Training on Advanced Impact Evaluation for Evidence-Based Development

Impact evaluation is essential for determining whether development interventions produce measurable and attributable change. Without rigorous evaluation methods, organizations cannot confidently distinguish between outcomes caused by their programs and those resulting from external factors, limiting the effectiveness of policy and investment decisions.

This advanced course provides participants with comprehensive, practical skills to design and implement robust impact evaluations for development programs and public policy interventions. It focuses on causal inference methodologies that establish clear links between interventions and observed outcomes.

Participants will learn how to design impact evaluation frameworks, define counterfactuals, and apply experimental and quasi-experimental methods. The training covers randomized controlled trials (RCTs), difference-in-differences (DiD), propensity score matching (PSM), regression discontinuity design (RDD), and instrumental variable (IV) approaches.

The course also explores key stages of the evaluation cycle, including evaluation design, sampling strategies, data collection, statistical analysis, and interpretation of results. Emphasis is placed on addressing bias, ensuring internal and external validity, and managing ethical considerations in impact evaluation.

Participants will gain hands-on experience analyzing real-world datasets, interpreting econometric outputs, and translating findings into actionable policy recommendations. The program aligns with global best practices in evidence-based development and supports results-based management and accountability frameworks used by governments and donor agencies.

By the end of the training, participants will be able to independently design and conduct rigorous impact evaluations, generate credible evidence, and inform strategic decisions that improve development outcomes and resource allocation.

Duration

10 Days

Who Should Attend

• Development practitioners in NGOs, international organizations, and government agencies
• Monitoring and Evaluation (M&E) specialists and officers
• Program managers and coordinators in development projects
• Researchers and academics in economics, development studies, and social sciences
• Policymakers and advisors focused on evidence-based decision-making
• Graduate students seeking advanced skills in impact evaluation

Organizational Impact

• Attract funding and build trust with credible, evidence-based reports.

• Improve decision-making by scaling programs with proven impact.

Personal Impact

• Gain advanced skills for senior M&E, research, or policy roles.

• Strengthen professional influence with robust, evidence-based insights.

By the end of this course, participants will be able to

• Learn about theories and practices of impact evaluation
• Learn about evaluation problem: Attribution, selection and placement biases
• Understand IE designs: randomized, quasi and non-experimental
• Conduct a rigorous impact evaluation of a project
• Conduct sampling and power calculation
• Learn and practice econometric techniques for impact evaluation
• Carry out impact data analysis
• Report and disseminate impact evaluation findings

Module 1: Introduction to Impact Evaluation

• Overview of impact evaluation
• Importance of evidence-based development
• Types and phases of impact evaluation

Module 2: Experimental Designs in Impact Evaluation

• Randomized Control Trials (RCTs)
• Designing and implementing RCTs
• Ethical considerations in experimental evaluations

Module 3: Quasi-Experimental Designs

• Difference-in-Differences (DID)
• Propensity Score Matching (PSM)
• Regression Discontinuity Design (RDD)

Module 4: Advanced Statistical Techniques

• Instrumental Variables (IV)
• Fixed Effects and Random Effects models
• Handling attrition and non-compliance

Module 5: Qualitative Methods in Impact Evaluation

• Integrating qualitative methods with quantitative approaches
• Conducting focus groups and in-depth interviews
• Analyzing qualitative data

Module 6: Mixed-Methods Approaches

• Designing mixed-methods evaluations
• Triangulation of data sources
• Case studies of mixed-methods evaluations

Module 7: Data Collection and Management

• Designing surveys and questionnaires
• Sampling techniques and strategies
• Data management and quality control

Module 8: Data Analysis and Interpretation

• Introduction to statistical software (e.g., STATA, R)
• Analyzing impact evaluation data
• Interpreting and presenting results

Module 9: Communicating Evaluation Findings

• Effective reporting and visualization of findings
• Tailoring communication for different stakeholders
• Developing policy briefs and presentations