How do I do analyze and interpret qualitative data?

Step 1 - Sort data into thematic buckets

Sort your data into broad thematic buckets and describe or justify why each piece of data goes into this bucket. In this process, you will need to document what goes into each bucket (and what does not). You also might find thematic buckets that you had not anticipated beforehand. For example, you might sort your data into these buckets when assessing potential issues with your workshop:

1. Accommodation/accessibility of the materials.
2. The balance between interactive and non-interactive elements.
3. The length of the workshop.
4. The delivery mode of the workshop.
5. The connection between what was learned and the relevance for the participants’ practice of life.

In qualitative research and evaluation, this sorting step is often called 'coding'. Here's one way to go about this process. Click on each phrase to learn more.

Step 2: Look for sub-themes

In a second step, you will look at each of your thematic buckets (like the ones above) separately to see if there are sub-themes. For example, you would take a closer look at the accommodation and accessibility issues and find that there were issues related to

1. The technology used in the workshop.
2. Examples were not culturally relevant for some.
3. Fonts were hard to read, and presentation PowerPoint had too much text.
4. It was hard to hear in the back of the room, and the facilitator did not see people raising their hands in the back.

Step 3: Create write-ups

Next, you start creating a write-up for each thematic bucket and a write-up that summarizes your findings overall. This step likely includes combining some of your closed-ended, quantitative questions with your qualitative analysis. For example, you could investigate (and summarize) if there are differences in how people who rated their overall workshop experience answered the question about accommodation.

Please note that this is not a linear process. In qualitative research and evaluation, this concept is called “iterativity.” Iterativity means that the analysis process is circular: As you begin analyzing your data, you are noticing and developing different categories or themes from the information your participants have shared. This new understanding changes what you know; it changes your perspective.

With this new understanding, you then approach the data again. For example: In the beginning of an analysis, you might think that participants will describe the goals that the workshop met for them. But as you are reading and comparing their answers, you see that most of them did not describe their own goals. Rather, they talked about how valuable the workshop space was for them to connect with other professionals in their field. This may have not been their goal - and maybe not even the goal of your workshop. But now you may want to sort and synthesize the data based on a new angle: the social-emotional impacts of your workshop.

The beauty of asking open-ended (i.e. qualitative) questions is that you never know what people will say. You can count on them saying what is on their mind - and this is an important opportunity for better understanding your programming, and to be open for new discoveries, ideas, and surprises.