Course Introduction
REDCap database design and research data management require more than simply creating electronic forms. Well-designed REDCap projects need carefully structured variables, appropriate relationships, consistent metadata, reliable validation rules, clear workflows, controlled access, and comprehensive documentation. This course provides practical and advanced training for designing robust REDCap databases that support high-quality research data throughout the study lifecycle.
The course begins with the foundations of research database architecture and REDCap project planning, helping participants translate research protocols, objectives, data requirements, and operational workflows into well-structured database designs. Participants learn how to identify data entities, define variables, establish appropriate field structures, organize instruments, and make design decisions that support efficient data collection and downstream analysis.
Participants will develop practical skills in building REDCap instruments, configuring events and longitudinal structures, applying branching logic and calculated fields, creating validation rules, managing repeating data, and developing participant-facing surveys. Particular attention is given to avoiding common database design problems such as redundant variables, inconsistent naming, poorly defined response options, unnecessary data collection, and structures that make analysis or long-term maintenance difficult.
Research data management is integrated throughout the course, covering data quality, metadata, documentation, security, privacy, access control, audit trails, data cleaning, data review, reporting, export, and controlled data sharing. Participants learn how to establish systematic processes that preserve data integrity and traceability while supporting collaboration among investigators, coordinators, data managers, statisticians, and other research stakeholders.
The course also explores advanced REDCap capabilities including data dictionaries, APIs, automated workflows, external integrations, quality rules, dashboards, and programmatic data management. Participants examine how REDCap can connect with statistical analysis environments and other research systems while maintaining appropriate controls over data transformation, movement, security, and provenance.
By the end of the training, participants will be able to plan, design, implement, test, manage, monitor, and maintain a research database in REDCap using sound database and research data management principles. The integrated practicum enables participants to develop a complete REDCap research project and apply database design, data quality, governance, security, documentation, reporting, and lifecycle management techniques in a realistic research scenario.
Duration
10 days
Who Should Attend
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Clinical data managers responsible for designing and maintaining REDCap research databases.
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Research data managers seeking advanced skills in REDCap database architecture and governance.
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Clinical research coordinators managing electronic research data collection and participant records.
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Investigators developing REDCap databases for clinical, academic, epidemiological, and public health studies.
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Research database developers responsible for building structured REDCap projects.
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Clinical research associates involved in reviewing electronic research data and database quality.
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Biostatisticians working with REDCap datasets and research analysis workflows.
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Epidemiologists designing cohort, registry, survey, and longitudinal research databases.
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Research assistants responsible for REDCap data entry, review, cleaning, and management.
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Public health professionals managing electronic research and surveillance databases.
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Monitoring and evaluation professionals using REDCap for structured data collection and reporting.
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Research information technology specialists supporting REDCap environments and integrations.
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Data governance and quality assurance professionals working with research information systems.
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Postgraduate students developing research databases for dissertations, theses, and academic studies.
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Pharmaceutical, biotechnology, healthcare, and contract research organization professionals.
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Research institutions seeking to strengthen REDCap database design and research data management capabilities.
Course Objectives
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Explain database design principles and how they apply to REDCap projects used for clinical, academic, public health, and research applications.
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Translate research protocols, study objectives, data requirements, and operational workflows into structured and maintainable REDCap database designs.
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Design appropriate REDCap instruments, variables, field types, response options, metadata, and logical structures for reliable research data collection.
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Apply database normalization concepts, consistent naming conventions, data dictionaries, and standardized metadata to improve research database quality.
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Configure branching logic, calculated fields, validation rules, repeating instruments, events, and longitudinal structures for complex research studies.
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Develop research data management procedures covering data collection, quality control, cleaning, documentation, security, sharing, preservation, and archival.
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Implement appropriate user roles, access permissions, audit trails, and governance controls to protect research information and maintain accountability.
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Design systematic data quality processes for identifying missing, invalid, inconsistent, duplicate, and anomalous records within REDCap databases.
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Create reports, dashboards, queries, and review workflows that support research monitoring, operational oversight, and data quality assessment.
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Manage REDCap data imports, exports, transformations, reconciliations, and integrations while maintaining data integrity and traceability.
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Apply REDCap APIs, automation, and integration approaches to support efficient research data workflows and interoperability with external systems.
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Develop, test, document, deploy, maintain, and continuously improve REDCap databases throughout the complete research data lifecycle.
Comprehensive Course Outline
Module 1: Foundations Of REDCap Database Design And Research Data Management
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Principles of database design and their application to electronic research data environments.
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REDCap architecture, terminology, project structures, records, instruments, events, and metadata.
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Relationship between database design, data quality, research reproducibility, analysis, and reporting.
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Research data lifecycle considerations from project planning through closure and long-term preservation.
Module 2: Research Protocol Analysis And Database Planning
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Translating research protocols, objectives, hypotheses, and procedures into database requirements.
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Identifying study entities, data elements, participant attributes, events, outcomes, and operational requirements.
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Developing database specifications, data collection plans, and REDCap project design documentation.
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Identifying critical data, data dependencies, quality risks, and future reporting requirements during planning.
