Monitoring and Evaluation for Community Projects Training Course

Duration 10 days 20 modules · 65 contact hours
Level Intermediate Certificate on completion
Who attends M&E Officers, Project Managers and others
Fee from USD 2,200 KSh 190,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 9 Nov 2026 Course code SD-ME-093

Before you join: Participants should have experience supporting, managing or reporting on a development, humanitarian, public-service or corporate social investment project.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Community projects often generate activity reports without answering the questions funders, community leaders and delivery teams need answered: Are intended households being reached fairly? Are services being used? What changes can reasonably be linked to the project? What should be changed before resources are exhausted? Weak baselines, vague indicators, unreliable community data and reporting designed only for compliance can leave managers unable to make timely decisions or demonstrate results credibly.

This course develops practical monitoring and evaluation systems for community-based development and humanitarian projects. Participants define results chains, develop theories of change and logframes, formulate SMART indicators, establish baselines and targets, prepare monitoring plans, select sampling approaches, design data collection tools and set up data quality controls. The course also covers participatory monitoring, disaggregation, safeguarding, ethical data collection, outcome evaluation designs, qualitative analysis, contribution analysis and reporting against OECD DAC evaluation criteria.

Teaching combines focused briefings with cases drawn from livelihoods, health, education, water, sanitation and local governance projects. Participants work with project documents, beneficiary records, survey instruments and monitoring datasets, then apply each method to a community project of their choice. They leave with a complete monitoring and evaluation package: a results framework, indicator reference sheets, data collection tools, a monitoring plan, a data quality checklist and an evaluation terms of reference.

The course is suited to programme staff who already contribute to project delivery, grants management, field coordination, research or reporting and now need to design or improve M&E arrangements. It is appropriate for staff working for NGOs, government agencies, donor-funded programmes, community-based organisations and corporate social investment teams.

What you will be able to do

  • Construct a theory of change and results chain for a community project
  • Develop a logical framework with measurable outputs, outcomes and assumptions
  • Formulate SMART indicators and complete indicator reference sheets
  • Prepare a costed monitoring plan with responsibilities, schedules and reporting routes
  • Design quantitative and qualitative data collection tools using ethical protocols
  • Apply sampling, data quality assessment and disaggregation methods to community datasets
  • Develop an evaluation terms of reference using OECD DAC criteria
  • Present a complete monitoring and evaluation package for management review

Benefits of attending

For you

  • Produce M&E plans that connect field data to management decisions
  • Write clearer indicators and targets for proposals and donor reports
  • Conduct beneficiary data collection with stronger ethical and safeguarding controls
  • Challenge weak findings using sampling and data quality principles
  • Lead evidence-based project reviews with community stakeholders and managers

For your organisation

  • Improve evidence available for implementation decisions and corrective action
  • Reduce reporting errors through defined indicators, tools and verification procedures
  • Strengthen donor confidence through traceable results and documented assumptions
  • Identify exclusion patterns through sex, age, disability and location disaggregation
  • Create reusable M&E templates for future community project design and reporting

Course modules

20 modules over 10 days

Participants can distinguish monitoring, evaluation, accountability and learning functions within a community project.

  • Monitoring, evaluation, accountability and learning functions distinguished through project decisions
  • Community project cycle linked to evidence requirements and decision points
  • Inputs, activities, outputs, outcomes and impacts separated accurately
  • Accountability relationships mapped among communities, implementers, donors and authorities
  • Results-based management principles applied to community project design
  • Common M&E failure modes diagnosed from incomplete field reporting

Participants can plan inclusive stakeholder engagement for monitoring and evaluation activities.

  • Stakeholder mapping identifies influence, interests, information needs and participation roles
  • Power analysis examines representation, gatekeepers and excluded community groups
  • Participation spectrum distinguishes informing, consulting, collaborating and community-led monitoring
  • Community entry protocols agreed with local leaders and representative groups
  • Feedback mechanisms designed for accessible two-way communication and response
  • Conflict sensitivity considerations incorporated into stakeholder engagement plans

Participants can translate a community problem analysis into a coherent results chain.

  • Problem trees identify root causes, effects and affected population groups
  • Objective trees convert priority problems into feasible desired changes
  • Results chains connect activities, outputs, outcomes and intended impact
  • Causal pathways tested against local service systems and household behaviours
  • Assumptions distinguished from project-controlled activities and expected results
  • External factors recorded with plausible effects on project achievement

Participants can develop and test a theory of change for a community intervention.

  • Theory of Change statements specify beneficiaries, changes and causal mechanisms
  • Backward mapping traces long-term outcomes to required intermediate changes
  • Preconditions identified for services, behaviours, institutions and social norms
  • Assumptions articulated using evidence, stakeholder insight and contextual analysis
  • Rival explanations considered before selecting priority monitoring questions
  • Visual theory of change diagrams prepared for project team review

Participants can build a logical framework aligned with a project theory of change.

