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Meet the Team

Professor Vernon Gayle – Director

Professor Vernon Gayle

Vernon is Professor of Sociology and Social Statistics at the University of Edinburgh and is an international expert in the field of longitudinal data analysis. He has taught extensively in this area, delivering training and capacity building activities that have been funded by the ESRC Research Methods Programme, the ESRC Researcher Development Initiative, the ESRC National Centre for Research Methods, phases I and II of AQMEN which were funded by the ESRC to build capacity in quantitative methods in social science.

Stacey Johnstone – Events and Finance Officer

Stacey Johnstone

Stacey has been with AQMEN since 2013 when it was funded by the Economics and Research Council (ESRC) and Scottish Funding Council. Initially recruited as an administrative assistant, Stacey is currently the Events and Finance Officer for AQMEN and supports the Business Development Manager in the planning and delivery of all events.

Professor John MacInnes - Co-Director

John MacInnes

John is Associate Dean (Quantitative Methods) and Professor of Sociology at the University of Edinburgh, a Fellow of the Academy of Social Sciences and a Chartered Statistician. He is a member of the British Academy’s High Level Strategy group on Quantitative Skills, the advisory panel for Maths in Education and Industry and the Data Skills Taskforce. From 2009 to 2014 he was the Strategic Advisor to the Economic and Social Research Council on quantitative methods training, issuing two reports that eventually led to the Q-Step programme, since 2015 he has been Strategic Advisor on Quantitative Skills to the British Academy. Much of his current work focuses on statistical literacy and the promotion of quantitative methods within the social sciences. His own research focuses on population ageing and the measurement of age structures of populations.
 

Professor Susan McVie - Co-Director

McVieSusan is Chair of Quantitative Criminology within the School of Law. She has several major research roles within the School and plays a significant role with in the Scottish and UK research community. She is Director of the ESRC-funded Understanding Inequalities (UI) project which aims to create an innovative and ambitious programme of research on the causes, consequences and policy implications of social inequaltieis across different dimensions and spatial scales.  Susan is Co-Director of the Edinburgh Study of Youth Transitions and Crime, a prospective longitudinal study of youth offending based at the University of Edinburgh since 1998. She has responsibility for strategic management of the research programme and plays a key role in advancing statistical analysis of the data and publishing the results of the research.  She is also research leader for the crime and justice work package of the Administrative Data Research Centre for Scotland.  She is a member of the Management Committee for the Scottish Centre for Crime and Justice Research, another collaborative initiative involving Stirling, Glasgow, Strathclyde and Edinburgh Universities in partnership with the other Scottish HEIs.  Susan founded the Applied Quantitative Methods Network(AQMeN) in 2009 and was Director of a major programme of research and training until 2017.  She is currently Co-Director of AQMeN and is involved in developing a programme of training for business and industry.
 

Dr. Diarmuid McDonnell

Diarmuid McDonnell

Diarmuid McDonnell is a Research Fellow in the School of Social Policy, University of Birmingham. His research explores the determinants of nonprofit misconduct, accountability and vulnerability, using linked administrative data derived from charity regulators internationally. He is currently researching post-war organisational change in the UK voluntary sector. He has methodological interests in the use of administrative data for social science research, data science and policy evaluation methods. Diarmuid has designed and delivered workshops to staff and students on statistical literacy, and marshalling and analysing quantitative data using a range of statistical packages.