Social Synthesis argues the importance of an applied social science that appreciates social systems as manifestations of complex systems which are highly dynamic, interactive and emergent. Haynes proposes a new mixed method called Dynamic Pattern Synthesis (DPS) that can underpin an understanding of how complex systems adapt over time.
Social Synthesis argues the importance of an applied social science that appreciates social systems as manifestations of complex systems which are highly dynamic, interactive and emergent. Haynes proposes a new mixed method called Dynamic Pattern Synthesis (DPS) that can underpin an understanding of how complex systems adapt over time.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Philip Haynes is Professor of Public Policy in the School of Applied Social Science at the University of Brighton, UK.
Inhaltsangabe
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
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
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