From the Classroom to the Workplace: How Social Science Students Are Learning to do Data Analysis for Real


Authors: 
Jackie Carter, Mark Brown, and Kathryn Simpson
Year: 
2017
URL: 
http://iase-web.org/documents/SERJ/SERJ16(1)_Carter.pdf
Abstract: 

In British social science degree programmes, methods courses have a bad press,
and statistics courses in particular are not well-liked by most students.

A nationally coordinated, strategic investment in quantitative skills training, Q-Step, is an attempt
to address the issues affecting the shortage of quantitatively trained humanities and
social science graduates. Pedagogic approaches to teaching statistics and data
analysis to social science students are starting to indicate positive outcomes. This
paper contributes to these debates by focusing on the perspective of the student
experience in different learning environments: first, we explain the approach taken at
the University of Manchester to teaching a core quantitative research methods
module for second-year sociology students; and second, we introduce case studies of
three undergraduates who took that training and went on to work as interns with
social research organisations, as part of a Manchester Q-Step Centre initiative to
take learning from the classroom into the workplace.

The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education