Course Introduction
Electronic data capture is a fundamental component of modern clinical, academic, public health, and research data management. REDCap provides researchers and research organizations with a structured environment for designing databases, developing data collection instruments, managing study workflows, monitoring data quality, and securely collecting research information. This course provides comprehensive practical training in using REDCap for effective electronic research data capture and management.
The course introduces participants to the architecture, terminology, features, and operational principles of REDCap before progressing into hands-on project development. Participants learn how to create projects, configure project settings, build data collection instruments, define fields and variables, establish validation rules, organize instruments into appropriate workflows, and prepare REDCap projects for real-world research activities.
Participants will explore advanced REDCap functionality including branching logic, calculated fields, validation, longitudinal projects, repeating instruments, events, arms, surveys, automated invitations, alerts, data quality rules, and record management. Emphasis is placed on designing intuitive and reliable data collection systems that reduce errors, standardize information capture, improve data completeness, and support efficient research operations.
Data quality, security, privacy, and governance are central themes throughout the training. Participants examine approaches for controlling user access, maintaining audit trails, protecting sensitive research information, monitoring data quality, resolving discrepancies, managing project changes, and supporting responsible handling of participant data. The course also considers appropriate procedures for testing projects before deployment and maintaining data integrity throughout the study lifecycle.
The training also addresses REDCap's integration and automation capabilities, including data import and export, APIs, external data connections, reporting, automated workflows, and programmatic interaction with research databases. Participants gain an understanding of how REDCap can operate alongside statistical software, laboratory systems, institutional platforms, and other research technologies while maintaining appropriate controls over data movement and transformation.
By the end of the course, participants will be able to design, configure, test, deploy, monitor, and maintain REDCap electronic data capture projects. Through practical exercises and an integrated project, participants will develop a functional understanding of how REDCap can support clinical studies, academic research, surveys, registries, longitudinal studies, and other structured research data collection requirements.
Duration
10 days
Who Should Attend
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Clinical researchers seeking practical skills in electronic data capture using REDCap.
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Clinical data managers responsible for developing and maintaining research databases.
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Research coordinators managing participant data and study workflows.
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Clinical research associates supporting electronic data collection and data quality activities.
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Investigators developing databases for clinical, academic, or public health research.
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Epidemiologists conducting surveys, observational studies, registries, and longitudinal research.
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Biostatisticians working with structured research datasets and electronic data collection systems.
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Research assistants responsible for participant recruitment, enrollment, and data entry.
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Public health professionals collecting and managing research and program data.
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Data managers and database developers supporting institutional research projects.
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Monitoring and evaluation professionals using structured electronic data collection workflows.
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Information technology professionals supporting research data platforms and REDCap environments.
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Research governance, quality assurance, and compliance professionals working with electronic research records.
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Postgraduate students conducting dissertations, clinical studies, surveys, or other research projects.
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Pharmaceutical, biotechnology, healthcare, and contract research organization professionals.
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Academic institutions and research organizations seeking to strengthen electronic data capture capabilities.
Course Objectives
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Explain the architecture, purpose, terminology, and core functionality of REDCap within modern electronic research data capture environments.
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Create and configure REDCap projects according to study objectives, data requirements, workflows, and participant management needs.
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Design effective data collection instruments using appropriate field types, variable names, labels, validations, and standardized structures.
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Apply branching logic, calculated fields, required fields, validation rules, and other REDCap features to improve data collection quality.
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Configure longitudinal projects, events, arms, repeating instruments, and other structures for complex research study designs.
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Develop surveys and participant-facing instruments that support clear, consistent, accessible, and reliable electronic data collection.
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Implement user roles, permissions, access controls, audit trails, and appropriate security practices for REDCap research projects.
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Design data quality checks and workflows for identifying missing, inconsistent, invalid, duplicated, or unusual research data.
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Import, export, review, transform, and manage REDCap data while maintaining data integrity and appropriate documentation.
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Generate reports and dashboards that support study monitoring, data review, operational decision-making, and research oversight.
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Use REDCap APIs and automation capabilities to support programmatic data exchange, integrations, and advanced research workflows.
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Test, deploy, maintain, and continuously improve REDCap projects while supporting data governance, privacy, quality, and research requirements.
Comprehensive Course Outline
Module 1: Foundations Of Electronic Data Capture And REDCap
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Principles of electronic data capture and its role in modern clinical and research data management.
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REDCap architecture, terminology, project types, core features, and research use cases.
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Comparing electronic data capture with paper-based and spreadsheet-based research data collection.
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REDCap project lifecycle from study planning and development through deployment, maintenance, and closure.
Module 2: REDCap Project Setup And Configuration
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Creating new REDCap projects and selecting appropriate project development approaches.
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Project settings, project attributes, data collection options, and administrative configurations.
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Development, production, testing, and deployment considerations for research projects.
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Project documentation, naming conventions, version control, and configuration management.
