Event Data Analysis Guide

Data Security and Privacy Considerations

Data Storage

• Store all data files locally and DO NOT upload raw data to GitHub or any public repository
• If version control is needed, use .gitignore to exclude data files
• Consider using sample data or anonymized subsets for documentation purposes

Privacy Requirements

1. Remove entries for participants who did not consent to data usage
2. Document the process of consent verification
3. Keep a separate record of excluded entries for audit purposes
4. Maintain a log of when and why data was excluded

Data Cleaning Protocol

Standardizing Manual Entries

1. Review and standardize educational fields:

   • Create a mapping table for similar entries (e.g., "Computer Science" = "CS" = "CS Vanderbilt")
   • Document all equivalences in a separate reference file
   • Include original and standardized values in the cleaning log

2. Create a data cleaning log with:

   • Original value
   • Standardized value
   • Reason for change
   • Date of modification
   • Person responsible for the change

Statistical Analysis Framework

Demographic Analysis

1. Calculate basic statistics:

   • Age distribution
   • Gender representation
   • Geographic distribution
   • Educational background
   • Professional experience

2. Documentation of any assumptions made during analysis

Reporting Guidelines

Data Visualization

1. Include clear labels and legends
2. Note excluded categories or filtered data
3. Provide context for interpretations

Reproducibility Checklist

• Data exclusions documented
• Manual entry standardizations logged
• Privacy requirements met
• Analysis code version controlled
• Data cleaning steps documented
• Statistical assumptions stated
• Results independently verified