Becoming part of the dataset

“How do we strike a balance between teaching students to use research methods rigorously, while also stimulating them to think critically about the assumptions built into these techniques? Fiona Gogescu interviewed Jackie Carter about how she teaches quantitative research methods to social sciences students for a special issue in Postgraduate Pedagogies titled Conversations on teaching in the contemporary university: Perspectives from within and across disciplines The excerpt below features two passages from the interview – why Fiona chose to interview Jackie, and a lightning round series of questions and answers where Fiona quizzes her about teaching, dream courses, and invaluable resources Quantitative abilities have been identified as a skill shortage among OECD countries. In the UK, over the last 15 years, there has been a focus on developing the quantitative skills of social science graduates. The British Academy has called for a national strategy to tackle the quantitative skills deficit in the humanities and social science. In response, the Q-step programme was launched in 2013-14 in 17 universities in the UK. As co-director of the Q-step programme at the University of Manchester, Jackie pioneered an innovative Data Fellows programme, which enables students to develop as hybrid data analysts fluent in data skills and social science subject expertise. Her approach, grounded in the social science tradition, provides interesting insights into a question I find myself returning to: how do we strike a balance between students being critical of the research methods they’re applying and being rigorous with applying these methods? Jackie and I have both taught courses where statistics and quantitative methods are learned in connection with students posing a problem, designing their own small-scale research project, and interpreting the results. In this interview, we discuss the big issues, the day-to-day practicalities, and the learning and reflection that occurs when students become “a part of the dataset” and analyse topics important to them. Lightning round Fiona: What is one idea every student on your course should come away knowing? Jackie: To be critical of the data behind the analysis. Fiona: Name one concept covered in this course that most students struggle with. Jackie: That the answer has uncertainty associated with it – and how to communicate this. Fiona: Name one topic on this course that you really enjoy teaching. Jackie: How to measure and compare inequalities. Fiona: What is one idea you’d like readers to take away from this interview? Jackie: That’s easy – that social science and humanities students make great data analysts, but the world of employment needs to wake up to this. Fiona: Recommend a blogpost/podcast/op-ed that a novice/stranger to this field should read and will understand. Jackie: More or Less podcast (BBC Radio 4) and Hans Rosling’s Factfulness Fiona: What would be your dream/ideal course to teach? Jackie: Taking Hans Rosling’s book as a guide I would teach a 10-week course on factfulness. Fiona: As a teacher, what do you find the hardest? Jackie: Persuading students that they can do numbers – even if they dropped maths at high school. Fiona: Recommend an invaluable resource to your fellow teachers in this field. Jackie: Can I recommend my own book? Work placements, internships and applied social research Read the interview in full in Postgraduate Pedagogies who have kindly permitted us to re-publish this excerpt. This post is opinion-based and does not reflect the views of the London School of Economics and Political Science or any of its constituent departments and divisions. Main image: Booth poverty map of 1898-99. Courtesy LSE Library The post Becoming part of the dataset first appeared on LSE Higher Education .

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