?
Thematic analysis is the process of summarizing recurring or important patterns (called “themes”) in a qualitative dataset. Thematic analysis involves organizing, summarizing, and interpreting qualitative research.
A benefit of qualitative datasets is their large size and richness. They can contain many different types of data (e.g., interview transcripts, written documents, or photo and video recordings) from different sources.
However, this benefit is also a disadvantage—extracting meaning from so much data can be time-consuming and overwhelming. Thematic analysis is a structured approach researchers can follow to organize qualitative data and identify themes. It is often broken down in 6 steps, as outlined by researchers Virginia Braun and Victoria Clarke.
Braun and Clarke (2006, p. 82) define a theme as “a patterned response or meaning” that “captures something important about the data in relation to the research question.”
In other words, a theme is an idea or concept that (1) repeats throughout a dataset and (2) provides insight into your research question.
There are no set rules for how frequently a theme must repeat in a dataset; the researcher must use their own judgement. The researcher must also use their expertise to decide whether a theme is related to a research question.
Types of thematic analysis
When conducting thematic analysis, researchers can use different strategies to identify themes. Two main approaches are inductive and deductive thematic analysis.
Inductive thematic analysis
Inductive thematic analysis takes a bottom-up approach to analyzing data and looking for themes.
Inductive theoretical analysis is data-driven: the researcher considers the dataset itself, looking for existing patterns without considering any overarching theoretical framework or research questions.
Because it does not rely on existing theories or preconceptions, inductive thematic analysis is flexible. This approach provides a detailed description of the entire dataset and is good for exploring new research areas. However, the lack of a theoretical framework may make results difficult to interpret.
Deductive thematic analysis
Deductive thematic analysis, also called theoretical thematic analysis, relies on existing theory to identify themes in a dataset. The researcher searches for and identifies themes while considering existing frameworks and related literature.
Because of its reliance on existing theories, deductive thematic analysis may not capture unexpected results in a dataset. For example, if you analyze a series of college student interviews using deductive thematic analysis to study financial attitudes, you may identify themes related to budgeting and saving but miss themes related to mental health or social pressure.
Inductive vs deductive thematic analysis example
Consider the following example of how inductive and deductive thematic analysis differ.
Following a deductive approach, you might analyze the data according to the Food Choice Process Model, identifying themes specified by the model’s framework.
If you took an inductive approach, you would review the data and identify any themes related to eating habits. You may notice that people’s food choices are often guided by what they see on social media, which was not described in the original Food Choice Process Model.
Semantic vs latent thematic analysis
Another consideration when you are deciding how to identify themes is whether to use a semantic versus latent approach.
- If you take a semantic (or explicit) approach, you consider what is explicitly stated in the data without trying to read between lines or interpret someone’s meaning.
- If you take a latent (or interpretative) approach, you go beyond what someone has said and consider why they responded in this way, thinking about subtext or assumptions.
Your choice to identify semantic versus latent themes will depend on how deeply you wish to analyze your data—whether you want to describe the data at surface level (semantic) or focus on underlying patterns (latent).
The 6 steps of thematic analysis with examples
The definitive approach to thematic analysis was created by Virginia Braun and Victoria Clarke. Their research paper on the topic (Braun & Clarke, 2006) outlines six steps to conduct a thematic analysis:
- Familiarization
- Generating codes
- Searching for themes
- Reviewing themes
- Defining and naming themes
- Writing up results
Step 1: Familiarization
The first step of thematic analysis is getting to know your data. Familiarization might involve transcribing your data, reading (and rereading!) everything, and taking initial notes on the content.
Step 2: Generating codes
Codes are labels or tags used to describe sections of data. Coding is the process of highlighting sections of your data and labeling them using codes.
Step 1 (Familiarization) should give you a good idea of what interesting ideas are present in your data, which will help you generate a list of codes to label. Consider the following example of how you might label codes in an interview transcript.
| Transcript | Codes |
|---|---|
| Interviewer: Tell me about your experience working from home during the COVID pandemic.
