What is the purpose of familiarization in the first step of Thematic Analysis?

To get to know the data collected

What is the purpose of coding in Thematic Analysis?

To highlight sections of the data and come up with shorthand labels or 'codes' to describe their content

Why is it important to familiarize with the data before analyzing individual items in Thematic Analysis?

To get a thorough overview of all the collected data

To immediately start coding the data

Steps in Thematic Analysis. Step 1: Familiarization The first step is to get to know our data. It’s important to get a thorough overview of all the data we collected before we start analyzing individual items. This might involve transcribing audio, reading through the text and taking initial notes, and generally looking through the data to get familiar with it. Step 2: Coding Next up, we need to code the data. Coding means highlighting sections of our text – usually phrases or sentences – and coming up with shorthand labels or “codes” to describe their content. Let’s take a short example text. Say we’re researching perceptions of climate change among conservative voters aged 50 and up, and we have collected data through a series of interviews. Step 3: Generating themes Next, we look over the codes we’ve created, identify patterns among them, and start coming up with themes. Themes are generally broader than codes. Most of the time, you’ll combine several codes into a single theme.

Steps in Thematic Analysis.Step 4: Reviewing themes Now we have to make sure that our themes are useful and accurate representations of the data. Here, we return to the data set and compare our themes against it. Are we missing anything? Are these themes really present in the data? What can we change to make our themes work better? If we encounter problems with our themes, we might split them up, combine them, discard them or create new ones: whatever makes them more useful and accurate. For example, we might decide upon looking through the data that “changing terminology” fits better under the “uncertainty” theme than under “distrust of experts,” since the data labelled with this code involves confusion, not necessarily distrust. Step 5: Defining and naming themes Now that you have a final list of themes, it’s time to name and define each of them. Defining themes involves formulating exactly what we mean by each theme and figuring out how it helps us understand the data. Naming themes involves coming up with a succinct and easily understandable name for each theme. For example, we might look at “distrust of experts” and determine exactly who we mean by “experts” in this theme. We might decide that a better name for the theme is “distrust of authority” or “conspiracy thinking”. Step 6: Writing up Finally, we’ll write up our analysis of the data. Like all academic texts, writing up a thematic analysis requires an introduction to establish our research question, aims and approach. We should also include a methodology section, describing how we collected the data (e.g. through semi-structured interviews or open-ended survey questions) and explaining how we conducted the thematic analysis itself. The results or findings section usually addresses each theme in turn. We describe how often the themes come up and what they mean, including examples from the data as evidence. Finally, our conclusion explains the main takeaways and shows how the analysis has answered our research question. In our example, we might argue that conspiracy thinking about climate change is widespread among older conservative voters, point out the uncertainty with which many voters view the issue, and discuss the role of misinformation in respondents’ perceptions.

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