CECAN Seminar - 3rd April 2017 - Professor Phil Haynes
BEIS, 1 Victoria Street, London 12.45pm - 2pm
Dynamic Pattern Synthesis (DPS) : A mixed method for exploring longitudinal patterns in social science data.
Dynamic Pattern Synthesis (DPS) is a new mixed method designed by Philip Haynes that seeks to maximise the advantages of Cluster Analysis and Qualitative Comparative Analysis (QCA) to search for dynamic patterns in data. First trials with the method have been with macro country comparisons, including a study of the first ten years of the Euro currency membership - seeking to understand the complexities of economic convergence and divergence. This also included comparing welfare state regimes from the first twelve Euro member countries.
These are turbulent times in which to advance sustainable development. Environmental and social challenges, encapsulated in the United Nation’s Global Goals, are as pressing as ever, but the political, regulatory and funding landscape is changing rapidly, creating new uncertainties and opportunities.
You are warmly invited to participate in a CECAN Webinar with Justin Jagosh (PhD) from the University of Liverpool.
"Realist Methodology for Complexity-Mindful Evaluations in the Food, Energy, Water and Climate Sectors".
14th March 2017 - 16.00 pm
Many approaches to addressing complexity in evaluation design are burgeoning. In part this is in response to the inadequacy of 'complexity-thin' approaches to supporting solutions to entrenched and evolving problems. A second factor is witnessing new complex problems emerging with radical changes in society and sectors. In this context, the realist paradigm serves as a guide to expand complexity-mindedness, which presumably translates into more useful and relevant knowledge products, and a community of realist evaluation specialists dedicated to developing a lens on 'context-mechanism' interactions. This webinar will introduce realist methodological principles, and explain how realism can increase our complexity-mindedness and capacity to gain causal insight.