Results-Based Monitoring and Evaluation Training Course

Participants can define a programme problem, affected groups and relevant stakeholder interests.

• Problem tree analysis separating causes, effects and core development problems
• Stakeholder mapping by influence, interest, roles and information needs
• Disaggregating affected populations by sex, age, disability and location
• Power analysis identifying exclusion, gatekeepers and decision-making constraints
• Evidence sources for validating needs assessments and problem statements
• Turning a problem tree into a preliminary objective structure

Participants can create a testable theory of change with explicit causal pathways and assumptions.

• Theory of Change components: actors, pathways, assumptions and intended change
• Backward mapping from long-term outcomes to required intermediate outcomes
• Causal links tested against evidence, experience and contextual conditions
• Assumptions classified as operational, behavioural, institutional or political
• Contribution logic distinguished from direct attribution claims
• Narrative theory statements connecting intervention choices to expected results

Participants can translate a theory of change into a coherent results chain and logframe.

• Results chain construction from inputs through outputs, outcomes and impact
• Logical Framework Approach matrix: hierarchy, indicators, verification and assumptions
• Vertical logic checks for plausible cause-and-effect relationships
• Horizontal logic linking indicators with sources and collection methods
• Outcome statements written as observable changes rather than delivered activities
• Risk assumptions reviewed for likelihood, consequence and management action

Participants can formulate relevant, measurable and decision-useful programme indicators.

• Indicator selection criteria: validity, reliability, feasibility, sensitivity and utility
• SMART indicators written for outputs, outcomes and impact levels
• Quantity, quality, timeliness, coverage and equity measurement dimensions
• Proxy indicators chosen when direct outcome measurement remains impractical
• Disaggregation requirements specified by population group, geography and vulnerability
• Leading and lagging indicators used to monitor implementation progress

Participants can document indicators so different staff collect and interpret data consistently.

• Indicator reference sheet fields: definition, purpose, formula and disaggregation
• Numerator and denominator rules for percentage-based performance indicators
• Data source, frequency, responsibility and reporting schedule specification
• Inclusion and exclusion criteria preventing inconsistent indicator counting
• Indicator limitations, biases and interpretation cautions documented clearly
• Worked calculations using service coverage and beneficiary outcome measures

Participants can establish credible baselines, targets and milestones for programme results.

• Baseline studies designed around indicator definitions and decision requirements
• Secondary data appraisal for relevance, recency, coverage and credibility
• Target-setting methods using historical trends, benchmarks and implementation capacity
• Annual milestones sequenced against seasonal, operational and policy constraints
• Targets adjusted after documented scope changes or revised baseline findings
• Target trajectories displayed through disaggregated performance monitoring tables

Participants can develop a monitoring plan that assigns data responsibilities and reporting cycles.

• Monitoring plan linking indicators, methods, frequencies, owners and users
• Data flow mapping from field collection to management decision meetings
• Reporting calendars aligned with workplans, donor deadlines and review forums
• Roles defined through RACI matrices for collection, approval and analysis
• Routine monitoring integrated with supervision, partner reporting and community feedback
• Escalation procedures for missed targets, data gaps and safeguarding concerns

Participants can select and specify quantitative data collection methods suited to indicator requirements.

• Structured questionnaires designed from indicator definitions and measurement concepts
• Closed-response question formats: binary, categorical, ordinal and numeric scales
• Recall periods selected to reduce respondent memory error
• Skip logic and validation rules programmed for mobile data collection
• Administrative records assessed for completeness, duplication and reporting incentives
• Enumerator instructions standardised through field protocols and interviewer guides

Participants can plan qualitative inquiry and participatory methods to explain programme results.

• Key informant interviews structured around experience, change and causal explanations
• Focus group discussions designed for safe participation and diverse perspectives
• Most Significant Change technique capturing participant-defined programme effects
• Outcome harvesting identifies observed changes before tracing contribution pathways
• Community scorecards gather service-user ratings and provider response commitments
• Qualitative coding frameworks convert narratives into analysable evidence themes

Participants can prepare proportionate sampling and fieldwork plans for monitoring and evaluation studies.

• Census and sample approaches selected against population size and purpose
• Probability sampling: simple random, systematic, stratified and cluster methods
• Purposive sampling used for key informants, cases and underrepresented groups
• Sample size considerations: precision, confidence, resources and anticipated nonresponse
• Fieldwork schedules balancing travel, supervision, safety and respondent availability
• Nonresponse tracking and replacement rules documented before field deployment

Participants can assess, document and improve the quality of monitoring data.

