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