Developing a future pipeline of applied social researchers through experiential learning: The case of a data fellows programme

Abstract

This paper presents an innovative model for developing data and statistical literacy in the undergraduate population through an experiential learning model developed in the UK. The national Q-Step (Quantitative Step change) programme (2013–2021) aimed to (i) create a step change in teaching undergraduate social science students quantitative research skills, and (ii) develop a talent pipeline for future careers in applied social research. We focus on a model developed at the University of Manchester, which has created paid work placement projects in industry, for students to practise their data and statistical skills in the workplace. We call these students data fellows. Our findings have informed the development of the undergraduate curriculum and enabled reflection on the skills and software that we teach. Data fellows are graduating into careers in fields that would previously have been difficult to enter without a STEM (Science, Technology, Engineering and Mathematics) degree. 70% of data fellows to date are female, with 25% from disadvantaged backgrounds or under-represented groups. Hence the programme also addresses equality and diversity. The paper documents some of the successes and challenges of the programme and shares insight into non-STEM pipelines into social research careers that require data and statistical literacy, A major advantage of our approach is the development of hybrid data analysts, who are able to bring social science subject expertise to their research as well as data and statistical skills. Focusing on the value of experiential learning to develop quantitative research skills in professional environments, we provoke a discussion about how this activity could not only be sustained but also scaled up.