Monitoring and Evaluation Training Course

Participants can map a programme results chain that shows plausible pathways from activities to impact.

• Results chains linking activities, outputs, outcomes and intended impact
• Distinguishing deliverables, behavioural changes, systems changes and population-level effects
• Vertical logic testing for plausible progression between programme result levels
• Assumptions stated as external conditions required for results progression
• Risk analysis separating operational threats from results-pathway assumptions
• Contribution logic for multi-actor programmes with shared outcome responsibility

Participants can develop a theory of change with causal pathways, assumptions, actors and evidence needs.

• Theory of Change components: pathways, actors, assumptions, interventions and outcomes
• Backward mapping from long-term change to near-term preconditions
• Causal links tested through evidence, expertise and stakeholder perspectives
• Power analysis identifying groups influencing access, adoption and outcomes
• Equity considerations embedded within pathways for women, children and marginalised groups
• Evidence gaps converted into priority monitoring and evaluation questions

Participants can build a logical framework with coherent results, indicators, assumptions and verification sources.

• Logframe hierarchy connecting goal, outcome, output and activity statements
• Objectively verifiable indicators aligned with each intended result
• Means of verification matched to feasible data sources
• Assumptions and risks recorded at appropriate levels of logic
• Narrative statements written with population, change and timeframe specificity
• Logframe quality checks for vertical coherence and measurable results

Participants can specify valid indicators and document their measurement requirements in reference sheets.

• Indicator selection criteria: validity, reliability, feasibility, sensitivity and utility
• SMART indicator construction using numerator, denominator, unit and timeframe
• Output, outcome and impact indicators matched to results statements
• Indicator reference sheets defining purpose, method, source and frequency
• Proxy indicators used when direct measurement is impractical or unethical
• Indicator disaggregation by sex, age, disability, location and relevant characteristics

Participants can establish credible baselines and targets for tracking programme performance over time.

• Baseline values distinguished from contextual benchmarks and administrative starting points
• Target-setting methods using trend data, capacity, coverage and programme ambition
• Annual milestones sequenced against implementation assumptions and seasonal constraints
• Target revisions documented with evidence, approval authority and audit trail
• Performance measurement tables linking indicators, baselines, targets and responsibilities
• Traffic-light performance ratings interpreted alongside narrative evidence and risks

Participants can create a practical monitoring plan that assigns data responsibilities, timing and use.

• Monitoring plan fields covering indicators, sources, frequency, roles and users
• Data-flow mapping from frontline collection through validation to reporting
• Routine monitoring schedules aligned with workplans, reporting cycles and decisions
• Partner reporting requirements calibrated to capacity and data-use needs
• Data collection burden assessed against decision value and respondent time
• Escalation procedures for late, incomplete, inconsistent or sensitive data

Participants can select data-collection methods and draft instruments aligned with indicator definitions.

• Structured questionnaires designed from indicator definitions and response categories
• Key informant interviews for technical insight, implementation barriers and institutional change
• Focus group discussions planned around participant composition and discussion guides
• Observation checklists measuring service quality, infrastructure and process compliance
• Administrative records assessed for completeness, consistency and collection incentives
• Question wording tested for recall periods, ambiguity, translation and response bias

Participants can select defensible quantitative sampling approaches and interpret common measurement errors.

• Census, probability and non-probability sampling matched to evaluation purposes
• Simple random, systematic, stratified and cluster sampling compared
• Sample-size factors including precision, confidence, design effect and non-response
• Sampling frames checked for coverage errors, duplicates and outdated records
• Measurement scales: nominal, ordinal, interval and ratio data
• Common quantitative errors: selection bias, recall bias and social desirability bias

Participants can design qualitative and mixed-methods approaches that answer explanatory evaluation questions.

• Purposive sampling strategies for information-rich participants and contrasting cases
• Semi-structured interview guides linked to evaluation questions and assumptions
• Thematic coding using deductive codes, inductive codes and codebooks
• Triangulation across methods, sources, investigators and time periods
• Mixed-methods designs integrating quantitative patterns with qualitative explanation
• Qualitative quality criteria: credibility, dependability, confirmability and transferability

Participants can assess data quality and establish controls for secure, traceable programme data.

• Data Quality Assessment dimensions: validity, reliability, precision, integrity and timeliness
• Source document verification against reported totals and indicator calculations
• Field supervision protocols for enumerator checks, spot checks and back-checks
• Data-cleaning rules for missing values, duplicates, outliers and range errors
• Version control for datasets, tools, indicator definitions and reporting files
• Data protection measures covering consent, access controls, retention and disposal

Participants can apply ethical safeguards to M&E activities involving affected populations and sensitive information.

• Informed consent procedures explaining purpose, risks, voluntariness and withdrawal
• Do-no-harm assessment for data collection involving vulnerable populations
• Safeguarding referral protocols when disclosures indicate protection concerns
• Confidentiality protections for identifiable, sensitive and politically risky information
• Community feedback mechanisms documenting response, resolution and closure
• Ethical review thresholds for research, evaluation and routine monitoring activities

Participants can frame an evaluation design using clear questions, criteria, methods and governance arrangements.

• Evaluation scope defined by purpose, users, decisions and resource constraints
• OECD DAC criteria: relevance, coherence, effectiveness, efficiency, impact and sustainability
• Evaluation questions structured around criteria, assumptions and evidence requirements
• Design options: experimental, quasi-experimental, theory-based and case-study approaches
• Attribution distinguished from contribution in complex programme settings
• Evaluation governance covering independence, stakeholder participation and conflict management

Participants can plan comparative measurement studies that support credible assessment of change over time.

• Baseline study objectives aligned with indicators, target setting and evaluation design
• Repeated cross-sectional surveys compared with panel and cohort designs
• Comparison groups selected through matching, phased rollout or natural variation
• Questionnaire consistency maintained across baseline, midline and endline rounds
• Fieldwork plans covering recruitment, training, piloting, supervision and logistics
• Study limitations documented before interpreting measured change or programme contribution

Participants can analyse M&E evidence and communicate results through accurate management-focused reporting.

• Descriptive statistics using counts, proportions, means, medians and rates
• Cross-tabulations examining differences by sex, age, location and participant group
• Trend analysis comparing actual performance against baseline, targets and milestones
• Qualitative matrices organising evidence by question, theme, source and finding
• Data visualisation principles for charts, tables, maps and performance dashboards
• Findings, conclusions and recommendations distinguished through evidence and judgement

Participants can convert monitoring and evaluation evidence into documented management actions and learning products.

• After-action reviews structured around expected results, evidence and corrective actions
• Pause-and-reflect sessions converting implementation evidence into decision records
• Management response matrices assigning actions, owners, deadlines and status
• Donor reports connecting indicator performance, explanation, risk and next steps
• Learning products tailored for programme teams, partners, communities and funders
• Knowledge management routines for storing, retrieving and reusing M&E evidence

Participants can build, review and present a complete M&E package for a realistic development or humanitarian programme.

• Programme results framework integrating Theory of Change and logical framework
• Indicator reference sheets completed for priority output and outcome measures
• Costed monitoring plan assigning methods, frequency, owners and data users
• Baseline or evaluation design matched to priority evidence questions
• Data quality, ethics and safeguarding controls incorporated into implementation arrangements
• Management dashboard and learning loop translating evidence into programme decisions