Whether you are a teacher teaching qualitative research methods or a student learning methodology, this blog post aims to help you answer the questions people learning how to code qualitative data always ask:

• ‘Am I coding the right way?’
• ‘I start by doing X and then doing Y, is that the right order?’
• ‘How do I know if I am coding right?’...

To set our expectations regarding coding as a research practice, we need to understand what happens when we code:

• Coding is a pattern-finding activity;
• Coding is an iterative process.

Coding as an iterative process
Coding is not a linear process with clear cut steps that you take one after the other. Coding is an iterative process. By iterative, I mean a process for arriving at a decision or a desired result by repeating rounds of analysis or cycles of operations. The objective is to get closer to the desired results with each repetition, or rather, with each iteration.

To give you an example to illustrate what 'iteration' means, we can turn to how the word iteration is used in computer programming. In computer programming, iteration represents a process wherein a set of instructions or structures are repeated in a sequence a specified number of times or until a condition is met. When the first set of instructions is executed again, it is called an iteration.

In the case of coding qualitative data, this means that you keep repeating the process of coding until you meet the criteria of good analysis.

I have prepared an exercise to  enable you to experience and feel first-hand what I mean by coding qualitative data being an iterative and pattern-finding process. If you want to get the full experience, follow my guidelines and do it seriously (no cheating!).
​
​The exercise goes as follows. Let's say that you have conducted a series of interviews to answer the following research question: "How do university teachers experience funny cat videos on the Internet?" and that the first interview you have transcribed goes like this:

​Excerpt from Interview 1:
​​"The thing I really like about kitties is that you can cuddle them. I don’t have kids so basically my cats… they are my best friends. So when I watch them online it just reminds me of that feeling. I love it."

​The exercise goes as follows:
Task 1: Write a few codes for this excerpt (3-5). Work as if you are still in the process of constructing a research question and you are inductively exploring your data to figure out what you can make out of it. While you are coding, think about the kind of things you find relevant and interesting in the transcript. Don't look at the other excerpts yet (no cheating!).

Once you are done with Task 1, let's expand your material by adding excerpts from the transcripts of two other interviews, the interviews with Interviewer 2 and Interviewer 3. This is basically what happens when you start coding you material, you read a first interview and start coding it, not really knowing where it is going, and then you keep on by coding material from other sources.

Task 2: Code the excerpts from Interview 2 and Interview 3. As for the first excerpt, pick a few codes for each excerpt. At this stage, don't touch the codes you have created for the first excerpt (no cheating, again!).

Have you ever heard about the idiom 'Missing the forest for the trees'? This expression refers to a situation when someone is too involved in the details of a problem to look at the situation as a whole... And this is what happens when you start asking yourself questions like "is it ok if I turn my code into a theme?", "is it normal that I start changing the labels of my codes after having constructed my themes?" etc. Remember that coding is only a mean to an end, the end is for you to produce knowledge as interesting and rigorous as possible! Don't lose the big picture of what your objective is (=producing a good analysis) and the nature of the process that is taking you there (=a pattern-finding iterative process). Once you are clear about these points, I believe that all the questions you ask yourself can be better answered by asking yourself the two following questions:

• Among the options available to me, what is the one that enables me to produce the best analysis?
• Does what I am doing look like a pattern-finding iterative process? 

If you are stuck or in case of doubt, try to substitute the questions you are asking yourself with these two to see what happens. In many cases, I bet it will help you move forward and take your analysis to the next level. 

I hope this blog post has helped you unleash the coding force within you and that you are now ready to show the world your full coding potential 😉​.

References
​Gibbs, G. R. (2007) Qualitative Research kit: Analyzing qualitative data. London, England: SAGE Publications Ltd. ​
Saldana, J. (2009) The coding manual for qualitative researchers. Los Angeles, CA: SAGE.

As a student, have you ever been told that your work was “too descriptive” or that you needed to be “more analytical”? 

​Analysis is a process of transformation. Raw data and information do not have meaning per se. It is you, as researcher, who make meaning out of it, via the process of analysis.
Analysis is a creative process. What you create through analysis is a new discourse about the world, which helps people perceive the world in a different way, understand things that they did not know or did not understand before reading your research.
 
