Making a Difference: Evidence-Based Impact Course

Lesson 1 • The Evidence Spectrum

Maps evidence from anecdote to rigorous research. Enables learners to evaluate source credibility and select appropriate evidence types.

Lesson 2 • Why Rigor Matters for Decision-Making

Connects evidence quality to decision reliability. Shows how weak evidence leads to wasted resources and missed impact.

Lesson 3 • Core Principles of Evidence-Based Practice

Introduces the iterative cycle of evidence use: identify, appraise, apply, evaluate. Provides the operating framework for the entire course.

Lesson 1 • Scoping and Feasibility Assessment

Ensures impact questions are answerable within real constraints. Balances ambition with available time, data, and resources.

Lesson 2 • Crafting Precise Impact Questions

Teaches structured question formats that make impact measurable. Poorly framed questions are the leading cause of uninformative evaluations.

Lesson 3 • Logic Models as Planning Tools

Translates the theory of change into an operational logic model. Connects program activities to measurable short- and long-term outcomes.

Lesson 4 • Building a Theory of Change

Guides construction of causal pathways from inputs to impact. A robust theory of change anchors all subsequent measurement choices.

Lesson 1 • Qualitative Measurement Approaches

Interviews, focus groups, and observation as tools for capturing depth and context. Complements quantitative indicators in a mixed-methods framework.

Lesson 2 • Selecting Outcome Indicators

Provides criteria for choosing indicators that are meaningful, measurable, and actionable. Distinguishes leading from lagging indicators.

Lesson 3 • Quantitative Measurement Tools

Surveys, scales, and administrative records as quantitative data sources. Covers instrument selection, adaptation, and standardization.

Lesson 4 • Principles of Valid Measurement

Covers validity, reliability, and sensitivity as core measurement properties. Weak measurement undermines even the most rigorous evaluation design.

Lesson 1 • Causal Inference Fundamentals

Explains the fundamental problem of causal inference and the role of counterfactuals. Establishes why comparison groups are essential to impact claims.

Lesson 2 • Experimental Designs

Covers randomized controlled trials as the gold standard for causal inference. Addresses randomization mechanics, ethical considerations, and limitations.

Lesson 3 • Selecting and Justifying a Design

Provides a decision framework for matching design to question, context, and resources. Teaches how to document and defend design choices to stakeholders.

Lesson 4 • Non-Experimental and Developmental Designs

Addresses pre-post, case study, and contribution analysis for complex programs. Useful when experimental designs are impractical or inappropriate.

Lesson 1 • Managing Missing Data

Addresses causes and consequences of missing data and appropriate remediation strategies. Prevents biased conclusions from incomplete datasets.

Lesson 2 • Data Collection Protocols

Establishes standardized procedures for consistent data gathering across collectors and sites. Reduces measurement error and enables replication.

Lesson 3 • Data Quality Assurance

Introduces systematic checks for completeness, accuracy, and consistency during and after collection. Catches errors before they propagate into analysis.

Lesson 4 • Ethical Data Practices

Covers informed consent, confidentiality, and data protection as non-negotiable standards. Ethical failures undermine both participant trust and data validity.

Lesson 1 • Qualitative and Mixed-Methods Analysis

Applies thematic coding, narrative analysis, and integration strategies for mixed-methods studies. Strengthens causal explanations beyond what numbers alone provide.

Lesson 2 • Descriptive and Exploratory Analysis

Summarizes data distributions and identifies patterns before inferential testing. Provides the foundation for all subsequent analytical steps.

Lesson 3 • Regression and Multivariate Methods

Introduces regression as a tool for controlling confounders and estimating program effects. Covers model specification, diagnostics, and interpretation.

Lesson 4 • Inferential Statistics for Impact

Covers hypothesis testing, confidence intervals, and effect size estimation. Connects statistical results to practical significance for decision-makers.

Lesson 1 • Dissemination Planning

Develops a strategic plan for reaching all relevant audiences with evidence products. Ensures findings influence practice beyond the immediate project team.

Lesson 2 • Data Visualization for Impact

Applies principles of effective chart and graph design to impact data. Visuals must accurately represent findings without distorting interpretation.

Lesson 3 • Writing Evidence Briefs and Reports

Covers structure, language, and visual design for written evidence products. Emphasizes clarity, brevity, and actionable recommendations.

Lesson 4 • Audience Analysis and Message Design

Identifies audience knowledge, priorities, and decision contexts before crafting messages. Tailored communication increases evidence uptake and use.

Lesson 1 • Cost-Effectiveness and Value for Money

Introduces cost-effectiveness and cost-benefit analysis as tools for resource allocation. Connects impact evidence to financial stewardship and strategic prioritization.

Lesson 2 • Adaptive Management in Practice

Applies evidence from monitoring to make real-time program adjustments. Balances fidelity to design with responsiveness to emerging data.

Lesson 3 • Building an Evidence-Use Culture

Identifies organizational conditions that enable or block evidence use. Leaders must actively model and incentivize evidence-based decision-making.

Lesson 4 • Monitoring, Evaluation, and Learning Systems

Designs integrated MEL systems that generate timely, actionable data for program management. Distinguishes monitoring from evaluation and links both to learning.