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Reflections of my PhD research training

Image 1: A visual of my vision of the research cycle

PhD students are learning to do research and I am one of them. I am based in Tanzania, and I have been a part-time PhD student for two years now, at the University of Reading. My research is collaborative in nature and involves working with farmers. At the start of 2022, I decided to write what I have learnt about research so far.

My vision or theory of the research cycle, which begins with a research question followed by the methodology, is quickly becoming more of a myth to me as days go by. Well, at least up until now. It’s a myth because I am starting to understand that either I did not envision the process well enough to begin with, or that the practicality of the process was not emphasised enough, or that the saying ‘theory is different from practice’ is truer than I had imagined!

My experience as I have heard from other like-minded students is that the research cycle is an iterative process. Yes, you start with a question, then comes the method, but you then go back to the question - and then back to the method again. Additionally, in between the question-method loop, you need to take a critical look of what the method will give you regarding the question, and whether this will be enough. It could be the case that there are alternative methods that you can work with, given the timeframe and resources, that could provide more robust answers to the research question and the field at large. Then in the middle of this analysis, applicable to the problem-solving research, there comes the question of ‘what’s next?’ So, what if the answer is A or B? What will this tell us? What would come out of this question and method? Could we ask more, could we do more? Are we even in a position to do more? Can we adjust the research question to make it more robust, and an effective use of our time and resources?

In the middle of this thought process you may also wonder - what about the research participants? What about their time and the value they would find in engaging with the research? These include research subjects, technicians, the research assistants, and everyone taking part in the project. Is it a value they can see, touch or use? Is it a value they can realise immediately, or will it take a while for them to see and appreciate the value? I say ‘to them’, because as much as researchers want to contribute to knowledge to benefit further research, we ought to consider the value of the research to other people now, or at least in the short term, as we wait for others to pick up on that ‘shared knowledge’, and use it for its highest good. The research is a decent thing to do to, for obviously the research has value to us, otherwise we wouldn’t be conducting it. Be it for professional reasons, to graduate, contribute towards knowledge and publish work, to have fun, feel good about oneself for trying to make the world a better place - or because it is the only thing you can find to do. All of these are values we find in conducting research – and they matter to us! So does the research matter to others, or should we assume that it does?

Anyway, let me go back to my iterative process. Well, it is difficult for someone to describe the actual research cycle because of the number of iterations it may take to complete it, hence I can understand the simplified version I have always had in mind. And I think it is purposely simplified because the number of iterations could differ based on one’s experience or how well a question is framed¹. But what actually happens in the process? It involves getting a balance between being ambitious and realistic, between being daring and reasonable. Knowing what is fair to ask and try, and what isn’t fair to ask or try, given the circumstances. The circumstances being time, resources, value of the research, one’s expectations and those of their supervisors, and common graduate research norms! This is a balance that as a student I am being trained to understand and adapt to.

As someone with a maths background, I had been programmed to align with either ‘true’ or ‘false’ or ‘unknown’; or black, white or ‘no’ colour! In most cases, there are hardly any uncertainties in maths; something is either ‘true’, ‘false’ or ‘unknown’. There is no mixing between these values. Maths, as people put it, gives you something ‘as it is’. You can’t doubt maths, and if you do, it’s because it is not known. However, in this training, I am learning that it is OK to doubt, and it’s not necessarily because it’s unknown, but because you need to balance between the ‘true’, ‘false’ and the ‘unknown’. This balance is what I think people refer to as ‘research in practice’. Like maths, this ‘research in practice’ is intriguing as well scary, but I think this is what makes the whole training process more meaningful.

Reference

1. Coe, R. 2021. RMS Seminar - What is the question? Research questions and where they come from. Statistics for Sustainable Development. https://stats4sd.org/resources/547

Author: Nuru Kipato

Nuru is a mathematician and Research Methods specialist for SSD in Tanzania. She was a Research Methods Consultant and Junior Statistician at the African Maths Initiative in Maseno, Kenya.