Course Objectives:

  • Define and distinguish between monitoring and evaluation.
  • Develop a program logic model to communicate an evidence-based program theory.
  • Develop an M&E plan to track progress of program activities toward objectives and assess program effectiveness.   
  • Develop quantitative and qualitative indicators and targets for an M&E plan.
  • Use relevant qualitative and quantitative data collection and analysis methods to track and evaluate program progress.
  • Identify the qualities of effective qualitative and quantitative data collection tools.
  • Describe how program data can be used for decision-making.
  • Apply ethical guidelines for data collection and reporting.


Module 1: An introduction to monitoring and evaluating in global health

  • Define monitoring and evaluation.
  • Distinguish between monitoring and evaluation.
  • Explain why M&E is important.
  • Identify monitoring best practices.
  • Explain how key M&E activities fit into a typical program cycle.
  • Describe strategies to address common concerns about program evaluation.

Module 2: Program theory and frameworks

  • Define what a program theory is.
  • Identify three program frameworks.
  • List the five main components of a logic model.
  • Develop evidence-based program outcomes that align with program impact.
  • Develop program outputs that align with program activities and outcomes.

Module 3: The M&E plan

  • Describe what an M&E plan is and why it is an important aspect of program success
  • Explain the relationship between logic models and M&E plans
  • Define the key components of an M&E plan
  • Write SMART objectives
  • Name and explain the qualities of effective program indicators
  • Develop indicators and targets for an M&E plan according to specified criteria
  • Describe the 6 steps involved in developing and implementing an M&E plan

Module 4: Program monitoring

  • Describe the basic steps to conducting effective program monitoring.
  • List three potential data sources for program monitoring.
  • Conduct descriptive analysis to summarize data for program monitoring.
  • Apply data visualization principles in preparing tables and figures.
  • Describe three data visualization methods to visualize data for action.

Module 5: Designing and conducting program evaluations

  • Describe the main steps to conducting a program evaluation;
  • Explain when the five types of program evaluations are used;
  • Develop relevant program evaluation questions;
  • Describe three program evaluation methodologies;
  • Describe two quantitative designs commonly used in program evaluation;
  • Name one key element to successful dissemination of evaluation findings.

Module 6: Setting and participant selection

  • Define the terms evaluation setting and evaluation participants.
  • Explain how inclusion and exclusion criteria are used to select evaluation setting and participants.
  • Distinguish between population and sample.
  • Describe the three broad sampling approaches of convenience, probability, and purposive sampling.
  • Explain the criteria used to inform sample size for purposive sampling.
  • Describe seven commonly used purposive sampling methods.

Module 7: Data collection: Part 1

  • Define quantitative and qualitative data.
  • Describe four characteristics of high-quality data.
  • Describe the main steps to prepare for data collection.
  • Explain how to collect data through document review.
  • Explain how to collect data through data abstraction.

Module 8: Data collection: Part 2

  • List the two key concepts that should guide data collection tool design.
  • Describe the four best practices for overall data collection tool design.
  • Apply the four best practices for developing questions for data collection tools.
  • Differentiate between closed- and open-ended questions.
  • Recognize common question types used in surveys.
  • Define a Likert scale.
  • Explain how to collect data through surveys, observations, interviews, and focus groups.
  • Explain the overall structure of interview and focus group discussion guides.

Module 9: Data analysis, validation and dissemination

  • Describe four key data processing practices.
  • Explain two essential data quality checks to perform on quantitative data.
  • Differentiate between descriptive and inferential analysis.
  • Distinguish between statistically significant and programmatically meaningful differences.
  • Describe the basic steps involved in thematic analysis.
  • Describe elements to include in a codebook and why codebooks are important.
  • List guidelines for writing up qualitative findings.

Module 10:  Ethics

  • Explain what human subjects protections are and why they are important&nbsp.
  • Name and define the Belmont Report’s three fundamental principles of ethics.
  • Explain what informed consent means and describe the key elements of a consent process.
  • Distinguish between anonymity, confidentiality, and privacy and describe methods to protect each  
  • Describe procedures that evaluators can adopt to minimize participant vulnerability 
  • Identify the four categories of safeguards for ethical data management and give examples of each 
  • Describe key recommendations to promote ethical reporting, dissemination, and use of findings

Course Activities:

During the course, participants will be expected to:

  • Analyze problem statements and develop outcomes
  • Work with logic models
  • Write SMART objectives and indicators
  • Complete activities around data analysis and visualization (in Microsoft Excel)
  • Assess evaluation questions
  • Analyze qualitative methods
  • Choose sampling methods
  • Create open ended questions
  • Work on an M&E plan