- 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 .
- 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
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