Mapping, Organizing, and Visualizing Interdependent Events (MOVIE): A rigorous analytic framework and cost-free software application designed to model temporal and dynamic complex realist structures in social research settings

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.

Moving beyond randomized controlled trials in the evaluation of compulsory community treatment

Compulsory community treatment for people with severe mental illness remains controversial due to conflicting research evidence. Recently, there have been challenges to the conventional view that trial-based evidence should take precedence. This paper adds to these challenges in three ways. First, it emphasizes the need for critiques of trials to engage with conceptual and not just technical issues. Second, it develops a critique of trials centred on both how we can have knowledge and what it is we can have knowledge of. Third, it uses this critique to develop a research strategy that capitalizes on the information in large-scale datasets.