Jo-ann Larkins describes her role as a research enabler as the quiet cog in the research process, using her expertise in statistics to help researchers get the most from their project while allowing them to avoid ‘black holes’ they may encounter along the way.
The multidisciplinary researcher trained in applied statistics as part of a mathematics degree and has a role with the Graduate Research School working with Higher Degree Research students. She also works with undergraduate students doing their first projects in science as well as assisting academics on research projects.
Ms Larkins, who is Scholarly Teaching Fellow in the School of Engineering, IT and Physical Sciences, said researchers often needed assistance to get the most out of their work.
“My role is a little bit behind the scenes and it's a little bit not seen because we're not necessarily the people who have our names attached to particular pieces of research, but it's an essential part of the process.”
This role of enabling others’ research typically begins with a critical conversation at the beginning of a research project.
“People tend to think that statistics is just about the data and they need help to analyse it. But it works best if you have a critical discussion with a statistician at the beginning of the process as you’re planning the project,” Ms Larkins said.
“So we sit down and we have a look at where the black holes might be — where the gaps are. We also have critical conversations about what they’re going to measure, and how, so what they measure is actually what they intended.
“Another part of the process is thinking about the analysis and collecting data in such a way to make it easier to analyse because you can do things that mean that you don't have enough data or you can do things that mean you have way too much data to be useful.”
The uniqueness of the role means Ms Larkins is also in demand from other institutions and researchers. Her involvement across different disciplines has seen her recent work published in fields such as nursing, rural and farm crime, in gut microbiome and antimicrobial resistance in birds.
“This work across social sciences, science and engineering and business — across all sorts of areas — means I will often have seen an approach being used in a different discipline that might work with another dataset,” Ms Larkins said.
“I’m a scientist but I didn't study biology, chemistry or environmental science. But for many years I taught a science communication subject that meant every semester I could go and learn something totally new because the students would write literature reviews that I would mark. I learned all sorts of things from drugs in sport to phytoremediation, which is where you use plants to draw polluting chemicals out of the soil.”
It’s this genuine curiosity about the natural world that Ms Larkins draws on to translate complex statistical analyses communicating clearly with both researchers and their audience.
In her own research, Ms Larkins explore authentic assessment and teaching strategies to reduce anxiety in mathematics and statistics including the use of student-constructed notes, or cheat sheets, in exams, and reflective testing.
Her role is educative, teaching people to do their own statistics — an upskilling role for researchers and students enabling them to analyse their data. She said this was much easier to do when a researcher had a dataset that they were passionate about.
“It's about really making researchers cast aside some preconceptions about how they're going to do their research and sometimes it’s just about ordering and logic because they've thought really deeply about it. On other occasions, it might mean a total rethink or a reappraisal of the best way to do it.” Jo-ann Larkins
“When you talk about research, we have had two broad branches of research, qualitative and quantitative research. Quantitative is about collecting things that are measured, qualitative is about that deep dive into experience — this might be interviews and focus groups.
“Increasingly we're seeing mixed methods research which is where we use a bit of both and so part of my role is to help the researcher decide whether it's purely qualitative, purely quantitative or some mix and how that mix ratio works. There was once a hard line down the middle when I started studying statistics but now we're finding aspects of all three approaches being used where it's appropriate, where it's the best way to answer the question.
“Roles like mine exist so we can produce the best quality research we can.”