Training on Project Monitoring and Evaluation with Data Management and Analysis

This is a comprehensive M&E course that covers the principles and practices for results-based monitoring and evaluation for the entire project life cycle. This course equips participants with skills in setting up and implementing results-based monitoring and evaluation systems including M&E data management, analysis and reporting. The participants will benefit from the latest M&E thinking and practices including the results and participatory approaches.

This course is designed to enable the participants become experts in monitoring and evaluating their development projects. The course covers all the key elements of a robust M&E system coupled with a practical project to illustrate the M&E concepts.

Course Duration

10 Days

Who Should Attend

• Project managers and coordinators
• Monitoring and evaluation officers
• Data analysts and managers
• Program officers and staff involved in project implementation
• Development practitioners
• Non-governmental organization (NGO) staff
• Government officials involved in project management
• Consultants working in project planning and evaluation

Organizational Impact

• Improve project effectiveness and accountability through evidence-based decision-making.

• Enhance credibility with donors, partners, and stakeholders.

• Uncover opportunities, mitigate risks, and achieve better project outcomes.

• Increase operational efficiency through standardized M&E and data management.

Personal Impact

• Gain in-demand skills for project management and development careers.

• Progress into senior leadership, program evaluation, or data stewardship roles.

• Contribute directly to improved project performance and impact.

• Lead and champion data-driven initiatives with confidence.

By the end of this course, participants will:

• Understand the key concepts and principles of project monitoring and evaluation.
• Develop effective M&E plans and frameworks.
• Use various data collection methods and tools for M&E.
• Analyze and interpret M&E data to inform project decisions.
• Apply data management best practices to ensure data quality and integrity.
• Utilize statistical software for data analysis and visualization.
• Conduct impact assessments and evaluations.
• Communicate M&E findings effectively to stakeholders.
• Develop skills in using advanced data analysis techniques.
• Integrate M&E and data management practices into project cycles.

Introduction to Project Management

• Introduction to project management
• Project cycle management
• Triple constraints in project management
• M&E in project management

Introduction to M&E

• Definition of monitoring and evaluation
• Why monitoring and evaluation is important
• Key principles and concepts in M&E
• Monitoring and evaluation processes

Results-Based Management

• Introduction to results-based management approach
• Steps in results-based management
• Application of results-based management in project design
• Understanding the results chain approach

Project Analysis

• Problem analysis
• Objectives analysis
• Strategy analysis

Project Logic Design

• Introduction to problem analysis
• Understanding M&E causal pathway
• Developing the project results levels: goal, outcomes and output
• Formulating activities

Project Indicators

• Introduction to project indicator
• Indicator development
• Predefined indictors
• Linking indicators to results in programs
• Indicator matrix
• Program indicator performance tracking

Completing the M&E Tools

• Completing the logical framework
• Developing a theory of change
• Completing the performance measurement framework

Project Monitoring

• Introduction to programs monitoring
• Types of monitoring
• Monitoring strategies
• Tools for monitoring and performance measurement

M&E Reporting

• Introduction to reporting
• Key elements in progress reporting
• Reporting schedules

M&E Plan

• Introduction to M&E system and M&E Plan
• Components of the M&E Plan
• Developing the M&E Plan
• Implementing the M&E plan

Baseline Studies in Results based M&E

• Importance of baseline studies
• Process of conducting baseline studies
• Baseline study vs evaluation

Project Evaluations

• Introduction to project evaluation
• Types of evaluations
• Process of conducting evaluation
• Project evaluation criteria
• Developing evaluation questions
• Developing evaluation matrix
• Evaluation reporting

Overview of Impact Evaluation

• Introduction to impact evaluation
• Project attribution for outcome of interest
• Impact evaluation designs

Learning in M&E

• Introduction to learning
• Documentation of lessons learned and best practices
• Use of learning to improve and strengthen projects
• M&E Results dissemination

Gender Mainstreaming in M&E

• Key gender concepts and definitions
• Overview of gender mainstreaming in project management
• Engendering the logical framework
• Development of gender responsive data collection methods and tools

M&E Data Collection Methods and Tools

• Sources of M&E data –primary and secondary
• Qualitative data collection methods
• Quantitative data collection methods
• Participatory data collection methods
• Introduction to data triangulation

Mobile Data Collection

• Introduction to mobile data collection
• Setting up MDC
• Setting up the server
• Design of data collection forms

Introduction to Data Analysis

• Introduction statistics and data analysis
• Understanding descriptive vs inferential statistics
• Understanding variables
• Introduction to data types

Statistical Data Management

• Data cleaning and management

Exploring Data

• Descriptive analysis
• Frequencies
• Basic univariate analysis
• Cross tabulations
• Basic univariate analysis
• Cross tabulations

Comparison Tests

• Understanding parametric vs non-parametric tests
• Choosing a test – decision tree
• Parametric data- T test, ANOVA
• Non-parametric data
• Proportions

Tests of Association

• Introduction to correlation analysis
• Parametric data – Pearson correlation
• Nonparametric data – Spearman rank correlation

Regression Analysis

• Introduction to regression analysis
• Regression diagnostics – measurement error, multicollinearity, heteroskedasticity, endogeneity, non-normality
• Parametric data – linear regression analysis (simple and multiple regression)
• Non-parametric data – non parametric regression
• Categorical dependent variables – logit and probit regression models

 Data Visualization

• Pivot tables
• Graphs and charts
• Dashboards