Training on Advanced Data Collection and Management with REDCap

High-quality data collection and management are critical for research integrity, program performance, and evidence-based decision-making. As organizations increasingly rely on digital platforms, tools like REDCap (Research Electronic Data Capture) have become essential for building secure, scalable, and compliant data systems.

This course provides advanced, hands-on training in data collection and management using REDCap, focusing on the design and implementation of robust electronic data capture (EDC) systems. Participants will learn how to develop complex data collection instruments, configure project workflows, and manage large datasets across single-site and multi-site environments.

The program covers advanced REDCap functionalities, including branching logic, calculated fields, data validation rules, user rights management, and audit trails to ensure data quality and integrity. Participants will also explore methods for managing longitudinal and repeated measures data, enabling effective tracking of outcomes over time.

In addition, the course addresses integration of REDCap with statistical and analytical tools, supporting seamless data export, interoperability, and advanced analysis. Emphasis is placed on data governance, security, and regulatory compliance, including adherence to global standards for data protection, confidentiality, and ethical research practices.

Through practical exercises and real-world scenarios, participants will strengthen their ability to design efficient workflows, maintain high-quality datasets, and support large-scale research or organizational data initiatives using REDCap.

Duration

 10 Days

Who Should Attend

• Data Managers and Research Coordinators
• Monitoring and Evaluation (M&E) Specialists
• ICT and Database Administrators
• Health, Academic, and Research Professionals
• Project and Program Managers involved in large-scale data initiatives

Organization Impact

• Strengthens institutional data management systems and compliance readiness.

• Enhances accuracy, integrity, and efficiency of research or project data.

• Streamlines collaboration across departments and research sites.

• Builds in-house capacity for data-driven decision-making.

 Individual Impact

• Develops advanced practical REDCap expertise for data management leadership.

• Improves ability to manage secure, compliant, and complex data workflows.

• Expands professional value in research, monitoring, and evaluation roles.

• Increases readiness for cross-functional collaboration and system integration.

Upon completing this course, participants will be able to:

• Design advanced REDCap projects and implement complex data collection workflows.

• Manage user roles, data security, and access controls in compliance with global standards.

• Integrate REDCap with statistical and visualization tools for analysis and reporting.

• Apply best practices in data validation, cleaning, and quality assurance.

• Implement audit trails, version control, and governance for sensitive data management.

Module 1: Advanced REDCap System Overview

• Overview of REDCap’s architecture and data flow.

• Advanced project design and multi-institutional data capture.

• Building reusable templates and metadata frameworks.
  Case Study: Implementing REDCap in a regional health surveillance program.

Module 2: Advanced Form Design and Automation

• Using branching logic, calculated fields, and data piping.

• Setting up automated alerts and survey distribution.

• Managing repeating instruments and longitudinal data entry.
  Case Study: Streamlining participant follow-up in a clinical research project.

Module 3: Data Quality, Validation, and Governance

• Establishing data validation rules and real-time quality checks.

• Using Data Quality modules and audit trails effectively.

• Implementing data governance and compliance (GDPR, HIPAA).
  Case Study: Enhancing data reliability for a multi-country public health study.

Module 4: Integration, Import, and Export Techniques

• Integrating REDCap with R, Python, and statistical software.

• Importing legacy datasets and managing data transformations.

• Automating exports for dashboards and analytics platforms.
  Case Study: Automating data pipelines for performance dashboards.

Module 5: Security, User Management, and Data Backup

• Configuring user permissions and access hierarchies.

• Ensuring data encryption and secure transmission.

• Establishing disaster recovery and backup strategies.
  Case Study: Managing security protocols in an institutional data ecosystem.

Module 6: Longitudinal and Multi-Arm Studies

• Setting up longitudinal projects and event scheduling.

• Tracking participants over time and managing complex workflows.

• Utilizing reports and charts for time-based analyses.
  Case Study: Managing time-series data in a public health cohort study.

Module 7: REDCap API and System Integration

• Introduction to the REDCap API.

• Connecting REDCap to other systems and applications.

• Automating data synchronization and updates.
  Case Study: Building an automated REDCap–Power BI reporting system.

Module 8: Data Analysis and Reporting

• Generating descriptive reports and custom exports.

• Visualizing data trends and dashboards within REDCap.

• Linking REDCap datasets to external analysis platforms.
  Case Study: Real-time progress monitoring using REDCap analytics tools.

Module 9: Managing Multi-Center and Collaborative Projects

• Coordinating data management across multiple sites.

• Harmonizing metadata and maintaining data consistency.

• Enforcing governance and data sharing protocols.
  Case Study: Managing data for an international education consortium.

Module 10: Implementation Best Practices and Sustainability

• Establishing institutional policies for REDCap management.

• Ensuring long-term sustainability and scalability of projects.

• Managing user training and system maintenance.
  Case Study: Building a sustainable REDCap framework in a research organization.