Table Of ContentSocial Synthesis
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 differ-
ences 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 understand-
ing. 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.
Philip Haynes is Professor of Public Policy in the School of Applied Social
Science at the University of Brighton, UK.
Complexity in Social Science
This interdisciplinary series encourages social scientists to embrace a complex sys-
tems approach to studying the social world. A complexity approach to the social
world has expanded across the disciplines since its emergence in the mid- to- late
1990s, and this can only continue as disciplines continue to change, data continue to
diversify, and governance and responses to global social issues continue to challenge
all involved. Covering a broad range of topics from big data and time, globaliza-
tion and health, cities and inequality, and methodological applications, to more
theoretical or philosophical approaches, this series responds to these challenges of
complexity in the social sciences – with an emphasis on critical dialogue around,
and application of these ideas in, a variety of social arenas as well as social policy.
The series will publish research monographs and edited collections between
60,000–90,000 words that include a range of philosophical, methodological and
disciplinary approaches, which enrich and develop the field of social complexity
and push it forward in new directions.
David Byrne is Emeritus Professor at the School of Applied Social Sciences,
Durham University, UK.
Brian Castellani is Professor in Sociology and Head of the Complexity in Health
and Infrastructure Group, Kent State University, USA. He is also Adjunct Profes-
sor of Psychiatry, Northeastern Ohio Medical University.
Emma Uprichard is Associate Professor and Deputy Director at the Centre for
Interdisciplinary Methodologies, University of Warwick, UK. She is also co-
director of the Nuffield, ESRC, HEFCE funded Warwick Q- Step Centre.
For a full list of titles in this series, please visit www.routledge.com/
Complexity- in- Social- Science/book- series/CISS
Agile Actors on Complex Terrains
Transformative realism and public policy
Graham Room
Social Synthesis
Finding Dynamic Patterns in Complex Social Systems
Philip Haynes
Social Synthesis
Finding Dynamic Patterns in
Complex Social Systems
Philip Haynes
First published 2018
by Routledge
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© 2018 Philip Haynes
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ISBN: 978- 1- 138- 20872- 8 (hbk)
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Contents
List of illustrations viii
Acknowledgements xii
Abbreviations xiv
Introduction 1
1 Methodology: towards a representation of complex
system dynamics 5
Introduction 5
Complexity science 6
Sensitivity to initial conditions 8
Emergence 9
Autopoiesis 10
Feedback 12
Networks 13
Summarising the influences of complexity theory 14
Understanding system change as patterns 15
Complexity in economic systems 18
Time and space 20
Critical realism 22
Case similarity and difference 24
Convergence and divergence 26
Complex causation 27
Methodological conclusions 30
Conclusions 32
2 The method: introducing Dynamic Pattern Synthesis 34
Introduction 34
Cluster Analysis 36
Cluster Analysis: specific approaches 38
vi Contents
Distance measures 38
Hierarchical and nonhierarchical CA 39
Clustering algorithms 40
Dendrogram charts 40
Icicle charts 42
Using SPSS to calculate and compare cluster methods 43
Further considerations of the effects of clustering algorithms 47
Understanding variable relationships within cluster formulation 52
Repeating CA over time 53
Qualitative Comparative Analysis 53
Crisp set QCA 54
Accounting for time in case based methods 57
Combining the two methods: CA and QCA 57
Qualitative Comparative Analysis and software packages 58
Applying QCA 58
An alternative confirmation method: ANOVA 61
The application of CA and QCA as a combined method 63
Dynamic Pattern Synthesis: seven cities, three years later 63
Threshold setting for binary crisp set conversion 64
Prime implicant ‘near misses’ 65
Other considerations for the DPS 67
Conclusion 71
3 Macro examples of Dynamic Pattern Synthesis 73
Introduction 73
Macro case study 1: health and social care in Europe 73
Macro case study 1, wave 1, 2004 74
Macro case study 1, wave 2, 2006 79
Macro case study 1, wave 4, 2010 82
Macro case study 1, wave 5, 2013 90
Macro case study 1: conclusions 95
Macro case study 2: the evolution of the euro based economies 99
Macro case study 2, wave 1, 2002 99
Macro case study 2, wave 2, 2006 106
Macro case study 2, wave 3, 2013 112
Macro case study 2: conclusions 118
4 A meso case study example: London boroughs 122
Introduction 122
Meso case study, 2010 123
