Student Research Internship – A Report
Final year student, Megan Irvine, gives a report of her Student Research Internship working with Dr Roula Nezi, building on the skills and knowledge she gained in the Research Methods Module.
I was approached by Dr Roula Nezi, one of my lecturers in the Politics Department, after I enjoyed her Research Methods module. She thought I would be a good fit for a research internship; and would enjoy working with her on her study of affective polarisation in Europe, and its impact on cultural issues. As someone who enjoys work and structure – I thought this would be the perfect way to get the best out of my summer and I have thoroughly enjoyed learning new skills, getting a taste for academia after undergraduate, and expanding my knowledge of my subject.
While affective polarisation has been extensively researched in the US (a clearly bi-partisan country), both for its political and cultural implications (Iyenger 2012, 2019), the cultural and ideological impact of the phenomenon has not been explored to the same extent in Europe.
Druckman (2021), Alwin (2016) and Jacoby (2014) have all concluded that polarisation can reinforce ideological differences on cultural issues such as abortion, immigration, or the death penalty. Considering Reiljan’s 2019 research establishing affective polarisation as prominent in European multiparty systems, the aim of the research was to further investigate the link between these concepts.
The 8-week internship began with a week of reading the seminal journal articles needed for me to have a good grasp on the concept of affective polarisation and the state of the literature available. Next, I gathered the significant number of datasets needed for the project. Once I had these, many of which required permission from researchers to use, I scoured them to find the cultural issues and like/dislike scales I needed for the dataset. Using R Studio, I then compiled the data and began to manipulate it to create one large dataset that we then used to find patterns and analyse the phenomenon. The data preparation took significantly longer than we anticipated, and what I spent most of my time doing, as each different country used many different levels of measurement which all had to be recoded. Finally, using Excel, I used Roula’s code to measure Affective Polarisation and added this to our data set.
Learning new skills:
As well as significantly improving my knowledge of statistical analysis and measurement in social sciences, I also developed skills I didn’t anticipate I would, such as language skills in French, German and even Swedish, to read the codebooks of each dataset. I also was able to purchase a subscription to Datacamp, allowing me to explore R Studio further. I have, importantly, gained experience in the field of research which I intent to take further, learning about the importance of Open Research, the need for open-source code and replicability within data analysis – all of which will be useful when applying for postgraduate academic positions.
Photo by Fabio Ballasina on Unsplash