Full Description
How is it possible to understand society and the problems it faces? What sense can be made of the behaviour of markets and government interventions? How can citizens understand the course that their lives take and the opportunities available to them?
There has been much debate surrounding what methodology and methods are appropriate for social science research. In a larger sense, there have been differences in quantitative and qualitative approaches and some attempts to combine them. In addition, there have also been questions of the influence of competing values on all social activities versus the need to find an objective understanding. Thus, this aptly named volume strives to develop new methods through the practice of 'social synthesis', describing a methodology that perceives societies and economies as manifestations of highly dynamic, interactive and emergent complex systems. Furthermore, helping us to understand that an analysis of parts alone does not always lead to an informed understanding, Haynes presents to the contemporary researcher an original tool called Dynamic Pattern Synthesis (DPS) - a rigorous method that informs us about how specific complex social and economic systems adapt over time.
A timely and significant monograph, Social Synthesis will appeal to advanced undergraduate and postgraduate students, research professionals and academic researchers informed by sociology, economics, politics, public policy, social policy and social psychology.
Contents
List of Boxes
List of Figures
List of Tables
Acknowledgements
Abbreviations
Introduction
Chapter One: Methodology: towards a representation of complex system dynamics
Introduction
Complexity Science
The classical reductionist method
Beyond reductionist science
Sensitivity to initial conditions
Emergence
Autopoiesis
Feedback
Networks
Summarising the influences of complexity theory
Understanding system change as patterns
Complexity in economic systems
Time and Space
Critical Realism
Case similarity and difference
Convergence and divergence
Complex causation
Methodological conclusions
Mixed methods
Conclusions
Chapter Two: the Method - introducing Dynamic Pattern Synthesis (DPS)
Introduction
Cluster Analysis (CA)
Cluster Analysis: specific approaches
Distance measures
Hierarchical and non-hierarchical cluster analysis
Clustering algorithms
Dendrogram charts
Icicle chart
Using SPSS to calculate and compare cluster methods
Further considerations of the effects of clustering algorithms
Understanding variable relationships within cluster formulation
Repeating Cluster Analysis over time
Qualitative Comparative Analysis (QCA)
Crisp set QCA
Accounting for time in case based methods
Combining the two methods: Cluster Analysis and QCA
QCA and software packages
Applying QCA
An alternative confirmation method: ANOVA
The application of Custer Analysis and QCA as a combined method
Dynamic Pattern Synthesis: seven cities, three years later
Threshold setting for binary crisp set conversion
Primary Implicant 'near misses'
Other considerations for the Dynamic Pattern Synthesis
The stability of variables in DPS
Stability of cases in the chosen sample
The size of the chosen sample
The number of time points in the DPS
Conclusion
Chapter Three: macro examples of Dynamic Pattern Synthesis (DPS)
Introduction
Macro case study 1: health and social care in Europe
Macro Case study 1, wave 1, 2004
Macro case study 1, wave 2, 2006
Macro case study 1, wave 4, 2010
Macro case study 1, wave 5, 2013
Macro case study 1: conclusions
Case
Variables
Patterns
Macro case study 2: the evolution of the euro based economies
Macro case study 2, wave 1, 2002
Macro case study 2, wave 2, 2006
Macro case study 2, wave 3, 2013
Macro case study 2: conclusions
Cases
Variables
Patterns
Chapter Four: A meso case study example: London Boroughs
Introduction
Meso case study: 2010
Meso case study, 2011
Meso case study, 2012
Meso case study: conclusions
Cases
Variables
Patterns
Chapter Five: micro case study example: older people in Sweden
Micro case study: older people in Sweden born in 1918
Micro case study: wave 1, 2004
Micro case study, wave 2, 2006
Micro case study, wave 4, 2010
Conclusions for the micro case study
Cases
Variables
Patterns
Chapter Six: Conclusions
Dynamic Pattern Synthesis (DPS) and different dynamic typologies
Variable patterns
Case patterns
The stability of case and variable interactions: towards some typologies
Stable dynamics
Case instability
Cluster resilience
System Instability
Reflections on complexity theory and DPS
Interactions
Short and long range interactions and feedbacks
System openness and dynamics
Case and Data Patterns
Case dynamics and complexity theory
References
Index
