Newberry Pablo, Carhart Neil
University of Bristol, Bristol, UK.
Syst Dyn Rev. 2023 Sep 12;40(1). doi: 10.1002/sdr.1745. eCollection 2024 Jan.
"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.
“应对城市非健康发展的上游根源”是一个跨学科研究项目,旨在梳理并理解城市发展决策过程,可视化利益相关者的心智模型,并共同制定改进干预措施。该项目的主要数据通过123次半结构化访谈收集。本文应用、比较并讨论了构建因果循环图方法的四种变体,以阐明心智模型和集体决策,这些变体基于应用于个别访谈记录以及通过主题分析收集的数据集的手动和半自动过程。研究得出结论,虽然半自动方法在应用于大数据集时比手动方法节省一些时间,但在解释和纳入主题分析边界处的外围背景变量时需要谨慎。关于自动化的决策取决于建模目的。最后,本文建议未来的应用记录定量描述符,以表征从大量定性数据集中构建因果循环图 的过程。