Protocol for an interview-based method for mapping mental models using causal-loop diagramming and realist interviewing

Erin S. Kenzie, Wayne Wakeland, Antonie Jetter, Kristen Hassmiller Lich, Mellodie Seater, Rose Gunn and Melinda M. Davis

Evaluation and Program Planning, 2024, vol. 103, issue C

Abstract: Causal-loop diagramming, a method from system dynamics, is increasingly used in evaluation to describe individuals’ understanding of how policies or programs do or could work ("mental models"). The use of qualitative interviews to inform model development is common, but guidance for how to design and conduct these interviews to elicit causal information in participant mental models is scant. A key strength of semi-structured qualitative interviews is that they let participants speak freely; they are not, however, designed to elicit causal information. Moreover, much of human communication about mental models—particularly larger causal structures such as feedback loops—is implicit. In qualitative research, part of the skill and art of effective interviewing and analysis involves listening for information that is expressed implicitly. Similarly, a skilled facilitator can recognize and inquire about implied causal structures, as is commonly done in group model building. To standardize and make accessible these approaches, we have formalized a protocol for designing and conducting semi-structured interviews tailored to eliciting mental models using causal-loop diagramming. We build on qualitative research methods, system dynamics, and realist interviewing. This novel, integrative method is designed to increase transparency and rigor in the use of interviews for system dynamics and has a variety of potential applications.

Keywords: Interview; Causal-loop diagram; Systems science; Mental model; Realist evaluation; System dynamics (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:epplan:v:103:y:2024:i:c:s0149718924000132

DOI: 10.1016/j.evalprogplan.2024.102412

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