Procedural Justice, Social Identity & Legitimacy Meta-Analysis

Author: Angus Chan Institution: UCL Department of Security and Crime Science

Project Overview

This repository contains the reproducible analytical pipeline for a large-scale meta-analysis of 123 studies ($N = 200,966$). The project quantifies the global correlations between procedural justice, social identity, and police legitimacy.This infrastructure was designed to handle diverse statistical reporting formats and harmonize disparate datasets into a single relational structure—mirroring the robust data management requirements of multi-site longitudinal research.

Technical Infrastructure

This project is built using R and follows a modular design to ensure high data auditability and transparency.

• Language: R (Version 4.x)
• Key Libraries: metafor (meta-analysis), tidyverse (data cleaning), ggplot2 (visualization).
• Version Control: Git/GitHub for full pipeline history.
• Data Standards: Implements a custom coding schema to handle multi-level effect sizes and cross-study variance.

Repository Structure

├── R/                        # Modular R functions for data processing
├── main.R                    # Primary entry point to execute the full pipeline
├── meta_analysis_data.csv    # Harmonized dataset (anonymized/de-identified)
└── README.md                 # Project documentation

Usage & Reproducibility

To reproduce the findings, clone this repository and run the primary script:

1. Clone the repo:

git clone https://github.com/cyfangus/my_phd_meta_analysis.git

2. Execute Analysis: Open main.R in RStudio. The script is configured to automatically load dependencies and process the raw data in the /R directory.

Data Governance & Open Science

Ethics: All data handling follows UCL Research Ethics and GDPR protocols.

Anonymization: Raw data has been pseudonymized and aggregated to ensure participant privacy while maintaining research utility.

Transparency: This repository serves as an Open Science archive to support the "Interoperability" and "Reusability" (FAIR) of the research findings.

Contact

For inquiries regarding the data schema or methodological framework: Angus Chan - [email protected]