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.
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.
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