Module 3: REDCap Database Architecture And Data Modeling
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Structuring REDCap projects around appropriate records, instruments, events, arms, and repeating data.
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Applying database modeling principles to reduce redundancy and improve consistency.
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Identifying relationships between participant information, visits, assessments, laboratory results, and study outcomes.
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Designing scalable database structures that support evolving research requirements and long-term maintenance.
Module 4: Data Elements, Variables And Data Dictionaries
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Defining variables, field names, labels, codes, response options, and standardized data elements.
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Developing REDCap data dictionaries and maintaining consistent metadata across research projects.
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Applying naming conventions, controlled vocabularies, coding structures, and standardized value sets.
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Documenting variable definitions, validation requirements, derivations, and relationships for reproducible research.
Module 5: REDCap Instrument And Form Design
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Designing data collection instruments for participant demographics, clinical assessments, outcomes, and study procedures.
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Selecting appropriate REDCap field types for different categories of research information.
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Designing efficient forms that minimize unnecessary data entry and improve usability.
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Applying structured form design principles to support accuracy, completeness, consistency, and downstream analysis.
Module 6: Logic, Validation And Automated Data Controls
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Designing branching logic to control the presentation of relevant fields and research questions.
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Configuring field validation for dates, numbers, identifiers, ranges, formats, and other structured values.
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Creating calculated fields for derived measures and automated research data processing.
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Testing logic, validation, calculations, and exception scenarios before production deployment.
Module 7: Longitudinal Studies, Events And Repeating Data
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Designing REDCap databases for longitudinal, cohort, clinical trial, registry, and follow-up studies.
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Configuring events, arms, visit schedules, and event-specific instruments.
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Managing repeating instruments and recurring observations without compromising database structure.
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Maintaining participant-level consistency across multiple study visits, assessments, and research time points.
Module 8: Research Data Quality Management In REDCap
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Defining research data quality dimensions including accuracy, completeness, consistency, validity, and timeliness.
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Creating data quality rules, queries, review procedures, and discrepancy management workflows.
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Identifying missing, inconsistent, duplicate, implausible, or potentially erroneous research records.
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Establishing continuous data review and quality improvement processes throughout the study lifecycle.
Module 9: Data Governance, Security And Privacy
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Establishing research data governance frameworks, responsibilities, policies, and operational procedures.
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Managing user roles, permissions, access levels, record-level controls, and least-privilege principles.
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Protecting confidential participant information through appropriate privacy and security practices.
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Using audit trails, documentation, and accountability mechanisms to support data integrity and responsible data management.
Module 10: Data Cleaning, Review And Database Lock
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Developing systematic REDCap data cleaning strategies before analysis and study completion.
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Managing data queries, corrections, discrepancies, and documentation of important database changes.
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Performing data review, reconciliation, completeness assessment, and final quality checks.
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Preparing REDCap databases for database lock, analysis, reporting, and controlled post-lock changes.
Module 11: Data Import, Export And Integration Management
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Importing structured datasets into REDCap while maintaining field mappings and data integrity.
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Exporting data for statistical analysis, reporting, visualization, archiving, and downstream processing.
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Reconciling data between REDCap and external laboratory, clinical, survey, or research systems.
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Managing transformations, mappings, provenance, and documentation during research data movement.
Module 12: Research Data Documentation, Metadata And Provenance
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Developing comprehensive documentation for REDCap projects, variables, instruments, workflows, and data processes.
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Maintaining metadata that explains data definitions, coding, relationships, transformations, and quality rules.
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Establishing data provenance and traceability from original collection through cleaning and analysis.
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Supporting reproducibility, handover, auditability, and long-term understanding of research databases.
Module 13: REDCap Reports, Dashboards And Data Monitoring
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Designing REDCap reports for investigators, coordinators, data managers, and research oversight teams.
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Developing dashboards for participant recruitment, follow-up, completion, missing data, and study progress.
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Using filters and data selections to support targeted data review and operational monitoring.
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Applying reporting outputs to identify quality trends, workflow bottlenecks, and emerging data management issues.
Module 14: REDCap APIs, Automation And Advanced Integration
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Understanding REDCap API concepts and programmatic interaction with research databases.
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Automating data extraction, imports, exports, metadata retrieval, record updates, and recurring workflows.
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Integrating REDCap with statistical software, analytical environments, laboratory systems, and external platforms.
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Managing API authentication, permissions, error handling, logging, and governance for automated data processes.
Module 15: Database Lifecycle Management, Change Control And Sustainability
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Managing development, testing, deployment, maintenance, modification, and retirement of REDCap databases.
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Applying structured change control, impact assessment, version management, and testing procedures.
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Developing database documentation and handover procedures to support continuity across research teams.
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Planning for project closure, data retention, archival, preservation, controlled access, and future reuse.
Module 16: Integrated REDCap Database Design And Research Data Management Practicum
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Developing a complete REDCap research database from a simulated research protocol and data management specification.
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Building instruments, variables, validation rules, branching logic, longitudinal structures, reports, and user roles.
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Performing data quality review, cleaning, documentation, reporting, and controlled data export on simulated study records.
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Presenting an integrated REDCap database and research data management strategy covering quality, governance, security, integration, 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.
