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  • 2017GortonPhD

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Getting to know QM(s): exploring the actor-networks of quantitative methods across higher education social science subjects

Research output: ThesisDoctoral Thesis

Published
Publication date2017
Number of pages241
QualificationPhD
Awarding Institution
Supervisors/Advisors
Thesis sponsors
  • ESRC
Publisher
  • Lancaster University
Original languageEnglish

Abstract

In the UK, the need for more quantitatively-skilled citizens and employees has been widely publicised. This skills deficit has prompted a wide range of policy initiatives and academic research into quantitative methods (QM(s)) learning-teaching across all levels of education. Although the academic literature has provided useful insights into the learning-teaching of QM(s), it has overlooked key questions concerning the character of QM(s) across Social Science disciplines and the role of non-human actors.

This thesis begins to fill this gap in the literature by adopting Actor-Network Theory (ANT) to explore the learning-teaching of QM(s) within four Higher Education Social Science subjects. To investigate the actor-networks that QM(s) is comprised of, and located within, an assemblage of methods was used, including: semi-structured interviews, concept mapping, participant observation and document analysis. Together, these methods capture QM(s) across Harvey’s (2004) three spaces (abstract, relative, and relational), supplementing ANT’s own relational understanding of space(-time).

Challenging the passive and singular framings of QM(s), presented within policy initiatives and the literature, here, QM(s) was found to be a character occupying multiple positions of agency, taught content, and locations on participants’ concept maps. Within the teaching-learning environments, the construction of QM(s) as linear, fixed and learnt through doing was translated by worksheets and correct answers, producing a characterisation of QM(s) as a passive, linear activity of completing tests. When placed within disciplinary actor-networks, QM(s) was identified as performing a variety of roles: providing patterns/trends; offering reliable answers and predictions; aiding theory testing; and assisting decision-making. However, these positionings were being challenged by new techniques, software, and learning-teaching environments. These findings imply that instead of a focus on differentiating QM(s) knowledge, to successfully integrate QM(s) with disciplinary knowledges attention should be given to QM(s’) link to data and theoretical positionings.

Overall, this thesis provides an original contribution to knowledge through its adoption of ANT, a theory not before applied to QM(s) learning-teaching research. In doing this, it challenges common assumptions made within the literature to provide new insights into the character of QM(s) and the role of previously overshadowed non-human actors.