UBelong Quantitative Research Associate

We are looking for a passionate and enthusiastic post-doctoral researcher to join our MRC funded team investigating loneliness in university students. We are aiming to develop concepts to understand loneliness in university students and associated measures to enable effective quantification of loneliness.   

Levels of loneliness appear to be on the rise among university students, leaving many puzzled. University students are surrounded by peers, often live with friends and have many opportunities to socialise. Why would they feel lonely? At the moment, most studies addressing loneliness often rely on a single question; “how often do you feel lonely?” Through this project we are looking to improve the sensitivity of our measures of loneliness.  

This is an interdisciplinary collaborative project. You will be working with a team including quantitative and qualitative researchers and a historian. We will be using egocentric social network data to assess how changes in social network through the transition to university impact loneliness, working with students through co-creation activities to ensure their voice is represented across all our work, developing a new psychometric scale to assess student expectation for social connection and running a large-scale longitudinal survey to understand individual differences in loneliness.  

The successful candidate will be responsible for managing all elements of the project related to quantitative data collection and analysis, including student co-creation, recruitment for our large scale survey, managing and storing data responsibly and complex analysis including social network analysis and linear mixed models.  

The role will be based at King’s College London, within the Uni-SMaRt team, and managed by Dr Nicola Byrom.  

This post will be offered on an a fixed-term contract for 34 months 

This is a full-time  post

• Develop and validate a new psychometric scale to assess student expectations for social connection.   
• Run a large-scale longitudinal survey of loneliness in university students, including designing the survey, implementing this within Qualtrics, recruiting for the survey, managing attrition from the survey, cleaning and storing the data appropriately, analysing the data using linear mixed models.  
• Under the supervision of a collaborator with expertise in social network analysis, complete analysis of egocentric social network data.  
• Work with students as partners, including support co-creation activities, ensuring that student voice is consistently represented across the project, and supervising student projects. It is essential that the student voice is heard and the project requires active student involvement and partnership in shaping the research design, content and messaging. This will involve recruiting, training and supporting student partners. 
• Support the wider project team in their development of a new conceptual model of loneliness in students.     
• Communicate complex and conceptual ideas to those with limited knowledge as well as peers using high level skills using a range of media, including: writing and submitting academic publications; disseminating findings using other appropriate media; present research at conferences. 
• Coordinate and develop project relationships, ensuring effective communication with and between all collaborating partners and stakeholders.  
• At the direction of the PI, plan, co-ordinate and implement the research project activity including managing the use of research resources and ensuring that effective use is made of them; monitoring and reporting on the use of research budgets.  
• Support the further development of research in this area, identifying sources of funding, developing research proposals and contributing to the process of securing funds. 

The above list of responsibilities may not be exhaustive, and the post holder will be required to undertake such tasks and responsibilities as may reasonably be expected within the scope and grading of the post.  

Essential criteria  

Desirable criteria