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System Dynamics Review
Article . 2023 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Constructing causal loop diagrams from large interview data sets

Authors: Pablo Newberry; Neil Carhart;

Constructing causal loop diagrams from large interview data sets

Abstract

Abstract “Tackling the Root Causes Upstream of Unhealth Urban Development” is a trans‐disciplinary research project seeking to map and understand urban development decision‐making, visualise stakeholder mental models and codevelop improvement interventions. The project's primary data was gathered through 123 semistructured interviews. This article applies, compares, and discusses four variations on a method for constructing causal loop diagrams to illuminate mental models and collective decision‐making, based on manual and semiautomated processes applied to individual interview transcripts and datasets collected by thematic analysis. It concludes that while semiautomated approaches offer some time saving over manual approaches when applied to large data sets, care is required in interpreting and including peripheral contextual variables at the boundaries of the thematic analysis. Decisions regarding automation depend on the purpose of the modelling. Finally, the article recommends future applications record quantitative descriptors characterising the process of constructing CLDs from large qualitative data sets. © 2023 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Country
United Kingdom
Related Organizations
Keywords

Mental models, Urban development, Public Health, Causal Loop Diagrams (CLDs), Article

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
23
Top 10%
Top 10%
Top 10%
Green
hybrid