REDCap Database Management for Research Monitoring Training Course

Course overview

Programme monitoring teams often collect data in REDCap but struggle to turn individual project databases into controlled, auditable systems that support timely decisions. Common problems include inconsistent indicator definitions, incomplete records from field sites, unmanaged user access, duplicate participant entries, weak audit trails, and exports that require extensive cleaning before a donor report or management review. For development and humanitarian programmes, these weaknesses can delay corrective action, compromise safeguarding-sensitive data, and reduce confidence in reported results.

This five-day course equips participants to manage REDCap databases for rigorous monitoring and evaluation workflows. Participants configure longitudinal projects, instruments, events, repeating forms, calculated fields, branching logic, validation rules, data quality rules, record status dashboards, user rights, Data Access Groups, survey settings, and audit logging. They learn to translate logframes and indicator reference sheets into practical REDCap structures; establish routines for field-data review; manage query resolution; prepare de-identified exports; and connect REDCap data to Excel, Power BI, and DHIS2 reporting processes.

Instruction is built around a realistic multi-site humanitarian programme case. Each participant works in a REDCap training environment to build and administer a monitoring database, test field scenarios, identify data-quality failures, assign access controls, and create a documented monitoring workflow. They leave with a configured REDCap project template, a data management and quality assurance plan, an indicator-to-variable mapping sheet, and an implementation checklist that can be adapted for their own programme.

The course is designed for M&E professionals, data managers, programme quality staff, research coordinators, and digital data leads who already work with project monitoring data and need stronger control over REDCap configuration, data quality, and reporting readiness.

Course objectives

By the end of this course, participants will be able to:

• Configure a REDCap project structure using instruments, events, arms, repeating forms, and record identifiers for a multi-site monitoring programme
• Translate a logframe and indicator reference sheet into REDCap variables, field labels, validation rules, calculations, and coding conventions
• Build conditional data-entry workflows using branching logic, action tags, required fields, and automated survey invitations
• Implement data-quality controls through REDCap Data Quality rules, custom discrepancy checks, duplicate-record review, and query logs
• Manage role-based access using User Rights, Data Access Groups, instrument permissions, and audit-log review procedures
• Produce clean, de-identified monitoring datasets and reproducible codebooks for Excel, Power BI, and DHIS2 reporting workflows
• Design a routine data-quality assurance plan covering completeness, timeliness, consistency, outliers, correction authority, and escalation paths
• Document a REDCap database administration pack containing a data dictionary, user-access matrix, monitoring workflow, and change-control log

Benefits of attending

Target competencies

Who should attend

• Monitoring and Evaluation Officers — who need reliable programme data for indicator tracking, learning reviews, and donor reporting
• REDCap Database Administrators — who configure projects and must control user access, data quality, and documented changes
• Research Coordinators — who manage multi-site studies or assessments requiring traceable, validated participant and outcome data
• Programme Quality Managers — who need consistent monitoring systems across field offices, partners, and implementation teams
• Data Managers — who prepare REDCap exports for analysis, dashboards, and reporting while protecting sensitive data
• Humanitarian Information Management Officers — who need to connect field-data collection workflows with operational reporting and decision-making

Requirements and prerequisites

Participants should already understand basic monitoring and evaluation concepts, including indicators, targets, disaggregation, data collection tools, and routine data-quality checks. Experience entering, reviewing, or exporting data from REDCap is strongly recommended; participants should be comfortable navigating projects, records, instruments, and reports. Familiarity with Excel spreadsheets, filters, formulas, and CSV files is assumed. Participants do not need programming skills, prior API experience, Power BI expertise, database administration qualifications, or advanced statistical knowledge. The course teaches REDCap configuration and governance rather than introductory M&E theory or statistical analysis.

Training methodology

The course combines instructor-led demonstrations in REDCap with guided configuration labs, small-group design reviews, and scenario-based troubleshooting. Participants work through a multi-site humanitarian monitoring case, converting indicators into a data dictionary, configuring instruments and user roles, testing records, and investigating quality-rule exceptions. Daily exercises produce working components of a database administration pack. On the final day, participants review one another’s governance and reporting workflows, then complete an application plan identifying the REDCap changes, stakeholders, controls, and first 30-day actions for their own programme.

Course outline

Day 1: Designing REDCap for programme monitoring

• REDCap project lifecycle for development and humanitarian monitoring systems
• Converting logframes, indicator reference sheets, and disaggregation plans into data requirements
• Project purpose, record identifiers, naming conventions, and database documentation standards
• Instrument design for household, beneficiary, service-delivery, and partner-reporting data
• Field types, coded response options, matrix fields, descriptive fields, and field annotations
• Data validation for dates, numbers, identifiers, geographic fields, and contact information
• Data dictionaries, version control, and change-control principles for REDCap builds

Workshop: Participants convert a selected programme indicator set into a REDCap data dictionary and build the first monitoring instrument.

Day 2: Building controlled data-entry workflows

• Longitudinal projects, events, arms, and event-to-instrument mapping
• Repeating instruments and repeating events for follow-up monitoring cycles
• Branching logic for eligibility, referral, service uptake, and conditional indicator questions
• Calculated fields, date-difference calculations, and automated scoring methods
• Action tags for field visibility, read-only controls, hidden calculations, and default values
• Required-field design and prevention of invalid or incomplete submissions
• Survey settings, automated invitations, participant links, and response management

Workshop: Participants configure a longitudinal beneficiary-monitoring workflow with repeat visits, conditional questions, calculations, and a test survey.

Day 3: Data quality monitoring and query management

• Routine data-quality dimensions: completeness, timeliness, validity, consistency, and uniqueness
• REDCap Data Quality module rules and custom logic-based discrepancy checks
• Missing-data codes, unknown values, skipped questions, and non-applicable responses
• Duplicate-record detection using identifiers, matching fields, and review procedures
• Outlier checks for quantities, ages, dates, targets, and service-delivery values
• Data resolution workflows using field comments, query logs, correction authority, and escalation
• Record status dashboards, reports, and reviewer queues for field-data follow-up

Workshop: Participants run data-quality rules against a flawed monitoring dataset, log discrepancies, and produce a corrective-action tracker.

Day 4: Access control, governance, and secure data handling

• User Rights configuration for data entry, design, exports, reports, surveys, and API functions
• Data Access Groups for country offices, implementing partners, and geographically separated field teams
• Role-based permission matrices and least-privilege access design
• Audit Log review for record changes, exports, user activity, and configuration amendments
• De-identification, identifier removal, date shifting, and controlled export settings
• Sensitive-data handling for protection, health, safeguarding, and vulnerable-population records
• Database governance documents: ownership, approval routes, retention, backup, and closure procedures

Workshop: Participants create a user-access matrix, configure Data Access Groups, and investigate an audit-log scenario involving an unauthorised change.

Day 5: Reporting readiness and operational implementation

• REDCap reports for completeness tracking, exception lists, and programme review meetings
• Exporting analysis-ready datasets, labels, codebooks, and metadata
• Excel cleaning checks, lookup tables, pivot summaries, and reconciliation workflows
• Power BI data preparation and dashboard measures from REDCap monitoring exports
• DHIS2 alignment considerations for aggregate indicators, reporting periods, and data elements
• REDCap API use cases, token governance, automated extracts, and integration controls
• Implementation planning for database launch, field orientation, support, review cadence, and continuous improvement

Workshop: Participants present their completed REDCap monitoring database administration pack and produce a 30-day implementation plan for their workplace.

Tools & standards covered

REDCap, Microsoft Excel, Microsoft Power BI, DHIS2

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions