Once a month we discuss a particular research method that may be of interest to people working in health economics. We’ll consider widely used key methodologies, as well as more novel approaches. Our reviews are not designed to be comprehensive but provide an introduction to the method, its underlying principles, some applied examples, and where to find out more. If you’d like to write a post for this series, get in touch. This month’s method is coding qualitative data.
Health economists are increasingly stepping away from quantitative datasets and conducting interviews and focus groups, as well as collecting free text responses. Good qualitative analysis requires thought and rigour. In this blog post, I focus on coding of textual data – a fundamental part of analysis in nearly all qualitative studies. Many textbooks deal with this in detail. I have drawn on three in particular in this blog post (and my research): Coast (2017), Miles and Huberman (1994), and Ritchie and Lewis (2003).
Coding involves tagging segments of the text with salient words or short phrases. This assists the researcher with retrieving the data for further analysis and is, in itself, the first stage of analysing the data. Ultimately, the codes will feed into the final themes or model resulting from the research. So the codes – and the way they are applied – are important!
There is no ‘right way’ to code. However, I have increasingly found it useful to think of two phases of coding. First, ‘open coding’, which refers to the initial exploratory process of identifying pertinent phrases and concepts in the data. Second, formal or ‘axial’ coding, involving the application of a clear, pre-specified coding framework consistently across the source material.
Any qualitative analysis should start with the researcher being very familiar with both the source material (such as interview transcripts) and the study objectives. This sounds obvious, but it is easy, as a researcher, to get drawn into the narrative of an interview and forget what exactly you are trying to get out of the research and, by extension, the coding. Open coding requires the researcher to go through the text, carefully, line-by-line, tagging segments with a code to denote its meaning. It is important to be inquisitive. What is being said? Does this relate to the research question and, if so, how?
Take, for example, the excerpt below from a speech by the Secretary of State for Health, Jeremy Hunt, on safety and efficiency in the NHS in 2015:
Let’s look at those challenges. And I think we have good news and bad news. If I start with the bad news it is that we face a triple whammy of huge financial pressures because of the deficit that we know we have to tackle as a country, of the ageing population that will mean we have a million more over 70s by 2020, and also of rising consumer expectations, the incredible excitement that people feel when they read about immunotherapy in the newspapers that gives a heart attack to me and Simon Stevens but is very very exciting for the country. The desire for 24/7 access to healthcare. These are expectations that we have to recognise in the NHS but all of these add to a massive pressure on the system.
This excerpt may be analysed, for example, as part of a study into demand pressures on the NHS. And, in this case, codes such as “ageing population” “consumer expectations” “immunotherapy” “24/7 access to healthcare” might initially be identified. However, if the study was investigating the nature of ministerial responsibility for the NHS, one might pull out very different codes, such as “tackle as a country”, “public demands vs. government stewardship” and “minister – chief exec shared responsibility”.
Codes can be anything – attitudes, behaviours, viewpoints – so long as they relate to the research question. It is very useful to get (at least) one other person to also code some of the same source material. Comparing codes will provide new ideas for the coding framework, a different perspective of the meaning of the source material and a check that key sections of the source material have not been missed. Researchers shouldn’t aim to code all (or even most) of the text of a transcript – there is always some redundancy. And, in general, initial codes should be as close to the source text as possible – some interpretation is fine but it is important to not get too abstract too quickly!
Formal or ‘axial’ coding
When the researcher has an initial list of codes, it is a good time to develop a formal coding framework. The aim here is to devise an index of some sort to tag all the data in a logical, systematic and comprehensive way, and in a way that will be useful for further analysis.
One way to start is to chart how the initial codes can be grouped and relate to one another. For example, in analysing NHS demand pressures, a researcher may group “immunotherapy” with other medical innovations mentioned elsewhere in the study. It’s important to avoid having many disconnected codes, and at this stage, many codes will be changed, subdivided, or combined. Much like an index, the resulting codes could be organised into loose chapters (or themes) such as “1. Consumer expectations”, “2. Access” and/or there might be a hierarchical relationship between codes, for example, with codes relating to national and local demand pressures. A proper axial coding framework has categories and sub-categories of codes with interdependencies formally specified.
There is no right number of codes. There could be as few as 10, or as many as 50, or more. It is crucial however that the list of codes are logically organised (not alphabetically listed) and sufficiently concise, so that the researcher can hold them in their head while coding transcripts. Alongside the coding framework itself – which may only be a page – it can be very helpful to put together an explanatory document with more detail on the meaning of each code and possibly some examples.
Once the formal coding framework is finalised it can be applied to the source material. I find this a good stage to use software like Nvivo. While coding in Nvivo takes a similar amount of time to paper-based methods, it can help speed up the process of retrieving and comparing segments of the text later on. Other software packages are available and some researchers prefer to use computer packages earlier in the process or not all – it is a personal choice.
Again, it is a good idea to involve at least one other person. One possibility is for two researchers to apply the framework separately and code the first, say 5 pages of a transcript. Reliability between coders can then be compared, with any discrepancies discussed and used to adjust the coding framework accordingly. The researchers could then repeat the process. Once reliability is at an acceptable level, a researcher should be able to code the transcripts in a much more reproducible way.
Even at this stage, the formal coding framework does not need to be set in stone. If it is based on a subset of interviews, new issues are likely to emerge in subsequent transcripts and these may need to be incorporated. Additionally, analyses may be conducted with sub-samples of participants or the analysis may move from more descriptive to explanatory work, and therefore the coding needs may change.
Published qualitative studies will often mention that transcript data were coded, with few details to discern how this was done. In the study I worked on to develop the ICECAP-A capability measure, we coded to identify influences on quality of life in the first batch of interviews and dimensions of quality of life in later batches of interviews. A recent study into disinvestment decisions highlights how a second rater can be used in coding. Reporting guidelines for qualitative research papers highlight three important items related to coding – number of coders, description of the coding tree (framework), and derivation of the themes – that ought to be included in study write-ups.
Coding qualitative data can feel quite laborious. However, the real benefit of a well organised coding framework comes when reconstituting transcript data under common codes or themes. Codes that relate clearly to the research question, and one another, allow the researcher to reorganise the data with real purpose. Juxtaposing previously unrelated text and quotes sparks the discovery of exciting new links in the data. In turn, this spawns the interpretative work that is the fundamental value of the qualitative analysis. In economics parlance, good coding can improve both the efficiency of retrieving text for analysis and the quality of the analytical output itself.