Module 3: Data Collection Instrument Design
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Creating data collection instruments using text fields, dropdowns, radio buttons, checkboxes, dates, files, and other field types.
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Designing logical instrument structures, field labels, variable names, and participant-friendly data entry workflows.
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Required fields, field notes, matrix fields, descriptive fields, and standardized data collection components.
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Applying user-centered design principles to reduce confusion, entry errors, and unnecessary data burden.
Module 4: Field Validation, Logic And Calculations
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Configuring field validation for dates, numbers, ranges, emails, identifiers, and other structured values.
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Using branching logic to display relevant questions based on participant responses and study conditions.
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Creating calculated fields for automated derivation of values from collected information.
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Testing validation rules, logic expressions, calculations, and edge cases before deployment.
Module 5: Longitudinal Projects, Events And Repeating Data
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Designing longitudinal REDCap projects with multiple study events and time points.
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Configuring arms, events, event-specific instruments, and participant schedules.
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Using repeating instruments and repeating events for recurring assessments and observations.
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Managing complex study workflows while maintaining consistent participant-level data structures.
Module 6: Surveys And Participant-Facing Data Collection
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Designing REDCap surveys for participant self-administered electronic data collection.
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Survey settings, invitations, participant access, response workflows, and survey completion controls.
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Automated survey invitations, reminders, alerts, and scheduling approaches.
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Improving participant experience, accessibility, clarity, and completion quality in electronic surveys.
Module 7: User Management, Roles And Security
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Creating user accounts, assigning roles, and implementing appropriate project-level permissions.
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Applying least-privilege access principles to research data collection and management.
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Understanding audit trails, user activity tracking, record history, and accountability mechanisms.
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Security considerations for participant information, sensitive research data, and institutional REDCap environments.
Module 8: Data Quality Management And REDCap Data Quality Tools
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Identifying missing, invalid, inconsistent, duplicate, and potentially erroneous research data.
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Developing data quality rules and queries for systematic data review.
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Reviewing discrepancies, resolving data issues, and documenting quality management activities.
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Using REDCap quality tools to support ongoing study data review and database cleaning.
Module 9: Data Import, Export And Record Management
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Importing structured data into REDCap while maintaining field mappings and data integrity.
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Exporting research data for analysis, reporting, archiving, and downstream processing.
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Managing records, identifiers, record status, data corrections, and controlled data changes.
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Understanding data formats, coding, transformation requirements, and safe data transfer practices.
Module 10: Reports, Dashboards And Study Monitoring
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Creating custom reports for investigators, coordinators, monitors, and data management teams.
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Developing dashboards and summary views for participant recruitment, completion, and study progress.
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Using filters, calculated fields, and data selections to support targeted monitoring.
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Applying REDCap reporting capabilities to identify operational and data quality trends.
Module 11: Data Dictionary, Metadata And Project Documentation
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Understanding REDCap data dictionaries and their role in project structure and reproducibility.
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Documenting variables, field types, validation rules, choices, logic, and instrument relationships.
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Importing and exporting project structures while maintaining consistent metadata.
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Establishing documentation practices for project maintenance, handover, auditing, and replication.
Module 12: REDCap Data Governance, Privacy And Compliance
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Research data governance principles and responsible management of participant information.
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Privacy, confidentiality, consent, identifiers, and appropriate handling of sensitive research data.
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Auditability, documentation, data integrity, and institutional research governance requirements.
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Developing procedures for project access, data sharing, retention, archival, and secure disposal.
Module 13: REDCap APIs, Automation And System Integration
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Introduction to REDCap APIs and programmatic interaction with research project data.
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Automating data imports, exports, record updates, metadata retrieval, and recurring research workflows.
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Integrating REDCap with statistical software, analytical environments, laboratory systems, and other platforms.
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API authentication, access control, error handling, logging, and responsible automation practices.
Module 14: Advanced REDCap Applications And Emerging Technologies
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Using REDCap for registries, clinical trials, cohort studies, surveys, observational studies, and longitudinal research.
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Mobile and offline data collection considerations and field-based research workflows.
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Integrating external services, automated processes, and emerging research technology environments.
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Opportunities and limitations of artificial intelligence, automation, and intelligent data quality monitoring in REDCap workflows.
Module 15: Project Testing, Deployment And Lifecycle Management
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Developing comprehensive testing plans for instruments, logic, calculations, workflows, permissions, and reports.
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User acceptance testing and controlled deployment of REDCap projects into production environments.
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Managing changes, project revisions, upgrades, corrections, and ongoing maintenance.
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Preparing projects for database lock, study completion, archival, and long-term research data management.
Module 16: Integrated REDCap Electronic Data Capture Practicum
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Designing a complete REDCap project from a research protocol, data specification, and collection requirements.
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Building instruments, validation rules, branching logic, events, surveys, reports, and user roles.
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Testing the project using simulated participant records and resolving identified data quality issues.
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Presenting and evaluating an integrated REDCap project covering design, security, quality, reporting, and lifecycle management.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
