Systems thinking is a mindset and skill essential to understanding and taking effective action to address complex challenges. However, increasing and measuring systems thinking is difficult. This data set includes multiple approaches to measuring systems thinking. It includes 3 self-reported measures of a systems thinking mindset, and 2 performative measures of systems thinking skills (metrics derived from causal model diagrams of a system that participants created, and accuracy of responses to simple trophic cascade scenarios). The data allow for a comparison between different measurement approaches. They also allow for the evaluation of whether systems thinking is specific to disciplinary domains (ecological vs economic) or generalizes across domains.  Finally, the data provides a preliminary assessment of whether building causal models increased systems thinking.

Participants (N = 185) were workers from Amazon Mechanical Turk (MTurk) and located in the United States. Participants were paid $5.00 (roughly $10/hour).

Our survey used a mixed-methods counterbalanced design, resulting in both within-and between-subjects measures described in the table below. 

The survey was administered using Qualtrics and was entirely online. After providing informed consent, all participants completed pre-measures (the exact measures varied based on random assignment). All participants then watched a 5-minute instructional video created by the researchers and available at https://vimeo.com/479005460. The video explained and provided an example of how to use Mental Modeler. It did not, however, use the term model or system or discuss or explain anything about systems thinking.

Participants then read a written description of the ecological system. This narrative described how the foxes were dependent on the rabbits as a food source, and that the foxes reduced the number of rabbits by eating them, how the rabbits were dependent on the grass, how the grass was fertilized by the droppings of both species, etc. In addition to including the causal relationships just described, the narrative also included several non-causal factors. For example, it was stated that the landscape contained rocks. The description was written to define an unambiguously correct set of causal relationships that would be present in the model. A link then took participants to the Mental Modeler software, where they were asked to create a mental model of the scenario described to them. Participants were instructed to download the file produced by the software, as well as take a screenshot. Both were uploaded into the online survey. Participants then continued with the survey to complete the appropriate post-measures, given their random assignment. The measures are described below.