Qualitative Data Analysis – Analysis Is More Than Coding

“Coding means that we attach labels to segments of data that depict what each segment is about. Through coding, we raise analytic questions about our data from […]. Coding distils data, sorts them, and gives us an analytic handle for making comparisons with other segments of data.”

Charmaz, 2014:4

“Coding is the strategy that moves data from diffuse and messy text to organized ideas about what is going on.”

Richards and Morse, 2013:167

Data Analysis – Analyzing Data in Qualitative Research

Computer-aided qualitative data analysis consists of various consecutive phases, which are on the most general level: preparing data and creating a project file, coding the data, using the software to sort and structure the data, and querying the data with the aim of discovering patterns and relations.

Coding in computer-assisted analysis means assigning a label to a data segment. A better-known term these days is tagging. The goal of tagging is to find the things you tagged using the tag name. The software uses the words ‘code’ and ‘coding’, as almost all Computer-Aided Qualitative Data AnalysiS software does as well. If you have a better idea of what tagging is, in the following simply replace the terms code and coding in your mind with tag and tagging. The data segment that you can code (or tag) can be as small as one character in a text document, a few pixels in an image file, or less than a second in an audio or video file.

A code can be a simple description, a concept, a category, a subcategory, or a wildcard that modifies a link in a network. The software itself does not dictate how to use a code. It only provides this entity as an item in the toolbox. Its logic however is built around coding. None of the analysis tools for querying the data can be used without the user having coded the data. In coding the data, you describe what is in the data. These might be people, artefacts, organizations, emotions, attitudes, actions, strategies, consequences of actions, contextual factors, and the like.

Depending on the chosen methodological approach, this may mean that you are tagging the data at a nominal level or that you are developing code labels based on a more detailed interpretation of data segments. Once the data is coded and a code system is developed, it can be interrogated. Analysis can be divided in two phases:

Phase 1: Description of the data – creation of a code system

The aim of descriptive-level analysis is to explore the data, to read or to look through them, and to notice interesting things that you begin to collect during first-stage coding. This needs reading transcripts, field notes, documents, reports, newspaper articles, etc., or viewing video material or images, or listening to audio files. Generating word clouds and word lists may also be a starting point when you have lots of data. To capture the interesting things that you notice, you may write down notes, mark the segments you find interesting, write comments, or as is most common attach labels (=coding). These labels are referred to as “codes” in the software more for historic reasons.

At this point in the analysis process, the labels can be descriptive or already conceptual, lower or higher-order. Developing codes and a code system is a process and the labels you create at this stage of the analysis process are likely to change. Thus, you do not have to worry too much whether a label is right or wrong. Reading further, you will very likely notice a few things that are like others you have noticed before. If they fit under a label that you already have, you apply it again. If an issue is similar but does not quite fit a tag that you already have, renaming it may allow you to subsume the data segments.

The labels do not have to be perfect yet. You can continue to collect more similar data segments and later, when you review them, it will be easier to think of better and more fitting labels to cover the substance of the material you have collected. The intellectual work that you do at this stage is the same as described in the past for manual ways of analysis. As Strauss and Corbin wrote in 1998:

As the researcher moves along with analysis, each incident in the data is compared with other incidents for similarities and differences. Incidents found to be conceptually similar are grouped together under a higher-level descriptive concept. (p. 73)

The initial process of collecting interesting things (i.e. coding) can be manifold depending on the underlying research questions, research aim and overall methodology you are using. The things you collect in your data may include themes, emotions, and values at the same time. You can code the data using deductively derived codes as in provisional coding; or you can develop codes inductively (e.g., initial or open coding), or also abductively, which is often the case when developing categories.
The software does not explain it; it just offers functions to create new codes, to delete, to rename or to merge them. The metaphor of collecting helps to understand better that a properly developed code is more than just a descriptive label for a data segment and that it does not make sense to attach a new label to everything one notices.

The aim of the first phase of coding is to develop a code list that describes the issues / aspects / phenomena / themes that are in the data, naming them and trying to make sense of them in terms of similarities and differences. This results in a structured code list which you can apply to the rest of the data during second-stage coding. Very likely the code list will need to be refined further and there will be a few more cycles of noticing and collecting until all the data are coded and the coding schema is fully developed. In parallel you can comment on data segments and begin to write memos.

Phase 2: Querying data – finding answers – identifying relationships

At some point, all data is coded, and you can enter the next phase of analysis. So far you have been working at the data level. The aim now is to look at the data from a different angle, the perspective of the research questions. Starting from one of your questions, you begin to query the data based on your coding. CAQDAS like ATLAS.ti offers a variety of tools for further analysis. The results of queries can be displayed in the form of numbers, the coded quotations or as a visualization. However, the actual analysis takes place during the writing process by summarizing and interpreting the results. For this the memo function can be used. While writing comments as well as memos, you move the analysis further step by step, dig deeper, look at details and begin to understand how it all fits together.

When beginning to see how it all fits together, visualization tools like the network function in ATLAS.ti can be used. Working with networks stimulates a different kind of thinking and allows further explorations. Networks can also be used as a means of talking with others about a finding or about an idea to be developed. Before you reach the last step of the analysis, several networks will probably have been drawn, redrawn, deleted and created anew. The aim is to integrate all the findings and to gain a coherent understanding of the phenomenon studied; or, if theory building was your aim, to visualize and to present a theoretical model.

References

  • Charmaz, Kathy (2014). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. London: Sage.
  • Friese, Susanne (2019). Qualitative data analysis with ATLAS.ti. London: Sage.
  • Richards, Lyn and Janice M. Morse (2013, 3ed). Readme first: for a user’s guide to Qualitative Methods. Los Angeles: Sage.
  • Strauss, Anselm L. and Corbin, Juliet (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2. edition). London: Sage.

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