从大型采访数据集构建因果循环图
Pablo Newberry1, Neil Carhart1
1University of Bristol, Bristol, UK.
概括
本研究将手动和半自动方法进行比较,用于从采访数据中创建因果循环图 (CLD),以了解城市发展决策. 半自动化方法节省时间,但需要仔细解释复杂的数据.
科学领域:
- 城市规划和发展.
- 系统思维和建模是系统思维和建模.
- 定性研究方法 定性研究方法
背景情况:
- 城市发展决策是复杂的,往往不透明的.
- 了解利益相关者的心理模型对于有效的干预至关重要.
- 因果循环图 (CLD) 可以可视化这些复杂的系统.
研究的目的:
- 从定性数据构建CLD的手动和半自动方法进行比较.
- 阐明城市发展中的心理模型和集体决策过程.
- 评估不同CLD施工方法的效率和准确性.
主要方法:
- 应用和比较四种CLD施工方法的变化.
- 采用了123个半结构面试,来自"解决不健康城市发展上游的根源原因"项目.
- 在采访成绩单和主题分析数据集上采用手动和半自动化流程.
主要成果:
- 与手动方法相比,半自动化的CLD构建可以为大型定性数据集节省时间.
- 对于在主题分析边界的边缘变量,需要仔细解释.
- 手动和自动化方法之间的选择取决于具体的建模目标.
结论:
- 手动和半自动方法都能有效地可视化城市发展决策的心理模型.
- 建议包括从大型定性数据集中记录CLD构建过程的定量描述符.
- 未来的研究应该进一步完善复杂系统分析的自动化方法.
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