Meso case study, 2011 133
Contents vii
Meso case study, 2012 142
Meso case study: conclusions 149
5 Micro case study example: older people in Sweden 154
Micro case study: older people in Sweden born in 1918 154
Micro case study, wave 1, 2004 155
Micro case study, wave 2, 2006 162
Micro case study, wave 4, 2010 167
Conclusions for the micro case study 169
6 Conclusions 175
Dynamic Pattern Synthesis and different dynamic typologies 175
Reflections on complexity theory and DPS 180
References 183
Index 187
Illustrations
Figures
2.1 An example of a simple dendrogram 41
2.2 Understanding an icicle plot 42
2.3 City data: SPSS variable definitions view 45
2.4 City data: SPSS data view 45
2.5 Hierarchical cluster sub menu 45
2.6 Changing the SPSS hierarchical CA plots sub menu 46
2.7 Dendrogram using average linkage without standardisation 46
2.8 Dendrogram using average linkage with standardisation 47
2.9 Ward’s method with standardised data: the city data set 48
2.10 Complete linkage method with standardised data: the city data set 49
2.11 Median linkage method with standardised data: the city data set 49
2.12 Centroid linkage method with standardised data: the city data set 50
2.13 Average linkage method with standardised data: the city data set 51
2.14 Single linkage method with standardised data: the city data set 52
2.15 Cluster Analysis with Ward’s method and standardised data:
city dataset 2 65
3.1 Icicle plot of cluster formulation: macro case study 1, 2004 75
3.2 Dendrogram of cluster formulation: macro case study 1, 2004 76
3.3 Icicle plot of cluster formulation: macro case study 1, 2006 80
3.4 Dendrogram of cluster formulation: macro case study 1, 2006 81
3.5 Icicle plot of cluster formulation: macro case study 1, 2010 85
3.6 Dendrogram plot of cluster formulation: macro case study 1, 2010 86
3.7 Icicle plot of cluster formulation: macro case study 1, 2013 91
3.8 Dendrogram of cluster formulation: macro case study 1, 2013 91
3.9 Icicle plot of cluster formulation: macro case study 2, 2002 101
3.10 Dendrogram of cluster formulation: macro case study 2, 2002 102
3.11 Icicle plot of cluster formulation: macro case study 2, 2006 107
3.12 Dendrogram of cluster formulation: macro case study 2, 2006 107
3.13 Icicle plot of cluster formulation: macro case study 2, 2013 113
3.14 Dendrogram of cluster formulation: macro case study 2, 2013 114
4.1 Icicle plot of cluster formulation, meso case study, 2010 125
Illustrations ix
4.2 Dendrogram of cluster formulation: meso case study, 2010 126
4.3 Map of the location of London boroughs 132
4.4 Icicle plot of cluster formulation: meso case study, 2011 135
4.5 Dendrogram of cluster formulation: meso case study, 2011 136
4.6 Icicle plot of cluster formulation: meso case study, 2012 144
4.7 Dendrogram of cluster formulation: meso case study, 2012 145
5.1 Icicle plot of cluster formulation: micro case study, 2004 156
5.2 Dendrogram of cluster formulation: micro case study, 2004 157
5.3 Icicle plot of cluster formulation: micro case study, 2006 164
5.4 Dendrogram of cluster formulation: micro case study, 2006 164
5.5 Icicle plot of cluster formulation: micro case study, 2010 168
5.6 Dendrogram of cluster formulation: micro case study, 2010 169
Tables
1.1 Causality: necessary, but not sufficient 27
2.1 Example of an agglomeration schedule: Jayne, Ayesha,
Mark and Li Wei 43
2.2 Fictional dataset for hierarchical CA 44
2.3 A truth table examining pollution in cities 55
2.4 A simplified truth table examining pollution in cities 55
2.5 QCA: conversion of city data variables to binary scores 59
2.6 QCA threshold scores with cluster outcome variable added 60
2.7 Mean average scores by clusters; city example 62
2.8 ANOVA results for mean average cluster scores 62
2.9 Eta and eta squared results for variables explanation of cluster
definitions, ranked by largest eta squared score 63
2.10 Fictional dataset at time point 2 for hierarchical CA 64
2.11 QCA time period 2: conversion of city data variables to binary scores 66
2.12 QCA time period 2: threshold scores with cluster outcome
variable added 67
2.13 Variable average trends, the seven cities example 68
2.14 Longitudinal truth table: the stability of prime implicants over time 69
3.1 Variables included in macro case study 1 74
3.2 Agglomeration schedule for country aggregates, case study 1, 2004 75
3.3 QCA: data for truth table macro case study 1, 2004 77
3.4 QCA truth table for macro case study 1, 2004 78
3.5 Boolean simplification macro case study 1, 2004 79
3.6 Agglomeration schedule or country aggregates,
macro case study 1, 2006 80
3.7 QCA: data for truth table, macro case study 1, 2006 83
3.8 QCA truth table for macro case study 1, 2006 84
3.9 Boolean simplification macro case study 1, 2006 84
3.10 Agglomeration schedule for country aggregates,
macro case study 1, 2010 85
