Monitoring and Evaluation for Land Governance Programmes Training Course

Participants can structure a land governance intervention into a clear and testable results framework.

• Problem trees distinguish tenure constraints, drivers, consequences and affected groups
• Theory of change links interventions, assumptions, risks and outcomes
• Results chains separate inputs, activities, outputs, outcomes and impacts
• Assumption testing identifies conditions outside programme control or influence
• Contribution analysis tests plausible influence amid policy and market changes
• Rights-holder pathways specify changes for women, youth and pastoralists

Participants can specify measurable indicators that reflect land rights, inclusion and institutional performance.

• Indicator reference sheets define purpose, unit, disaggregation, source and frequency
• SMART and CREAM criteria test indicator usefulness and feasibility
• Rights-based indicators measure security, participation, non-discrimination and remedy
• Proxy indicators address unobservable tenure security and perceived protection
• Disaggregation plans cover sex, age, disability, tenure type and location
• Indicator baskets balance administrative, survey, geospatial and qualitative evidence
• Service indicators measure cost, processing time, accessibility and client satisfaction

Participants can plan credible baseline and follow-up studies for land governance results.

• Baseline protocols establish denominators, benchmarks, sampling frames and comparison logic
• Probability sampling selects households, parcels or communities with known inclusion chances
• Sample-size assumptions address precision, design effect, non-response and subgroup analysis
• Questionnaire design reduces recall bias, social desirability bias and respondent burden
• Focus groups and key-informant interviews surface tenure norms and exclusion
• Participatory mapping documents claims, boundaries, seasonal access and contested areas
• Informed consent procedures address risks of disclosure, retaliation and false expectations

Participants can establish practical monitoring workflows, controls and data-quality checks.

• Data collection workflows connect enumerator assignments, consent, validation and escalation
• KoboToolbox forms apply skip logic, constraints, required fields and timestamps
• Data quality assessments examine accuracy, completeness, timeliness, integrity and confidentiality
• Spot checks, back checks and supervisor reviews detect fabrication and errors
• Data cleaning logs preserve original values, corrections, reasons and approvals
• Role-based access controls protect claimant identities, coordinates and case records
• Standard operating procedures define reporting deadlines, responsibilities and exception handling

Participants can develop evaluation questions and methods appropriate to land governance interventions.

• OECD DAC criteria frame relevance, coherence, effectiveness, efficiency, impact and sustainability
• Evaluation questions align intended users, decisions, criteria and evidence needs
• Quasi-experimental options compare matched groups, before-after trends and exposure levels
• Process evaluation examines implementation fidelity, reach, adaptations and bottlenecks
• Mixed-methods triangulation reconciles administrative data, surveys, interviews and spatial evidence
• Sampling for qualitative inquiry pursues diversity, saturation and contested perspectives
• Evaluation terms of reference specify scope, governance, methods, ethics and deliverables

Participants can use administrative and spatial data responsibly within an M&E system.

• Parcel-level monitoring links registry updates, adjudication stages, claims and service times
• GIS layers compare land-use change, boundaries, infrastructure and protected areas
• Remote-sensing interpretation identifies settlement expansion, cultivation change and encroachment patterns
• Case-management data tracks complaints, dispute resolution, appeals and compliance
• Spatial data validation checks projections, coordinates, topology and attribute completeness
• Privacy safeguards manage publication risks from household, parcel and indigenous data
• Data interoperability maps identifiers across registry, survey, grievance and finance systems

Participants can interpret monitoring and evaluation evidence to assess results and distributional effects.

• Descriptive statistics summarize registration coverage, processing times, costs and dispute rates
• Equity analysis compares outcome gaps by sex, tenure status and location
• Before-after comparisons distinguish change from seasonal variation and measurement differences
• Outcome harvesting documents unexpected changes, contribution claims and supporting evidence
• Unintended-effects analysis identifies displacement, elite capture, conflict and exclusion risks
• Confidence intervals communicate uncertainty around survey estimates and subgroup comparisons
• Triangulation matrices compare convergence, divergence and evidence gaps across sources

Participants can communicate findings clearly and convert evidence into accountable programme improvements.

• Dashboards pair decision thresholds with disaggregated indicators and narrative interpretation
• Evidence briefs state findings, limitations, recommendations, owners and due dates
• Adaptive management cycles convert monitoring findings into decisions, actions and follow-up
• Learning agendas prioritize uncertainties, evidence gaps, questions and decision windows
• Management response matrices assign actions, owners, deadlines and verification sources
• Community feedback mechanisms close loops on complaints, findings and programme changes
• Donor reporting calendars align indicator updates, evaluations, budgets and governance meetings

Participants can assemble a practical M&E design pack and prioritise immediate improvements for their programme.

• Results framework consolidates outcomes, outputs, assumptions, risks and accountabilities
• Indicator reference sheets document definitions, disaggregation, sources and calculation methods
• Data collection plans sequence baseline, routine monitoring, reviews and evaluation milestones
• Evaluation matrix links questions, indicators, sources, methods and analytical approaches
• Data quality checklists assign controls for collection, storage, analysis and reporting
• Application plans schedule first monitoring and evaluation improvements after return