Managing and Analysing Monitoring and Evaluation Data for Development Programmes

INTRODUCTION

This course 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. 

DURATION

10 Days

COURSE OUTLINE

Module 1

Introduction to Results Based Project Management

• Fundamentals of Results Based Management
• Why is RBM important?
• Results based management vs traditional projects management
• RBM Lifecycle (seven phases)
• Areas of focus of RBM

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

Module 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

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

Module 3

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)

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

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

Module 4

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

Module 5

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
• Qualitative data collection methods
• Quantitative data collection methods
• Participatory data collection methods
• Introduction to data triangulation

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

Module 6

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

Module 7

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
• Manipulation and analysis of data using NVivo

Module 8

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

Module 9

Impact Assessment

• Introduction to impact evaluation
• Attribution in impact evaluation
• Estimation of counterfactual
• Impact evaluation methods: Double difference, Propensity score matching

Module 10

GIS in M&E

• Introduction to GIS in M&E
• GIS analysis and mapping techniques
• Data preparation for geospatial analysis
• Geospatial analysis using GIS software (QGIS)