Thematic analysis: a step-by-step guide

Thematic analysis is indeed a savior for finding qualitative information. Nowadays, the number of information sources is growing exponentially and differentiating into various niches. The big problem here is that with so many sources, it’s hard to find the right one. Thus, it’s crucial to have a way to filter out all the noise and find what you need. It is where thematic analysis comes in handy. It helps us eliminate all irrelevant information and focus on what matters most.

What is Thematic analysis?

Thematic analysis is a way of analyzing qualitative data. It involves looking at a collection of documents, such as interviews or focus groups, and identifying themes across those documents.

When to use thematic analysis?

Whenever you want to explore any qualitative dataset, interviews, discussions, focus groups, detailed surveys, open-ended questions, opinions, behaviors, and thematic analysis will help you.

Types of thematic analysis approach

There are four significant types of thematic analysis:

Inductive: It is a qualitative data analysis method used to identify and explore the major themes that emerge from a dataset. This method is particularly well-suited to data-rich details such as interview transcripts, field notes, and other forms of qualitative data.

Deductive: It helps identify, analyze, and interpret patterns in data with some preconceived themes. This approach can examine various data sources, including textual, numerical, and categorical data.

Semantic: This thematic analysis type is a process of analyzing the meaning of a text by identifying the recurring themes within it.

Latent: This method identifies and codifies the underlying themes of a large body of text. It assists in understanding the hidden meanings and messages within data. It is also useful when finding meaning in the text is complex, such as when there are few or no prior events.

How to use thematic analysis?

Step 1: Familiarize yourself with the data

Before any data analysis, it is essential first to become familiar with the data. It refers to understanding what the data represents and what information it contains. You can do it by first transcribing audio data and then reading the
data sets. You can also supplement your reading with note-taking to understand the data well.

Step 2: Develop a coding scheme

Generating preliminary codes is the second important step in thematic analysis. This step involves reviewing the data and identifying initial themes or ideas. You can locate themes by looking for patterns in the data. Once these themes are identified, you can code the data. Coding is assigning a label to data to sort further and analyze it.
Okay, let’s understand forming preliminary codes with an example: Suppose there is survey data that contains information on what users desire in an online shopping app: Some said they want automated recommendations, and some demand a smooth interface, seamless UI or UX, etc. While performing thematic analysis, you can form automated virtual features and UI/UX codes using this information.

Step 3: Identify themes

Once the data is coded, the analyst can look for themes and patterns. In the above example, we formed codes; now, we will extract themes from them. For example, automated and virtual features codes give us respective themes of automation and virtual reality. First, to identify the themes, ensure enough data to support a theme. Now, organize the coded data as the first step, identify the patterns, and ultimately find themes.

Step 4: Refine the themes

After identifying and categorizing themes, you can further analyze them to understand their significance. To do this, first, check whether the themes are comprehensive enough. Check if there is any issue with the themes, such as collapsing or repeated themes. If you have identified any issue, resolve it first.

Step 5: Interpret the themes

Defining and naming themes will help in categorization and further interpretation. You can do this by beginning by elaborating on the significance of each theme. Now, you can relate the themes to the research question. You can add substance to your theme’s interpretation by providing examples to demonstrate the themes.

Step 6: Report the findings

To report the findings, begin by summarizing themes. After theme summarization, you have to present all the findings. At last, you can add a recommendation section to elaborate on your findings. You can write all this in a research paper format- including the motives, evidence, trends, patterns, methodologies, and outcomes.

Wrap up

The thematic analysis includes getting familiar with the data, creating preliminary codes, further coding them to draw patterns, and finally, fetching themes. Thematic analysis is undoubtedly a powerful tool for understanding data and has applications in dynamic settings. If you want incredible thematic analysis services, connect with ANT immediately and get your custom quote!