Project Monitoring and Evaluation with Data Management and Analysis

This Project Monitoring and Evaluation with Data Management and Analysis course offers an impactful exploration of the principles and practices necessary for effective project monitoring, evaluation, and data management. This course is designed for professionals and practitioners involved in project management and evaluation who want to enhance their skills in monitoring project progress, measuring outcomes, and analyzing data for informed decision-making.

Through practical examples and hands-on exercises, participants will learn how to design and implement a comprehensive project monitoring and evaluation framework. They will gain insights into data collection methods, tools, and techniques for effective data management and analysis. Participants will also learn how to translate data into meaningful insights to drive evidence-based decision-making and project improvement.

Course Outline

Day 1.
Fundamentals of Monitoring and Evaluation

• Definition of Monitoring and Evaluation
• Why Monitoring and Evaluation is important
• Key principles and concepts in M&E
• M&E in project lifecycle
• Participatory M&E

Project Analysis

• Situation Analysis
• Needs Assessment
• Strategy Analysis

Day 2.
Design of Results in Monitoring and Evaluation

• Results chain approaches: Impact, outcomes, outputs and activities
• Results framework
• M&E causal pathway
• Principles of planning, monitoring and evaluating for results

M&E Indicators

• Indicators definition
• Indicator metrics
• Linking indicators to results
• Indicator matrix
• Tracking of indicators

Day 3.
Logical Framework Approach

• LFA – Analysis and Planning phase
• Design of logframe
• Risk rating in logframe
• Horizontal and vertical logic in logframe
• Using logframe to create schedules: Activity and Budget schedules
• Using logframe as a project management tool

Theory of Change

• Overview of theory of change
• Developing theory of change
• Theory of Change vs Log Frame
• Case study: Theory of change

Day 4:
M&E Systems

• What is an M&E System?
• Elements of M&E System
• Steps for developing Results based M&E System

M&E Planning

• Importance of an M&E Plan
• Documenting M&E System in the M&E Plan
• Components of an M&E Plan-Monitoring, Evaluation, Data management, Reporting
• Using M&E Plan to implement M&E in a Project
• M&E plan vs Performance Management Plan (PMP)

Day 5:
Base Survey in Results based M&E

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

Project Performance Evaluation

• Process and progress evaluations
• Evaluation research design
• Evaluation questions
• Evaluation report Dissemination

Day 6:
M&E Data Management

• Different sources of M&E data
• Qualitative data collection methods
• Quantitative data collection methods
• Participatory methods of data collection
• Data Quality Assessment

M&E Results Use and Dissemination

• Stakeholder’s information needs
• Use of M&E results to improve and strengthen projects
• Use of M&E Lessons learnt and Best Practices
• Organization knowledge champions
• M&E reporting format
• M&E results communication strategies

Day 7:
Gender Perspective in M&E

• Importance of gender in M&E
• Integrating gender into program logic
• Setting gender sensitive indicators
• Collecting gender disaggregated data
• Analyzing M&E data from a gender perspective
• Appraisal of projects from a gender perspective

Data Collection Tools and Techniques

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

Day 8:
Data Quality

• What is data quality?
• Why data quality?
• Data quality standards
• Data flow and data quality
• Data Quality Assessments
• M&E system design for data quality

ICT in Monitoring and Evaluation

• Mobile based data collection using ODK
• Data visualization – info graphics and dashboards
• Use of ICT tools for Real-time monitoring and evaluation

Day 9:
Qualitative Data Analysis

• Principles of qualitative data analysis
• Data preparation for qualitative analysis
• Linking and integrating multiple data sets in different forms
• Thematic analysis for qualitative data
• Content analysis for qualitative data

Quantitative Data Analysis – (Using SPSS/Stata)

• Introduction to statistical concepts
• Creating variables and data entry
• Data reconstruction
• Variables manipulation
• Descriptive statistics
• Understanding data weighting
• Inferential statistics: hypothesis testing, T-test, ANOVA, regression analysis

Day 10:
Impact Assessment

• Introduction to impact evaluation
• Attribution in impact evaluation
• Estimation of counterfactual
• Impact evaluation methods: Double difference, Propensity score matching
• Causal inference methods (randomized control trials, quasi-experimental designs)