1. Building confidence in causal maps generated from purposive text data ： mapping transcripts of Federal Reserve 101256025 公行碩二林瑜婷

2. Abstract This paper explains a systematic way to code qualitative text data to generate causal maps for system dynamics modeling. The coding method elucidated in the study was influenced by grounded theory → it provides systematic and reliable ways to build higher levels of confidence in models.

3. Methods for using qualitative data in system dynamics modeling niches “purposive” text data ： FOMC transcripts

4. Learning from grounded theory The primary goal of grounded theory is theory generation rather than theory testing. grounded theory generates a theory inductively from raw qualitative data conceptualization phase

5. Learning from grounded theory Three types of coding ： Open coding Axial coding Selective coding

6. Guide to coding maps from purposive text data

11. Implications and discussion Value of coding for system dynamics modelers Systematically Reliable Evaluation criteria for data analysis

12. Challenges and future research Some trade-offs ： Costly and labor intensive Less polished