Respondent: Yeah, working from home during the pandemic was definitely an adjustment. Sometimes it was hard to get in touch with my team. At first, I struggled with setting boundaries between work and personal life because everything happened in the same space. But over time, I found that having a dedicated workspace and sticking to a routine helped a lot. |
|
When coding, be as thorough as possible. Code for as many potential themes as you can, and try to preserve context whenever possible (e.g., highlighting “struggled with setting boundaries” is less informative than highlighting “struggled with setting boundaries between work and personal life”). Codes are also not mutually exclusive—more than one code may apply to a single data excerpt.
Step 3: Searching for themes
Codes serve as the building blocks for themes. After you’ve gone through all of your data and identified and labeled different codes, these codes can be organized to create a thematic map (i.e., a collection of themes that describes your data).
A code may stand on its own as a theme, or multiple codes may be combined into a single theme. Visual tools, like tables or mind maps, can be used to connect codes and identify themes in your data. Consider how the codes from our previous example may be used to create themes.
| Codes | Theme |
|---|---|
|
Challenges |
|
Time management |
|
Creating a productive work environment |
As you identify themes associated with each code, you will start to group together related data excerpts, which serve as evidence for your theme.
Step 4: Reviewing themes
After you’ve created your thematic map, you must evaluate it. Theme refinement happens at two levels.
- At the level of individual themes:
Consider the coded data extracts associated with each theme to ensure that they are cohesive and accurately reflected by the theme. The following questions may be helpful when evaluating each theme:
- Are there enough data to support this theme?
- Does this theme overlap with any others (and should they be combined)?
- Is the theme too broad (and could it be separated into two themes)?
This process may involve revising or deleting existing themes and creating new themes.
- At the level of the entire dataset:
After you’ve evaluated your individual themes, you should consider how all of the themes relate to the dataset as a whole. You are evaluating whether your proposed themes accurately capture the dataset. What is considered “accurate” will depend on your research objectives.
Step 5: Defining and naming themes
Once you’re satisfied that your themes accurately reflect all important aspects of your dataset, you need to name and define them. The name for each theme should be descriptive, clear, and concise.
The description for each theme should capture the narrative of the underlying data (i.e., what story do the related data excerpts tell?) and why this narrative is important to your research question more broadly.
Step 6: Writing up results
The final step of thematic analysis is writing up your findings. Your goal is to tell the story of your data using the themes you’ve identified. You want to convince the reader that the data provide evidence of the themes you have identified and that these themes sufficiently address your research question.
Like any research paper, your thematic analysis should include an introduction that provides your reader with the background information necessary to understand your work. You should clearly outline your research methods and describe your findings in the context of existing work.
Pros and cons of thematic analysis
When evaluating whether thematic analysis is the best research method to address your research question, consider its benefits and disadvantages.
- Flexible
- Suitable for many types of data
- Can analyze large datasets
- Subjective (influenced by researcher bias and preconceptions)
- Often time-consuming
- Results may not generalize
Frequently asked questions about thematic analysis
- What is a theme?
-
A theme is an idea or pattern that recurs throughout a dataset and is related to a specific research question.
The identification of themes is a core component of thematic analysis, which is a research method commonly used to analyze qualitative data.
- What are Braun and Clarke’s 6 steps to thematic analysis?
-
In their 2006 paper, researchers Virginia Braun and Victoria Clarke outlined the following 6 steps for conducting thematic analysis:
- Familiarization
- Generating codes
- Searching for themes
- Reviewing themes
- Defining and naming themes
- Writing up results
- What is reflexive thematic analysis?
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Reflexive thematic analysis is a type of thematic analysis that centers the researcher’s interpretation of the data.
Reflexive thematic analysis acknowledges the subjective nature of data interpretation—rather than focusing on uncovering some “ground truth” in the data, researchers are encouraged to engage with their data and use their own knowledge and experiences for interpretation and analysis.
- What is thematic content analysis?
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Thematic content analysis is often defined in different ways, and the term is sometimes used interchangeably with thematic analysis or qualitative content analysis.
Qualitative content analysis focuses on systematically summarizing the prevalence of specific codes in a dataset, whereas thematic analysis not only codes data but also identifies themes and underlying narratives.
Thematic content analysis is sometimes defined as a hybrid between these two methods: much like qualitative content analysis, its purpose is to describe a body of qualitative data, but data are broken down into themes rather than more simple codes.
Because “thematic content analysis” can be interpreted in different ways, it’s important to provide a detailed description of your methodology if you choose to use this term.
