Gibbs, Graham R. (2007) Mathematics and statistics skills in the Social Sciences: dealing with deficits. In: Addressing the Quantitative Skills Gap: Establishing and Sustaining Cross-Curricular Mathematical Support in Higher Education, 25th – 27th June 2007, University of St. Andrews, Scotland. (Unpublished)

The quantitative/numeracy skills issues for social scientists are somewhat different from those affecting many of the natural sciences and technology. In general students do not need a facility with abstractionism and symbolic systems (the equations) but they do need a good sense of number (scale, size etc) and an understanding of some of the logical principles and thinking that underlie mathematical proofs. The main area of application of these skills is in research methods and statistics.

Dealing with the quantitative part of research methods is always a struggle for teachers, but there is a range of different approaches that have been adopted: teach statistics with formulae; teach statistics using step-by-step instructions and teach statistics use without calculations and formulae.

QAA benchmarks, the BPS and the ESRC Training Guidelines for postgraduates are very clear about the importance of methods and statistics in the subjects. These guidelines will be outlined and their implications for student teaching and student recruitment examined.

Such numeric and statistical skills are important for work after graduation. A survey of recent graduates in sociology and psychology undertaken at Huddersfield in the 1990s showed that the topic from their degree that they found most valuable after graduating and in work was the research methods and statistics.

The paper will conclude with a look to the future, first in terms of possible impacts on the nature of the disciplines and secondly on some of the ways that students may be supported to overcome the hurdle of poor maths and statistics skills.

Gibbs_Math_in_SocSci_.ppt - Published Version
Restricted to Repository staff only
Available under License Creative Commons Attribution No Derivatives.

Download (926kB)
Gibbs_Math_in_SocSci_.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (1MB) | Preview


Downloads per month over past year

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email