Monitoring and Evaluation for Development Projects Training Course

Participants can identify M&E stakeholders, define information needs and assign governance responsibilities.

• Stakeholder mapping by influence, interest, information need and accountability relationship
• Primary users identified for management, learning, accountability and donor reporting
• RACI matrix allocating data collection, validation, analysis and approval duties
• Participation approaches for involving communities in monitoring and feedback processes
• Feedback loops linking field evidence, management decisions and community communication
• M&E governance arrangements for implementing partners and consortium projects

Participants can develop a testable theory of change with explicit pathways, assumptions and risks.

• Theory of Change pathways linking interventions to intended behavioural change
• Backward mapping from impact statements to preconditions and activities
• Assumptions articulated as conditions required for causal pathways
• External risks differentiated from programme assumptions and implementation constraints
• Causal links tested using evidence, stakeholder knowledge and plausibility
• Equity considerations incorporated for gender, disability, age and marginalised groups

Participants can convert a theory of change into a coherent logical framework.

• Logical Framework Approach structure: goal, outcome, outputs, activities and inputs
• Vertical logic checked against causal pathways and programme scope
• Narrative statements written with specific populations, changes and timeframes
• Objectively verifiable indicators matched to each results level
• Means of verification specified before data collection systems are selected
• Assumptions and risks recorded at relevant levels of the logframe

Participants can specify valid, feasible indicators and document them for consistent use.

• SMART criteria applied to output, outcome and impact indicators
• Indicator reference sheets defining numerator, denominator, unit and calculation
• Disaggregation requirements specified by sex, age, disability, location and status
• Proxy indicators selected when direct measurement is impractical or unethical
• Indicator targets set using baselines, benchmarks, capacity and context
• Leading and lagging indicators balanced for timely management action

Participants can establish baseline requirements, set defensible targets and select data sources.

• Baseline study purposes, timing, scope and limitations for project measurement
• Secondary data appraisal using source credibility, relevance, comparability and recency
• Target-setting methods using trends, service standards, benchmarks and scenario assumptions
• Administrative records assessed for completeness, timeliness and definitional consistency
• Data source matrix matching indicators with collection methods and frequency
• Baseline gaps addressed through rapid assessments, records review or phased measurement

Participants can create an operational monitoring plan that teams can implement.

• Monitoring plan fields: indicators, sources, frequency, responsibilities and reporting dates
• Data collection workflow from field capture through validation and aggregation
• Reporting calendars aligned with workplans, partner schedules and donor deadlines
• Digital and paper collection methods selected for access and reliability
• Resource estimates covering enumerators, travel, devices, translation and supervision
• Escalation procedures defined for missed targets, anomalies and safeguarding concerns

Participants can assess and improve the reliability, integrity and usability of monitoring data.

• Data-quality dimensions: validity, reliability, precision, integrity and timeliness
• Verification protocols comparing source documents, registers, reports and databases
• Spot checks, recounts and field observation used to detect discrepancies
• Data cleaning rules for duplicates, missing values, outliers and invalid codes
• Audit trails maintained through version control, corrections and approval records
• Data Quality Assessment checklist applied to partner reporting systems

Participants can plan and interpret basic quantitative monitoring and evaluation data.

• Survey question formats matched to indicators, respondents and recall periods
• Sampling frames defined from populations, lists, locations and eligibility criteria
• Probability and non-probability sampling methods compared for stated purposes
• Sample-size considerations: precision, variability, resources and expected non-response
• Descriptive statistics using frequencies, percentages, means, medians and disaggregation
• Cross-tabulation interpreted without overstating association, attribution or causation

Participants can select, conduct and analyse qualitative inquiry for project learning and evaluation.

• Key informant interviews designed around expertise, role and decision relevance
• Focus group discussions structured for participation, inclusion and manageable group dynamics
• Observation checklists capturing service conditions, behaviours and process quality
• Participatory tools: community mapping, ranking, seasonal calendars and transect walks
• Thematic coding applied to interview notes, transcripts and open responses
• Triangulation compares qualitative evidence, monitoring data and documentary sources

Participants can frame evaluation questions and choose designs proportionate to intended use.

• Evaluation purpose defined for accountability, learning, decision-making or programme improvement
• Evaluation questions framed using OECD DAC criteria and stakeholder priorities
• Process, outcome, impact and formative evaluations distinguished by intended use
• Evaluation matrix linking questions, indicators, sources, methods and analysis
• Experimental, quasi-experimental and non-experimental designs compared for feasibility
• Contribution analysis used where attribution cannot be credibly established

Participants can make defensible sampling and evidence judgements for an evaluation.

• Purposive sampling criteria documented for qualitative participant selection
• Stratified sampling used to represent priority groups and geographic areas
• Comparison groups assessed for similarity, ethics and practical feasibility
• Confidence intervals interpreted as uncertainty around survey estimates
• Evidence strength judged through triangulation, bias assessment and alternative explanations
• Findings separated from conclusions, recommendations and management responses

Participants can plan M&E activities that protect participants, data and affected communities.

• Informed consent procedures matched to literacy, language, age and risk
• Do-no-harm assessment identifies foreseeable risks from questions and data collection
• Safeguarding referral protocols prepared before engaging children or vulnerable adults
• Confidentiality controls for identifiable data, case narratives and sensitive findings
• Data minimisation and retention schedules applied to project information
• Enumerator conduct standards address power, coercion, privacy and respectful communication

Participants can communicate M&E findings clearly and connect them to management action.

• Results reports distinguish activities completed from outputs delivered and outcomes achieved
• Dashboard measures selected for management decisions rather than reporting volume
• Tables and charts designed with clear labels, denominators and disaggregation
• Data stories combine quantitative trends, qualitative explanation and operational implications
• Recommendation criteria specify priority, owner, timeframe and required resources
• Management response plans track decisions, actions and follow-up evidence

Participants can use monitoring evidence to support structured adaptation and learning.

• Pause-and-reflect sessions convert monitoring evidence into management decisions
• After-action reviews identify intended results, actual results and contributing factors
• Decision logs record adaptations, rationale, approvals and expected consequences
• Learning agendas prioritise unanswered assumptions and high-value evidence needs
• Indicator reviews retire measures that no longer inform decisions
• Change-control procedures maintain traceability when project designs are revised

Participants can prepare clear evaluation terms of reference and manage an evaluation process.

• Terms of reference specify purpose, scope, questions, methods, deliverables and timelines
• Evaluator selection criteria assess sector expertise, methods, independence and safeguarding
• Inception reports reviewed for methodology, sampling, risks and workplan quality
• Fieldwork oversight protects independence while resolving access and operational issues
• Draft reports quality-assured against evidence, logic, clarity and actionable recommendations
• Dissemination plans identify audiences, products, timing and response arrangements

Participants can build, review and present a complete M&E package for a development project.

• Project brief translated into a results chain and Theory of Change
• Logical framework completed with indicators, sources, assumptions and targets
• Indicator reference sheets prepared for priority outcome and output measures
• Monitoring plan costed with roles, schedules, data flows and quality checks
• Evaluation outline developed with questions, methods, sampling and ethical safeguards
• Peer review tests feasibility, coherence, data quality and decision usefulness
• Final presentation defends the M&E package before management and donor stakeholders