# Digital Inclusion and Equitable Public Services for People with Disabilities in Thailand

## 📌 Project Overview
This repository contains datasets, AMOS model files, and documentation related to the research article:  
*"Overcoming Data Barriers: Digital Inclusion and Equitable Public Services for People with Disabilities in Thailand."*  

This study examines the accessibility and efficiency of public services for people with disabilities in Thailand. It evaluates disparities between government officials and people with disabilities in data-driven governance practices, focusing on **Digital Inclusion**. The research employs **Multi-Group Structural Equation Modeling (multi-SEM)** to analyze variations in accessibility, policy implementation, and public service outcomes.

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## 📂 Project Structure

### 📁 **Dataset/**
- `Dataset.csv` → **De-identified dataset** (CSV format)

### 📁 **AMOS_Models/**
- `AMOS_Models.amw` → **AMOS file for Multi-Group SEM analysis**

### 📁 **Documentation/**
- `README.txt` → **This file explaining project structure and data usage**

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## 📊 **How to Use the Data**
1. **Dataset (`.csv`)** can be opened in **IBM SPSS Statistics**, **Jamovi**, or other statistical software that supports CSV format.

2. **AMOS Model Files (`.amw`)** can be loaded into **IBM SPSS AMOS (Version 24.0)** for analysis.  
   - The dataset (`.csv`) can also be used to create a **Multi-Group SEM model in Jamovi** using the **jSEM module**.  
   - This module is based on the **lavaan package in R** and allows structural equation modeling without requiring AMOS.  
   - **For guidance on setting up the model, please refer to the official documentation and tutorials for Jamovi and jSEM.**  

For more details on **jSEM and lavaan**, visit:  
- 🔗 **Jamovi jSEM module documentation**: [https://jamovi.org] 
- 🔗 **lavaan package documentation**: [https://lavaan.ugent.be/]

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## 🔐 **Ethical Considerations and Restricted Data Access**
To ensure compliance with research ethics, all **personally identifiable information** has been removed from the dataset. The de-identification process follows the **Safe Harbor method**, and the removed variables include:  
- **Gender, Age, Government Official Category, Type of Disability, and Experience Level.**  
- **Provincial data has been aggregated into regional classifications** to protect participant anonymity while preserving the representativeness of the sample.  

**Openly available data:**  
All **de-identified data (`.csv`) and AMOS model files (`.amw`)** are **openly available** in Zenodo at:  
🔗 **[https://doi.org/10.5281/zenodo.15015473]**  

**Restricted data and controlled access:**  
Due to privacy and ethical concerns, certain sensitive data that could lead to re-identification of participants **are not publicly available**. These data are stored in a **controlled-access repository**.  

Researchers who require access to these restricted data must:  
1. Submit a formal request via **email to the corresponding author**.  
2. Provide details on **research purpose and intended data usage**.  
3. Sign a **Data Use Agreement (DUA)** ensuring compliance with ethical guidelines.  

Requests for access will be reviewed on a **case-by-case basis** to ensure compliance with research integrity standards.  

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## 📞 **Contact Information**
For inquiries about the project or data access, please contact:  
📧 **ch.sitt@gmail.com**  
🏛 **Mahidol University**
