Overview

Health is a highly sensitive and foundational sector in any economy, with national development closely anchored in the performance of health programmes. Given the sector’s complexity and multi-faceted nature, effective monitoring and evaluation, supported by robust data management and analysis systems, is essential. This course equips participants with practical skills to design, implement, and manage monitoring and evaluation systems for health programmes. Participants are introduced to M&E frameworks, indicators, tools, data quality approaches, and analytical techniques to ensure health programmes are implemented as planned and evaluated against intended results.

Target Audience

Researchers, project staff, development practitioners, managers, and decision makers working in health programmes.

Learning Outcomes / Objectives

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

• Design health programmes using the logical framework analysis approach.
• Define and apply appropriate indicators for health programmes.
• Track performance indicators throughout the programme life cycle.
• Conduct effective monitoring of health programmes.
• Evaluate health programmes against objectives and targets.
• Design and conduct impact evaluations for health programmes.

Duration

5 days

Modules / Course Content

Module 1: Introduction to monitoring and evaluation in health programmes

• Strategic information in health programmes
  • Need for reliable information
  • Components and uses of strategic information
  • Strategic information and the programme life cycle
  • Decision-making using strategic information
• Introduction to monitoring and evaluation
  • Definitions, importance, and key M&E concepts
  • M&E within the project and programme life cycle
  • Complementary roles of monitoring and evaluation
• M&E frameworks
  • Conceptual and results frameworks
  • Logical framework analysis
  • Designing health programmes using logframes
• Health indicators and M&E planning
  • Indicator selection and metrics
  • Linking indicators to results
  • Indicator matrices and tracking
  • Developing and implementing M&E plans

Module 2: Data collection and data quality in health programmes

• Data collection tools and techniques
  • Primary and secondary data sources
  • Sampling approaches
  • Qualitative, quantitative, and participatory methods
  • Data triangulation
• Data quality management
  • Importance of data quality in health programmes
  • Data quality dimensions
  • Routine data quality assessments and audits
  • Data quality assurance mechanisms

Module 3: Data management and quantitative analysis

• Introduction to statistics and Stata
  • Statistical concepts for health programmes
  • Stata environment, data structures, and variable types
  • Data management and output handling
  • Basics of Stata programming
• Descriptive statistics and visualization
  • Describing quantitative data
  • Tabulating qualitative data
  • Graphing quantitative and qualitative data
• Inferential analysis
  • Correlation and subgroup analysis
  • Chi-square tests
  • Mean comparison using t-tests and ANOVA
  • Factorial ANOVA

Module 4: Regression analysis, nonparametric statistics, and GIS

• Regression analysis
  • Assumptions of regression models
  • Linear, logistic, multinomial, ordered, and Poisson regression
• Nonparametric statistical methods
  • Mann-Whitney, Wilcoxon, Kruskal-Wallis, and Friedman tests
• Use of GIS in health programmes
  • GIS benefits and applications in health
  • GIS data sources
  • Spatial analysis and mapping techniques

Module 5: Qualitative analysis, evaluation, and data use in health programmes

• Qualitative data analysis
  • Principles and preparation of qualitative data
  • Thematic and content analysis
  • Integrating multiple data sources
  • Use of NVivo for qualitative analysis
• Health programme evaluation
  • Evaluation points and designs
  • Process, implementation, and impact evaluations
  • Performance evaluation and dissemination of findings
• Data demand and use
  • Using data to inform policies and programmes
  • Data flow and determinants of data use
  • Linking data to action and knowledge management

Training Methodology

The course will employ a hands-on, practical approach to ensure participants develop both conceptual understanding and technical proficiency. Each module will integrate interactive lectures, guided software demonstrations, and individual or group exercises based on real-world illustrations. Participants will receive continuous feedback and personalized coaching to reinforce learning. By the end of the training, they will have completed a mini project that demonstrates their ability to apply the acquired skills in a practical context.

More Details

Upon successful completion of this course, participants will be issued a certificate.