Constructing causal loop diagrams from large interview data sets
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View abstract on PubMed
Summary
This summary is machine-generated.This study compares manual and semi-automated methods for creating causal loop diagrams (CLDs) from interview data to understand urban development decision-making. Semi-automated methods save time but require careful interpretation of complex data.
Area Of Science
- Urban planning and development
- Systems thinking and modeling
- Qualitative research methodologies
Background
- Urban development decision-making is complex and often opaque.
- Understanding stakeholder mental models is crucial for effective interventions.
- Causal loop diagrams (CLDs) can visualize these complex systems.
Purpose Of The Study
- To compare manual and semi-automated methods for constructing CLDs from qualitative data.
- To illuminate mental models and collective decision-making processes in urban development.
- To assess the efficiency and accuracy of different CLD construction approaches.
Main Methods
- Application and comparison of four variations of CLD construction methods.
- Utilized 123 semi-structured interviews from the 'Tackling the Root Causes Upstream of Unhealth Urban Development' project.
- Employed both manual and semi-automated processes on interview transcripts and thematic analysis datasets.
Main Results
- Semi-automated CLD construction offers time savings for large qualitative datasets compared to manual methods.
- Careful interpretation is needed for peripheral variables at the boundaries of thematic analysis.
- The choice between manual and automated approaches depends on the specific modeling objectives.
Conclusions
- Both manual and semi-automated methods can effectively visualize mental models for urban development decision-making.
- Recommendations include recording quantitative descriptors for CLD construction processes from large qualitative datasets.
- Future research should further refine automated methods for complex systems analysis.
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