Qualitative analysis of interview data, a basic step by step guide.
Part one, a description of each step. Step one: reading the transcripts. Quickly browse through all
the transcripts as a whole. Then, make notes about your first impressions. Re-read the transcripts
again one by one very carefully, line by line.
Step two: start labeling relevant pieces, such as words, phrases, sentences or sections in the
transcripts. And these labels can be about actions, they can be about activities or whatever you
think is relevant. And this process is called coding or sometimes it's referred to as indexing.
Here's an example of an interview transcript that has been coded.
So, how do I know what to code, you might wonder. Well, you might decide that something is
relevant to code because it is repeated in several places or perhaps it's something that surprises
you or it might be that the interview him or herself explicitly states that this is important or you
have read about something similar in previously published reports, for example, in scientific
articles, or it reminds you of a theory or a concept, or
for some other reason that you think is relevant.
You can use pre-conceived theories and concepts or you can be more open-minded. You can aim
for a description of things that are superficial or you can code and aim for a conceptualization of
underlying patterns. It's up to you. It's your study and your choice of methodology. You are the
interpreter and these phenomena are highlighted because you think they are important. Just make
sure that you tell your reader about your methodology and the choices that you make and you do
that under the heading method.
In your coding, try to be unbiased and stay close to the data, i.e. the transcripts. Don't hesitate to
code plenty of phenomena. You can have lots of codes, even hundreds.
Step three: decide which codes are the most important and create categories by bringing several
codes together. Go through all the codes created in the previous step. Read them with a pen in
your hand. You can create new codes if you want to by combining two or more codes. You
don't have to use all the codes that you created in the previous step. In fact, many of these initial
codes can now be dropped. Keep the codes that you think are important and group them together
in the way that you want. Create categories, in other words.
You can call them themes if you want to.
Here's an example. I've grouped these codes together and created a category. Here's a second
example and here's a third one. The categories don't have to be of the same type. They can be
about objects, processes, differences, or whatever. Be unbiased and creative and try to be open-
minded. Your work now, compared to the previous steps, is on a more general, abstract level.
You are conceptualizing your data.
Step four: label categories and decide which are the most relevant and keep those and also
decide how they are connected to each other. Label the categories. Well, in my example, I had
three different categories. I'm going to call the first one adaptation and the second one is seeking
information and the third one is problem solving.
At this stage, I should also describe the connection between these categories. These categories
and the connections are the main results of my study. It's the core of the whole study, at least
when it comes to the results. It is new knowledge about the world from the perspective of the
participants in my study.
Step five: here are some options. You could, if you want to, decide if there's a hierarchy among
the categories. You could also decide if one category is more important than the others and you
could also draw a figure if you want to. Here's an example that I put together.
Step six: it's time to write up your results. Under the heading results, describe the categories and
how they are connected. Use a neutral voice and don't interpret your results. Under the heading
discussion, write out your interpretations and discuss your results. Interpret the results in light of,
for example, results from similar, previous studies published in relevant scientific journals in your
field, theories or concepts from your field, or other relevant aspects.
Part two: ending remarks. I have assumed that your task is to make sense of a lot of unstructured
data, i.e. that you have qualitative data in the form of interview transcripts. However, remember
that most of the things that I have said in this tutorial are basic and also apply to qualitative
analysis in general. What does that mean? Well, it means that you can use the steps described in
this tutorial to analyze, for example, notes from participatory observations, documents, web
pages, or other types of qualitative data.
Suggested reading: Alan Bryman's book 'Social Research Methods' together with Steinar
Kvale's and Svend Brinkmann's book 'InterViews' are excellent for anyone who wants to dig in
deeper and understand how to do qualitative interview research.
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