ABSTRACT
Dynamic Pattern Synthesis (DPS) offers a mixed method for considering small longitudinal datasets over time. It offers a replicable method that can help understand the dynamics of social and economic interactions and identify both stable and unstable patterns. While the DPS method proposed starts with a multivariate exploration of scale variables that utilises Cluster Analysis (CA), after the binary conversion to the QCA stage it has the potential to be adapted to include other categorical variables with two nominal categories, and can also model another chosen outcome variable. There are three key elements to the concluding stage of DPS and its qualitative interpretation of the synthesis situation for the system it is observing. Firstly, DPS considers variable trends over time, secondly it considers case trajectories over time, and finally it seeks to understand the combined stability patterns of variable and case relationships. This conclusion also presents some closing thoughts on the concepts discussed in the preceding chapters of this book.