  • Logical Framework Approach structures goals, outcomes, outputs and activities
  • Vertical logic checks whether planned outputs plausibly support intended outcomes
  • Objectively verifiable indicators assigned to each results level
  • Means of verification specified using feasible evidence sources
  • Assumptions and risks positioned at appropriate logical framework levels
  • Logframe narratives checked for clarity, attribution claims and feasibility

Participants can formulate useful indicators and document their measurement specifications.

  • SMART indicators distinguish measurable change from activity completion statements
  • Indicator types compared: input, process, output, outcome and impact
  • Numerators, denominators and calculation formulas defined without ambiguity
  • Indicator reference sheets record definitions, sources, frequency and responsibility
  • Proxy indicators selected where direct outcome measurement is impractical
  • Disaggregation dimensions specified by sex, age, disability and location

Participants can establish defensible baselines, targets and performance milestones.

  • Baseline studies determine starting values before major project implementation
  • Target setting uses historical data, feasibility assumptions and planned coverage
  • Milestones sequence expected progress across implementation reporting periods
  • Target assumptions documented for population movement, seasonality and service capacity
  • Benchmark data assessed for relevance, quality and comparability
  • Baseline gaps addressed through rapid assessments and secondary data review

Participants can prepare a monitoring plan that assigns data responsibilities and reporting schedules.

  • Monitoring plans link indicators, methods, sources, frequency and responsible staff
  • Data flow diagrams trace information from field collection to decisions
  • Reporting calendars align community, management and donor reporting requirements
  • Roles assigned using RACI matrices for collection, review and approval
  • Costed monitoring activities incorporated into project workplans and budgets
  • Escalation thresholds define when field findings require management action

Participants can select and design quantitative tools suitable for community monitoring.

  • Household surveys matched to indicators, respondent burden and literacy levels
  • Question wording avoids double-barrelled, leading and ambiguous questions
  • Response scales selected for frequency, agreement, satisfaction and knowledge
  • Skip patterns reduce irrelevant questions and data entry errors
  • Observation checklists capture service quality, infrastructure and behavioural practices
  • Pilot testing identifies timing, translation and enumerator instruction problems

Participants can use qualitative and participatory methods to investigate community experiences and change.

  • Focus group discussions explore shared experiences, norms and service perceptions
  • Key informant interviews gather informed perspectives from duty bearers
  • Most Significant Change captures participant-defined stories of change
  • Community scorecards structure dialogue between service users and providers
  • Transect walks document environmental conditions, access barriers and facilities
  • Facilitation plans reduce elite capture and encourage quieter participants

Participants can choose proportionate sampling approaches for monitoring and evaluation questions.

  • Census and sample approaches selected according to population size
  • Simple random sampling procedures protect against conscious selection bias
  • Systematic sampling uses intervals derived from ordered beneficiary lists
  • Stratified sampling ensures representation of priority demographic groups
  • Purposive sampling justified for qualitative interviews and case selection
  • Sample size trade-offs assessed against precision, time and available budget

Participants can apply ethical, safeguarding and data protection controls to M&E activities.

  • Informed consent procedures explain purpose, risks, choice and confidentiality
  • Child safeguarding protocols govern interviews, observations and referral actions
  • Do-no-harm screening identifies risks from questions, disclosure and participation
  • Data minimisation limits collection to information required for stated purposes
  • Personally identifiable information separated from analysis and reporting datasets
  • Referral pathways prepared for protection concerns identified during data collection

Participants can assess and improve the quality of monitoring data before reporting.

  • Data quality dimensions assess validity, reliability, precision, integrity and timeliness
  • Data Quality Assessment procedures test source documents and reported values
  • Verification checks compare registers, forms, databases and summary reports
  • Enumerator supervision plans include spot checks, back-checks and daily reviews
  • Missing values, duplicates and outliers identified using cleaning rules
  • Corrective action logs record errors, owners, deadlines and closure evidence

Participants can organise, analyse and interpret routine community project data.

  • Data dictionaries standardise variable names, labels, codes and permitted values
  • Data cleaning sequences address duplicates, missing data and inconsistent records
  • Frequency tables summarise coverage, participant characteristics and service uptake
  • Cross-tabulations compare results across sex, age, disability and location
  • Trend charts track indicator movement against targets and milestones
  • Interpretation notes distinguish observed patterns from untested causal claims

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

  • After-action reviews identify what happened, why and required adjustments
  • Pause-and-reflect sessions connect monitoring findings to operational decisions
  • Decision logs document evidence, choices, owners and follow-up dates
  • Learning questions focus inquiry on uncertain assumptions and delivery constraints
  • Feedback loops communicate actions taken in response to community concerns
  • Change control procedures update workplans, targets and monitoring arrangements

Participants can select evaluation approaches and frame questions using OECD DAC criteria.

  • OECD DAC criteria frame relevance, coherence, effectiveness, efficiency, impact and sustainability
  • Evaluation questions distinguish descriptive, normative and causal lines of inquiry
  • Formative evaluations support mid-course improvement and implementation decisions
  • Summative evaluations assess achieved results, value and future funding decisions
  • Process evaluations examine fidelity, reach, barriers and implementation quality
  • Evaluation matrices connect questions, indicators, methods, sources and analysis

Participants can assess plausible contribution to outcomes without overstating attribution.