But how does it work? How does analysis make sense of the world, then?
To summarise: by bringing focus, synthesising, naming, establishing patterns and relationships that will help other people in perceiving these patterns and relationships and thus understand the world differently.
 
A good metaphor for analysis is the work of astronomers and other stargazing lovers who identified constellations. The sky is full of stars. And some people drew patterns in the sky, named these patterns and even created stories about the shapes thus formed. This way of perceiving the sky is passed down from generations to generations, taught and written in books. And people who learned them can’t help but seeing these patterns when they look at the stars.

Here are two definitions of analysis taken from methodology handbooks:
 
“Taken literally, ‘breaking up’ something complex into smaller parts and explaining the whole in terms of the properties of, and relations between, these parts” (Robson 2011: 412)
 
“The process of bringing order to the data, organizing what is there into patterns, categories and descriptive units, and looking for relationships between them; ‘interpretation’ involves attaching meaning and significance to the analysis, explaining the patterns, categories and relationships…” (Brewer 2000: 105)
 
These definitions highlight the double process involved in analytical work:
- on the one hand, the breaking down and simplification of the inherently messy social world;
- on the other hand, the building up of patterns between selected elements to produce a new (and synthetic) way of interpreting the world.
 
In practical terms, the transcription of one hour of interview can take up to 40 pages. Thus, the transcription of 10 interviews amounts to 400 pages. You need to break down your material into manageable segments, and focus on certain elements and sacrifice others, in order to find out what is the most interesting knowledge you can produce out of this data. 

Listing explicit criteria about what we need to do to produce a good analysis can help us ensure we are on tracks regarding our analytical goals.

​To sum it up:  the objective of analysis is to produce a convincing demonstration based on empirical evidence describing, explaining and interpreting a social phenomenon.
​

In the schematic ‘wheel’ below, I unpack the definition above to make explicit a series of criteria to help you produce a good analysis:

Let’s unpack further these criteria:

• Description: What is going on? Make sure you are not jumping to conclusions before describing the evidence.
• Explanation and Interpretation: The description of the empirical material is a necessary but not sufficient dimension of analytical work. The readers need you to go one step further. Depending on your research question, it might be, for example, the reasons behind the situation, the conditions of their emergence, how actors make meaning of it in context etc… You need to go beyond simply describing what is in the data and synthesise the way you interpret and explain the situation under scrutiny by linking your material to the context, existing theory and literature.
• Specific research object: Provide an answer to the research question by selecting relevant material to analyse. ONLY keep in the final project the data that is relevant for your analysis.  You have a purpose and you need to stick to it. There will be things in your data that are interesting but that don’t directly answer your research question: you have to be ruthless and sacrifice them. Pasta is very good but if you’re baking a cake, you don’t put the pasta in the cake, otherwise you miss the point by spoiling the cake. To summarise: there may be things in your data that are very interesting, but if they are outside the scope of your research project: bye bye.
• Discourse for someone else: The analysis is not a summary of your data that you write for yourself. The analysis is something you communicate to someone else. It needs to be clear and engaging. It is an argumentative exercise. You need to convince the reader. Imagine you are trying to convince someone you know when you write.
• Demonstration:​ A very good way of producing a convincing analysis is to demonstrate the rigour of your analysis. Create something that is more than generating impressions. You need to go beyond anecdotal examples and cherry-picked cases. Aim for your demonstration to be transparent and traceable so the readers can understand how you reached your interpretation. Provide a clearly articulated account of your assumptions, procedures, and steps. Aim for your demonstration to be systematic. Develop analytical strategies and procedures with steps to be followed consistently. That means that you approach all your data (all your interview transcripts, for example) with the same framework of analysis and the same questions.

 
Each of us has natural strengths and weaknesses. For example, some of us will have no problem in expressing their argument with clarity but will struggle to establish clear boundaries to their research topic. Others will provide a rich interpretation but without demonstrating how they reached these conclusions. It is important to identify which dimensions of analysis are your weaknesses or which ones you usually tend to neglect, and work on them as a priority.