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Why do causal mapping rather than ordinary QDA?

Updated: Nov 12, 2023



Question: I've got a load of texts to analyse, can I use Causal Map just for standard Qualitative Data Analysis, like ordinary thematic analysis, to identify important themes?


Our answer: That doesn't really work in Causal Map. Sorry!


Question: Why not? If you've got a good qualitative text analysis pipeline, why can't you generalise it?


Our answer:

  1. We put the question the other way round: why do thematic analysis (which is harder) when causal mapping can identify not only some of the important themes but also tell you how they influence one another?

  2. ... what you really want to do in the end, especially if you are doing evaluation, is find out what causes what in the eyes of your stakeholders. Identifying static themes can be interesting but often it's the causal information which helps you answer your main research and evaluation questions. Causal mapping is often a great way to cut to the chase.

  3. Thematic analysis is more of an art than a science. "What are the main themes here" is a very open-ended question which can (and should) be interpreted in different ways by different analysts ("positionality"). Whereas people (and GPT-4) tend understand the instruction "identify each and every section of text which says that one thing causally influences another" quickly and easily, and they tend to agree on how to apply the rule.

  4. The way we do causal mapping means identifying each and every causal connection. There is less room for someone's opinion in selecting what themes are most salient. Surprisingly, we can get good results without even a codebook of suggested themes aka causal factors, let alone bothering to train the AI or give it examples.

  5. This means that the steps from your initial research idea all the way up to (but not including) your final analyses can be quite easily automated in a transparent way. You can train an army of analysts to the coding for you manually, or you can press the AI button, or a combination of both, and either way you will get pretty similar results. There isn't so much room for the opinion of your analysts, whether human or robot, at any point in the pipeline.


Of course causal mapping is not free of bias (or positionality) due to human analysts' or AI-analysts' "world-views". It just leaves less room for those biases than more general thematic coding.


And of course, there are times when general thematic analysis or some other kind of QDA is really what you need. We're just saying that causal mapping might fit your need more often than you think.


Finally, you can in fact use Causal Map to identify some kinds of theme, and in particular when we are doing manual coding we sometimes code where causal factors are mentioned but without any specific cause or effect, like if someone just says "unemployment has gone up". But this isn't a main focus of our work.


And just to emphasise, causal mapping isn't proprietary. It's been around for 50 years. You can even do it (manually) in Excel.

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