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Statistical modelling with R: Training Tips on how to keep Participants Engaged

Since 2014, I have been providing Research Methods Support to McKnight Foundation funded projects in Eastern Africa. Currently, I am based in Uganda and sit within Makerere University (Kampala), where 40% of my time is allocated to work with students within the University. Last summer, I was requested to take on an additional task that involved taking PhD students (Agriculture and Rural Innovations) through practical sessions on statistical modelling with R. This struck me as both an exciting and challenging task, and I set to work by discussing with my supervisor and colleagues on how best to approach the training.


The view from outside Makerere University, Kampala in Uganda

Since my requests for a data set to use during the session did not yield any results, I decided to use some of the experimental data sets from Stats4SD that are used for introducing project teams to R. During the first training session, it was clear that the students (or the course they were undertaking) were mostly prepared for using survey study designs, and that most of the experimental jargon was new to them. By the end of this initial session, it was clear that working with experimental data was not the way to go and I needed to change strategy. The next option was to request a data set from the students that they could all relate to. Unfortunately, the students had not yet collected any data and we could therefore not pursue this option. So finally, I went for the third option that involved using an old US Employee data set. With this, the students could easily relate to what they had covered in the theoretical part of the class - and were able to actively participate.

During the week of training, the following topics were covered:

  

I made various observations across the week, including how students knew the different analysis that they could run by name, however, they didn't pay as much attention to what analysis to run and why/when. Additionally, I noted that there was little emphasis put on linking objectives to the overall analysis done – the students were more interested in how to run a certain test - irrespective of whether it would yield insightful results relating to the set objectives, or not. Overall though, the students were excited about using R, which gave them the opportunity to keep the analysis scripts as well as notes as they worked along.

Throughout the training, I realised some tips on best practise and how to make future trainings of this type more engaging for participants – and these are shared below:


For me, this training experience was very useful as I picked up many tips to apply for next time. I really hope that some of these pointers help inspire your sessions. If you have any further tips to add, or remarks to make – please do so in the comments box below!  

Shiphar Mulumba
Author: Shiphar Mulumba

Shiphar is a Research Methods specialist for Stats4SD in Uganda, working with the National SP Program and previously with CCRP. Her aims are to make research for development more effective through improved methods and the implementation of training programmes for scientists and students.

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