DOI
10.1007/s11213-019-09507-7

Summary
Experienced system dynamicists commonly conceptualise causal relationships and feedback loops using Causal Loop Diagrams (CLDs). In adhering to best practice, multiple data collection activities may be required (e.g. multiple group model building sessions), resulting in multiple CLDs. To achieve covariation that correctly attributes cause and effect from multiple data sets, aggregation of CLDs may be necessary. Such aggregation must adequately account for attribution variations across constructed CLDs to produce a coherent view of a phenomenon of interest in a ‘complete’ model. Discourse concerning model completeness should account for the potential for method bias. The data collection method chosen for CLD development will influence the ability to create a model that is fit for the purposes of the study and influence the likelihood of achieving model completeness. So too does the method chosen for model aggregation. Little processual guidance exists on a method for data aggregation in system dynamics studies. This paper examines three data aggregation approaches, based on existing qualitative analysis methods, to determine the suitability of each method. The approaches considered include triangulation, includes all data in the aggregation process; grounded theory, bases aggregation on frequency of occurrence; and synthesis, extends aggregation to include variables based on magnitude of occurrence. Comments are made regarding the relevance of each method for different study types, with final remarks reiterating the consideration of equifinality and multifinality in research and their impact on method selection. This paper enhances the rigour of research aiming at facilitating greater success in studies utilising CLDs.

Relevance for Complex Systems Knowledge
This article states that system dynamics studies seek to generate a holistic understanding of the interactions among system components and their environment and that Causal Loop Diagrams (CLDs) are used to provide conceptual insight into the causal relationships within a system under examination. Amalgamation of multiple perspectives is key for convergence, whether this be among a variety of investigators or a group of participants, each stakeholder must be provided with the opportunity to contribute and reach a consensus on the final output. Participants drive model development,  when all participants agree upon the model and its completeness convergence is achieved, reached when participants believe no further iterations would bring them closer to a final result.

The article examines three methods to aggregate data into a CLD:

- Triangulation - data validation or data completion by comparing multiple data sources. The major risk is information overload.

- Grounded theory - is looking for convergences of data related to  frequency and magnitude of occurrences. Major risk is that important data raised from a single source might get lost.

- Synthesis - as a qualitative research approach brings together separate parts of information to create a whole. This can be used also fr a meta-synthesis where different CLDs are brought together in a larger system. The proble is that synthesis has a risk to get too detailed, and would be best used for really large and complex analysis.

Point of Strength
The article contributes to a discussion about data management in system analysis. It would be most suited as an inspirational piece for the lecturers.  It would most likely be difficult to uses as reading material for students.