We were contracted by World Vision to support the development of a global research report examining the intersection of climate change, conflict, and hunger across fragile and conflict-affected regions.
World Vision provided large, multi-country survey datasets in Excel. Tropic Flare led the data cleaning, statistical analysis, and analytical synthesis using Python and R, transforming raw survey data into rigorous insights, statistical tables, and evidence-based narrative analysis used directly in the final published report.
World Vision collected extensive survey data across multiple countries and regions, capturing perceptions and lived experiences related to climate shocks, conflict, displacement, and food insecurity.
However, the data presented several challenges:
The analysis needed to be methodologically sound, transparent, and suitable for a global advocacy and policy audience.
Tropicflare designed and executed a full analytical workflow to transform raw survey data into publishable research outputs.
Using Python for data preparation and exploratory analysis and R for statistical modeling, we cleaned, standardized, and analyzed survey responses across countries and regions. We then produced structured statistical tables, regression outputs, and written analysis paragraphs that directly informed the report's findings and conclusions.
Close collaboration with World Vision's research and editorial teams ensured analytical clarity, consistency with the research methodology, and alignment with the report's narrative goals.
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