Training Course on Project Monitoring and Evaluation with Data Management and Analysis Verified listing

Course Overview

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.

By the end of the training, participants will:

Have a comprehensive understanding of project monitoring and evaluation principles.
Master key M&E concepts, including indicators, targets, baselines, and milestones, along with familiarity with frameworks such as the logical framework approach and results-based management.
Gain practical skills in data management, including the design of effective data collection tools and the establishment of robust data management systems.
Demonstrate proficiency in analyzing both quantitative and qualitative project data using tools like Excel, SPSS, or R.
Implement techniques for data validation, cleaning, and quality assurance to ensure the accuracy and reliability of project data.
Effectively interpret and communicate evaluation findings to diverse stakeholders through reports, presentations, and data visualization.
Apply adaptive management principles for informed decision-making and improved project performance.
Utilize hands-on learning experiences, including practical exercises and real-world project data application, to confidently apply acquired knowledge in professional settings.
Duration: 10 days

Course Outline

Module 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
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
Module 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
Module 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)
Module 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
Module 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
Module 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
Module 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
Module 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
Module 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)