URE Coding Analysis Project

Overview

This project contains a comprehensive analysis of Undergraduate Research Experience (URE) coding data exported from NVivo. The analysis includes statistical summaries, visualizations, and detailed breakdowns of coding patterns across research articles.

Project Structure

Main Analysis

• analysis.ipynb - Jupyter notebook containing all analysis code and visualizations
• coding matrix.csv - Original coding matrix exported from NVivo

Generated Outputs

Excel Files

• comprehensive_coding_analysis.xlsx - Main analysis file with 4 sheets:
  • Articles_with_Codes: Detailed article analysis with metadata
  • Codes_with_Articles: Code frequency and article associations
  • Code_Matrix: Full cross-tabulation matrix
  • Summary_Stats: Overall statistics

CSV Files

• articles_with_codes_and_metadata.csv - Articles analysis with extracted metadata
• codes_with_article_details.csv - Codes analysis with frequency statistics
• coding_matrix_crosstab.csv - Cross-tabulation matrix
• article_summary.csv - Basic article summary
• code_summary.csv - Basic code summary

Visualizations

• coding_analysis_visualizations.png - Comprehensive 8-panel visualization dashboard
• detailed_coding_analysis.png - Code co-occurrence and usage pattern analysis

Key Findings

Dataset Overview

• 335 articles analyzed
• 81 different codes identified
• Average 8.4 codes per article
• 2,818 total code applications

Universal Codes (used in >50% of articles)

1. Program type (91.94%)
2. Primary Institutional Context (91.64%)
3. STEM (80.0%)
4. 4-Year University (63.28%)

Code Distribution

• High frequency (>50% of articles): 4 codes
• Medium frequency (10-50% of articles): 22 codes
• Low frequency (<10% of articles): 55 codes

Usage

Running the Analysis

1. Ensure you have the required Python packages installed:

   pip install pandas numpy matplotlib seaborn openpyxl

2. Open analysis.ipynb in Jupyter Notebook or VS Code

3. Run all cells to:

   • Load and analyze the coding matrix
   • Generate comprehensive spreadsheets
   • Create visualizations
   • Export summary files

Customizing the Analysis

• Modify code groupings in the visualization functions
• Add external metadata by following the instructions in the notebook
• Adjust visualization parameters for different outputs

Requirements

• Python 3.7+
• pandas
• numpy
• matplotlib
• seaborn
• openpyxl (for Excel file generation)

Author

Analysis conducted as part of URE research project.

Date

Generated: September 2025