June 16, 2024
Blog / PhD

Coding and Analysis

I attended a workshop today on ” A Crash Course in Coding and Analysis ” by Dr Richard Budd

My Key Takeaways

It is all about reduction key features include looking for commonalities exceptions tensions disagreements. You need to boil it down and document it as you go. It is a slow iterative process with two steps forward and one step back .

  • You need to immerse yourself in the data.
  • a deductive approach is more top down and hypothesis testing with quantitative measurement of outcome whereas an inductive approach is more qualitative and his bottom up so data driven or theory generated
  • Develop basic codes extend those codes and identify what people are saying.
  • You are using big codes to start with and then putting text in two initial coding buckets eg likes and dislikes.
  • When you are reducing the data it is a bit like making jam you have lots of fruit but end up with not much jam.
  • When you are deciding on your analytical framework be systematic transparent fair and representative and don’t cherry pick
  • advantages of using software to help code are it can help you to organise store your codes backtrack run reports and facilitates collaborative working with shared access. The disadvantages of using software is that it can be a steep learning curve it can cost money together licence and sometimes it can distance you from the data.
  • Key questions to ask include
    • what stands out what is common or unusual what are tensions or disagreements ?
    • what answers your research question ?
    • what corroborates extends or refutes previous scholarship ?

Break Out Rooms

  • We looked at two different types of data in the breakout group exercises
  • One looked at diversity in university and lived experience of students in relation to the diversity of staff. Where we identified top level codes of race gender age and being in a bubble.
  • The second exercise we looked at was in relation to UK higher education institutions negotiation of social science doctoral policy spaces where we coded these into categories and looked for pattern. The framework that Richard used for this was a neo institutional isomorphism framework where aspects were categorised into coercion mimesis normativity and rationalisation.


I found this quite helpful and I pretty much understood what Richard was saying. It was quite difficult jumping into the breakout sessions from a cold start as it were as these weren’t really facilitated etc. But we still manged to have a few useful discussions. I found the sound was dipping a bit which was a bit challenging at times though. I also spotted a few familiar faces at the workshop but didn’t get chance to say hello (Gabi and Nicola I think were there.

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