Monitoring and Evaluation Course

Lesson 1 • The Results Chain and Logic Model

Explains how programme logic links resources to intended change through a results chain. Students construct a basic logic model for a sample social project.

Lesson 2 • What M&E Is and Why It Matters

Defines monitoring, evaluation, and their distinction from auditing and research. Establishes the rationale for M&E as a management and accountability tool.

Lesson 3 • Stakeholders and M&E Roles

Maps the actors involved in M&E and clarifies their responsibilities. Understanding roles prevents gaps and conflicts in data collection and use.

Lesson 4 • Ethical Principles in M&E Practice

Covers informed consent, data privacy, do-no-harm, and impartiality as core ethical obligations. Ethics compliance protects participants and ensures credible findings.

Lesson 1 • From Logic Model to M&E Framework

Shows how each element of the logic model generates measurable questions and indicators. This linkage ensures the framework reflects actual programme theory.

Lesson 2 • Setting Baselines and Targets

Explains how to establish starting values and realistic performance targets for each indicator. Baselines and targets create the benchmark for measuring change over time.

Lesson 3 • Building the Performance Monitoring Plan

Guides students through creating a performance monitoring plan that specifies data sources, frequency, and responsibility. The plan operationalises the framework into daily M&E work.

Lesson 4 • Developing SMART Indicators

Teaches criteria for selecting specific, measurable, achievable, relevant, and time-bound indicators. Well-designed indicators reduce ambiguity in data collection and analysis.

Lesson 1 • Sampling Strategies for M&E

Covers probability and purposive sampling techniques suited to M&E budgets and timelines. Appropriate sampling ensures findings are credible and representative.

Lesson 2 • Mixed-Methods Approaches

Explains how to combine quantitative and qualitative methods for triangulation and richer insight. Students learn sequencing and integration strategies for mixed-methods designs.

Lesson 3 • Designing and Piloting Data Tools

Guides students through drafting, testing, and refining questionnaires and interview guides. Piloting reduces errors before full-scale data collection begins.

Lesson 4 • Qualitative Data Collection Methods

Introduces focus group discussions, key informant interviews, and case studies for capturing depth and context. Qualitative data complements quantitative findings in social projects.

Lesson 1 • Data Quality Assurance Frameworks

Introduces the five dimensions of data quality: validity, reliability, timeliness, completeness, and precision. Students apply a data quality assessment tool to real or simulated data.

Lesson 2 • Building a Data Management System

Covers database design, file naming conventions, and access controls for M&E data. A well-structured system prevents data loss and enables efficient retrieval.

Lesson 3 • Digital Data Collection Tools

Surveys mobile data collection platforms and their features for offline use, skip logic, and GPS tagging. Digital tools accelerate data flow and reduce transcription errors.

Lesson 4 • Data Entry and Cleaning Procedures

Teaches double-entry verification, range checks, and outlier detection to produce clean datasets. Clean data is the prerequisite for valid analysis and credible reporting.

Lesson 1 • Descriptive Quantitative Analysis

Covers frequency distributions, measures of central tendency, and cross-tabulations for summarising M&E data. Descriptive statistics form the baseline for all further quantitative analysis.

Lesson 2 • Interpreting and Validating Findings

Guides students through sense-checking results, identifying anomalies, and validating findings with stakeholders. Validation strengthens credibility and reduces misinterpretation risk.

Lesson 3 • Qualitative Data Analysis Techniques

Teaches thematic coding, content analysis, and narrative synthesis for qualitative M&E data. Systematic qualitative analysis produces defensible, evidence-based findings.

Lesson 4 • Inferential Statistics for M&E

Introduces hypothesis testing, confidence intervals, and basic regression for assessing programme effects. Inferential methods help distinguish real change from random variation.

Lesson 1 • Planning and Budgeting an Evaluation

Guides students through creating an evaluation scope of work, timeline, and cost estimate. Realistic planning prevents scope creep and ensures evaluation completion.

Lesson 2 • Experimental and Quasi-Experimental Designs

Covers randomised controlled trials, difference-in-differences, and regression discontinuity for causal inference. Students assess feasibility of each design for social project contexts.

Lesson 3 • Developing Evaluation Questions

Teaches how to formulate precise, answerable evaluation questions aligned with stakeholder needs. Well-crafted questions drive all subsequent design and methods decisions.

Lesson 4 • Types of Evaluation and Their Purpose

Distinguishes formative, summative, process, outcome, and impact evaluations by purpose and timing. Matching evaluation type to decision needs prevents wasted resources.

Lesson 1 • Donor Reporting Requirements

Covers standard donor reporting formats, indicator tables, and narrative requirements for compliance. Meeting donor expectations protects funding relationships and organisational reputation.

Lesson 2 • Structuring M&E Reports

Covers executive summary, findings, conclusions, and recommendations as standard report components. A logical structure ensures readers can locate and act on key information.

Lesson 3 • Communicating with Diverse Audiences

Adapts M&E communication for funders, government partners, communities, and media. Audience-specific messaging maximises the influence of M&E findings.

Lesson 4 • Promoting Evidence-Based Decision-Making

Examines barriers to data use and strategies for embedding M&E findings into programme decisions. Closing the evidence-to-action gap is the ultimate purpose of M&E.

Lesson 1 • Designing an Organisational M&E Strategy

Guides students through setting M&E priorities, standards, and governance structures at the organisational level. A strategy aligns M&E practice across multiple projects and teams.

Lesson 2 • M&E in Complex and Fragile Contexts

Addresses adaptations needed for M&E in conflict-affected, remote, or rapidly changing environments. Students apply complexity-aware methods to a challenging scenario.

Lesson 3 • Adaptive Management and Real-Time Learning

Explains how to use ongoing M&E data to adjust programme design during implementation. Adaptive management reduces waste and improves outcomes in dynamic social contexts.

Lesson 4 • Organisational M&E Capacity Assessment

Introduces tools for assessing staff skills, systems, and culture needed for effective M&E. Capacity gaps identified here inform the M&E strengthening plan.