Methodological Innovations, Ahead of Print.
The study of the social world involves multiple, multidimensional, and endlessly dynamic competing systems evolving over time. This inherent complexity, however, does not mean that the social world is chaotic, random, or unstructured. Rather, structural forms do emerge and co-exist in social settings. It is the emergence, maintenance, and decay of these structures that allows researchers to detect temporary stability and provides them with the means to make predictions about continuity and change in social dynamics. Arguably then, the main challenge in the study of the social world consist of developing robust and consistent strategies or tools capable of tracing, mapping, or retrieving these structural forms in order to ultimately model this social complexity. Accordingly, the overarching purpose of this study consists of addressing this analytic and methodological challenge by proposing a groundbreaking analytic framework, and its corresponding software application, designed to extract temporal and dynamic structures in the social world relying on complex realism, complex systems, dynamic temporal network analyses, and data science and visualization techniques. Together, these frameworks constitute the foundations of Mapping, Organizing, and Visualizing Interdependent Events (MOVIE), an analytic framework [and software application] designed to ease the understanding of, individually-produced or interactively-generated, events and knowledge evolution, by tracing and recreating the processes that may have affected participants’ experiences, outcomes, and standpoints. To demonstrate MOVIE’s performance and rigor in capturing and recreating the dynamic complexity of micro-level interactions, the analyses relied on publicly available data sources on foreign policy and conflict resolution. All data elements and tools are provided with this study to make these analyses fully transparent and reproducible. MOVIE can trace/recreate the temporal elements embedded in existing qualitative databases (e.g. those generated with NVivo/MAXQDA/Atlas.ti), even if they were created without considering their dynamic time-evolving features, whose meaning-building relevance may help inform policy planning and action.