• Data quality dimensions: validity, reliability, precision, integrity and timeliness
• Data Quality Assessment procedures comparing reports with source documentation
• Verification checks for completeness, outliers, duplicates and calculation errors
• Spot checks, back-checks and supervisory reviews during field collection
• Data cleaning logs documenting corrections, exclusions and unresolved anomalies
• Corrective action plans addressing recurring data quality weaknesses

Participants can manage M&E data with appropriate protection, confidentiality and ethical controls.

• Data management plans covering storage, access, retention and destruction
• Informed consent processes matched to study purpose and participant risk
• Personally identifiable information minimised through purpose-limited data collection
• Confidentiality procedures for sensitive protection, health and safeguarding information
• Do-no-harm assessment applied to monitoring and evaluation activities
• Incident reporting procedures for breaches, coercion and participant distress

Participants can analyse routine quantitative data and communicate performance patterns accurately.

• Descriptive statistics: counts, proportions, means, medians and rate calculations
• Disaggregated analysis revealing differences by sex, age, location and disability
• Trend analysis comparing actual performance against targets and prior periods
• Cross-tabulations used to examine relationships between participant characteristics and outcomes
• Charts selected for comparisons, trends, distributions and target progress
• Data visualisation checks prevent misleading scales, totals and percentage claims

Participants can interpret qualitative evidence and combine it with other evidence sources.

• Thematic analysis coding interview, focus group and observation data
• Codebooks define categories, inclusion rules and illustrative evidence
• Triangulation compares findings across methods, respondent groups and data sources
• Negative case analysis tests explanations against contradictory participant evidence
• Evidence matrices connect findings with questions, sources and confidence assessments
• Interpretive claims separated from direct quotations and observed facts

Participants can design an evaluation approach aligned with intended use and OECD DAC criteria.

• Evaluation purpose defined for accountability, learning, scale-up or policy decisions
• OECD DAC criteria applied: relevance, coherence, effectiveness, efficiency, impact, sustainability
• Evaluation questions developed from programme theory, risks and stakeholder priorities
• Experimental, quasi-experimental and non-experimental designs compared for feasibility
• Contribution analysis examines plausible causal contribution without counterfactual estimation
• Evaluation matrix links questions, indicators, methods, sources and analysis plans

Participants can prepare terms of reference and manage a credible evaluation process.

• Terms of reference specify scope, questions, methods, deliverables and governance
• Evaluator selection criteria assess technical competence, independence and contextual knowledge
• Inception reports reviewed for design coherence, feasibility and ethical safeguards
• Quality assurance standards applied to draft findings, conclusions and recommendations
• Stakeholder validation sessions test factual accuracy and interpretation credibility
• Management responses assign actions, owners, deadlines and follow-up mechanisms

Participants can convert M&E findings into decision-ready reports and adaptation actions.

• Performance review meetings structured around evidence, decisions and accountability actions
• Traffic-light dashboards distinguish attention priorities from routine performance variation
• After-action reviews identify implementation lessons following significant delivery events
• Learning questions guide evidence collection for uncertain programme assumptions
• Results reports combine indicator trends, explanations, risks and corrective actions
• Recommendation tracking monitors management commitments through closure and verification

Participants can adapt RBME methods to common development and humanitarian operating conditions.

• Humanitarian response monitoring tracks reach, timeliness, quality and community feedback
• Post-distribution monitoring assesses assistance use, access and unintended effects
• Education programme indicators measure attendance, retention, learning and inclusion
• Livelihoods monitoring tracks asset recovery, income sources and coping strategies
• Public health indicators distinguish service utilisation, coverage and behavioural outcomes
• Social protection monitoring examines payment reliability, grievance handling and exclusion

Participants can build, review and present a complete results-based monitoring and evaluation package for a programme.

• Programme problem, stakeholder analysis and Theory of Change consolidated
• Results chain and logframe checked for vertical and horizontal logic
• Indicator reference sheets completed for priority output and outcome measures
• Baseline, targets, sampling and data collection methods aligned
• Data quality, ethics, reporting and evaluation arrangements incorporated
• Peer review assesses feasibility, decision-use and evidence credibility