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:

• Importing the data set from SPSS
• Checking data structure
• Generating summaries by groups
• Simple ggplots
• Measures of central tendency
• Correlation
• Chi-sq
• Tests for normality and homogeneity of variance
• T-tests and OLS.
• Logistic regression

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:

• Start with the most basic concepts to keep students interested and to not scare them off. This also helps in keeping the class lively, as everyone can contribute to the discussion.
• Keep it simple. Find the easiest methods of doing things that don’t involve multiple steps, so participants can easily recall and apply the steps again.
• Assume the students do not have any theory background in the topic of interest. This enables the facilitator and participants to dive deeper into different concepts and permits the students to relate the theory to practice easier.
• Resist from overloading the attendees with too much content in a very short space of time to allow them to reflect and internalise the information in small, bitesize chunks.
• Keep asking simple insightful questions throughout the training sessions to encourage active participation and regularly refresh memory.
• Try not to leave any of the participants behind. Follow up with each individual and check how much progress they are making.
• Encourage the participants to use the various sources of help available e.g. from their peers or Google, so that even after the training, they can still get help on their own to achieve certain tasks.
• Put yourself in their shoes. Don’t get carried away with how much you know and how easy it is for you to do all that the participants are doing, but within a shorter space of time.
• Patience is key. Some participants will pick up the concepts very quickly, whereas others will find it much harder. Keep encouraging the slow learners so that they too achieve the set objectives.
• Keep an open mind and allow new ideas on how the training can flow and change. Sometimes you might have to change the focus from what you had planned earlier in order to make the training more fun and effective.
• Share your first experiences with the tools so participants feel motivated and understand that the process they are going through is not any different to what someone else learning statistical modelling with R would go through.
• Lower your expectations on what you think the participants already know.

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!