ssi3-data-analysis

This repository stores replication files for "Which frame fits? Policy learning with framing for climate change policy attitudes"

Pre-registration: https://osf.io/2ztxe

Setup

Create a virtual environment and install dependencies:

python3 -m venv ssi3
source ssi3/bin/activate
pip install -r requirements.txt
python -m ipykernel install --user --name ssi3 --display-name "Python (ssi3)"

Replication

Starting from raw data

1. Place raw data files in the data/ directory (not included in repo)
2. Run code/SOSC Data Cleaner.do in Stata to generate data/ssi-data-cleaned.csv

Starting from cleaned data

3. Run code/dataAnalysis.ipynb in Jupyter (select the "Python (ssi3)" kernel)
   • Outputs: data/data_with_additional_vars.csv
4. Run R scripts for tables and figures (from the code/ directory):

   cd code
   Rscript dataAnalysis.R
   Rscript figure-code.R

   • dataAnalysis.R outputs: LaTeX tables in tables/
   • figure-code.R outputs: PNG and TIFF figures in figures/

Requirements

• Python 3.x: See requirements.txt
• R 4.0+: tidyverse, estimatr, modelsummary, kableExtra, grf
• Stata 14+ (only if cleaning raw data)

Repository Structure

• code/ - Analysis scripts (Stata, Python, R)
• data/ - Data files (cleaned data only)
• preregistration/ - Pre-analysis plan
• tables/ - Generated LaTeX tables
• figures/ - Generated figures

License

MIT License - see LICENSE file for details