John Collins, MZES: Prediction-based Adaptive Designs for Panel Surveys (PrADePS) (November 2024)

Foto von John Collins im Freien. Er hat lange rotblonde Haare und trägt eine Brille und ein grünes Hemd.

John ‘Jack’ Collins is a PhD Student in Sociology at the Graduate School of Economic and Social Sciences. He holds a Bachelor’s of Sociology with Honours from the Australian National University. Jack has a Master’s degree in Data Science from James Cook University. His Master’s project was regarding predictive modelling for student attrition from sub-tertiary courses in Australia. During his Master’s studies, he also assisted in research projects regarding social attitudes and voting behaviour in Australia. Before starting PhD, Jack was a Senior IT Consultant specialising in data engineering, analytics and software development. Jack is interested in applying Data Science and IT to sociological research, particularly with regard to machine learning, analytics, and web applications.

What is your current research topic?

I research how to use Machine Learning (ML) to predict who will nonrespond in longitudinal surveys.

For those who have not yet delved deeply into the topic of Data Science: How would you explain to a child what you are working on?

I think there are at least two kinds of data science: one is about the management of information, and the other is about discovering patterns in raw data. Almost every enterprise in the world needs to manage information, and data scientists are trained in processing data a little more deeply than computer scientists, which suits us well for tasks like designing databases or integrations between databases. The other part of data science is advanced data analysis: ML and AI are extremely complex topics, but enterprises desire simple answers derived from their data. Data scientists are therefore tasked with handling that complexity on their client/employer’s behalf – this requires the rare mixture of both technical skill and communication skills.

Everyone talks about Data Science – how would you describe the importance of the topic for yourself in three words?

Sophisticated Harnessing of Information.

What points of contact with Data Science does your work have? Which methods do you already use, and which would be interesting for you in the future?

Two parts: firstly, I preprocess raw survey data into a format that is suitable for machine learning. This is an example of data transformation, which is an essential part of data science: it is extremely easy to corrupt data as you transform it, and data scientists should be experts in avoiding those errors. Secondly, I execute ML models, which require deep knowledge of the process to properly troubleshoot and evaluate.

How high is the value of Data Science for your work? Would your research even be possible without Data Science?

My work requires an ML component, so it is essential.

What development opportunities do you see for the topic of Data Science in relation to your field?

Survey methodologists are already fast adopting data science techniques. However, there is always more to do: AI, particularly LLMs, is advancing fast, and the applications of that technology are yet to filter into this sector (as is the case in many sectors). I expect a wave of research on this as practitioners aim to find what works and what doesn’t in this space.

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