  • Contribution analysis tests causal claims against evidence and alternative explanations
  • Outcome harvesting identifies observable changes before tracing project contributions
  • Before-and-after comparisons identify change while recognising design limitations
  • Comparison groups assessed for feasibility, ethics and similarity to participants
  • Triangulation combines monitoring records, survey data and qualitative evidence
  • Evidence strength ratings communicate confidence, limitations and remaining uncertainties

Participants can prepare decision-focused M&E reports for different stakeholder groups.

  • Results reports structure findings, interpretation, limitations and recommended actions
  • Indicator dashboards present targets, actuals, trends and status flags
  • Disaggregated findings reveal unequal reach, participation and outcome patterns
  • Data visualisations selected for comparisons, trends, proportions and geographic distribution
  • Community feedback summaries use clear language and locally meaningful formats
  • Management recommendations assign action owners, deadlines and evidence requirements

Participants can review an existing M&E system and prioritise practical improvements.

  • M&E capacity assessments examine people, processes, tools and governance
  • System bottlenecks identified from reporting delays, poor data quality and unused findings
  • Indicator rationalisation removes duplicative measures and reporting burden
  • Tool audits assess usability, accessibility, translation and data protection controls
  • Improvement roadmaps sequence quick fixes, resource needs and accountable owners
  • M&E governance arrangements define review forums, approvals and escalation routes

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

  • Project results framework aligns problem analysis, theory of change and logframe
  • Indicator reference sheets define measures, sources, formulas and disaggregation
  • Monitoring plan schedules collection, verification, analysis and reporting responsibilities
  • Data collection tools reflect sampling, ethics, safeguarding and accessibility requirements
  • Evaluation terms of reference specify questions, methods, governance and deliverables
  • Management presentation defends priorities, risks, costs and planned evidence use

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Community project theory of change template
  2. Logical framework and indicator reference sheet pack
  3. Costed monitoring plan template
  4. Household survey and focus group discussion guides
  5. Data Quality Assessment checklist
  6. Evaluation terms of reference template

Who should attend

  • M&E Officers — design indicators, tools and reporting systems for assigned projects
  • Project Managers — use evidence to adjust delivery, budgets and implementation priorities
  • Programme Coordinators — align monitoring arrangements across multiple community interventions
  • Field Officers — collect, verify and use beneficiary data responsibly
  • Grants and Reporting Staff — meet donor evidence requirements with credible results data
  • Government Development Officers — track local programme performance and community outcomes

Skills and tools covered

Results framework designIndicator developmentCommunity data collectionData quality assuranceParticipatory monitoringEvaluation planning

Tools and standards

Theory of Change, Logical Framework Approach, OECD DAC evaluation criteria, Data Quality Assessment

How the course runs

Training methodology

The course uses short instructor briefings followed by guided case work, group analysis, simulations and application planning. Participants examine community project scenarios, convert project descriptions into results frameworks, test indicators against real data constraints and review sample monitoring tools for bias and usability. Small groups conduct structured data quality assessments, interpret monitoring findings and prepare evaluation scopes. Each participant progressively builds materials for a selected project, receiving peer review and instructor feedback before presenting a completed M&E package.

Requirements and prerequisites

Participants should have experience supporting, managing or reporting on a development, humanitarian, public-service or corporate social investment project. They should understand basic project terms such as activities, outputs, beneficiaries, workplans and budgets, and be comfortable reading simple tables. Experience with routine data collection, surveys or donor reports is useful but not essential. No prior statistics qualification, specialist evaluation training, programming knowledge or advanced spreadsheet expertise is required. Participants should bring a current or recent community project idea, proposal or report where possible.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

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

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

Participants should understand how projects are planned and delivered, and should be familiar with basic terms such as activities, outputs and beneficiaries. They do not need prior evaluation training, advanced statistics or specialist software knowledge.

Bring a current or recent community project proposal, logframe, workplan, donor report or monitoring form if available. These materials help participants apply the methods to a realistic project and build a usable M&E package.

It is designed for M&E officers, project managers, programme coordinators, field officers, grants staff and government development personnel. It is particularly useful for staff moving from activity reporting into results measurement and evidence-based management.

Project management courses focus on planning, scheduling, budgets, risk and delivery control. This course focuses on defining results, collecting credible evidence, assessing change, engaging communities and using findings to improve project decisions.

Participants develop materials that can be adapted for active projects, including indicator sheets, monitoring plans, data collection tools and evaluation terms of reference. They also learn how to review weak data and raise practical corrective actions with project teams.

Each day runs from 08:30 to 16:30 and provides about 6.5 contact hours. The first part builds results frameworks and monitoring systems, while the later part covers data collection, quality, evaluation, reporting and a final applied capstone presentation.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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