The prolonged global financial crisis has questioned the appropriateness of the previous use of statistical theory and methods for understanding the causes and impact on public policy. Central to this critique is the idea that the relationship between cases (consumers, organisations, nation states) is highly dynamic and interactive and subject to constant changes in interactions that results in unpredictable and unknowable futures.
Nevertheless, new approaches originating from complexity theory and critical realism propose that certain outcomes are more likely than others are, and that longitudinal patterns can be detected amongst highly complex case interactions. One new method emerging for examining these patterns in complex social systems is Dynamic Pattern Synthesis (DPS). This method combines cluster analysis and Qualitative Comparative Analysis (QCA) with small samples of cases to identify longitudinal patterns.
The approach identifies:
1. stable clusters of similar countries that remain similar despite social and economic change,
2. countries that move clusters over time, and
3. outliers (countries that are unusual and/or exceptional in their trajectory).
The exploration of variable relationships with these case patterns is revealing in that major changes in variable trends do not necessarily disrupt case based patterns. For example, first use of the method to compare European nations’ economies within the Eurozone, and their typology of welfare states, has revealed the tendency for country clusters to largely remain, despite crisis driven change, and for case differences across the sample to be more likely to diverge than converge (Haynes, 2014, 2015, 2016).
Dynamic Pattern Synthesis is contributing new and important approaches to understanding policy convergence and divergence.
Dynamic Pattern Synthesis is an innovative mixed method, first presented to the academic community at the ESRC complex methods seminar series at Warwick University in February 2015, where it was well received. It is a novel approach to researching complexity in meso and macro case patterns. It was recently presented by invitation to the ESRC CECAN programme network at Whitehall in April 2017.
The method is being published this summer in a book entitled; Social Synthesis: Finding Dynamic Patterns in Complex Social Systems Oxon: Routledge (ISBN 9781138208728). Series Editors: Profs. David Byrne, University of Durham; Brian Castellani, Kent State University, US; Emma Uprichard, University of Warwick. The development of DPS has been supported by Prof. Nigel Gilbert, ESRC CECAN programme director at Surrey University. The student will use the ESRC data service to access appropriate international datasets (ie: WB, IMF, OECD, WHO etc)
The selected student will need at least a 2:1 undergraduate honours degree or master’s degree that includes a robust achievement in quantitative methods, especially with regard to multivariate analysis.
Previous use of cluster and analysis and/or QCA is advantageous, although training can be provided in these methods in the first semester if necessary.