11 Topics
5 Days
42 Sessions
Programme Overview
Training Description
Who Should Attend
- Business Analysts
- Risk Managers
- Finance and Budget Professionals
- Quality Assurance Professionals
- Strategy and Planning Professionals
- Programme Officers
- Development and NGO Project Professionals
- Government Project Officers
- Consultants
- Internal Auditors
- Senior Managers responsible for project performance
Session Objectives
- Understand the distinction between monitoring and evaluation.
- Identify and utilize relevant project indicators for evaluation.
- Design and plan comprehensive project evaluations.
- Apply mixed methods of data collection effectively during evaluations.
- Perform quantitative and qualitative data analysis using advanced tools.
- Use findings to formulate evaluation conclusions and recommendations.
About the Course
The Project Performance Evaluation Training Course equips project professionals with the knowledge, tools, and practical techniques required to assess project performance systematically and objectively throughout the project lifecycle. The course focuses on measuring project progress, efficiency, effectiveness, quality, cost, schedule, resource utilization, risk management, stakeholder satisfaction, and achievement of intended outcomes.Participants will learn how to establish meaningful performance indicators, collect and analyze project data, conduct project health assessments, apply Earned Value Management (EVM), evaluate project benefits, identify performance gaps, and develop corrective and improvement actions. The course also covers project reviews, lessons learned, evaluation reporting, and evidence-based decision-making.
Through practical exercises, case studies, dashboards, performance analysis activities, and real-world project scenarios, participants will develop the ability to evaluate whether projects are delivering planned outputs, outcomes, value, and strategic objectives.
Curriculum & Topics
11 Topics | 42 Sessions
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Workshop 1.1:
Understanding the Results Framework
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Workshop 1.2:
Project Indicators of M&E
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Workshop 1.3:
The Logical Framework
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Workshop 2.1:
Definition and importance of project evaluation
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Workshop 2.2:
Differentiating between monitoring and evaluation
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Workshop 2.3:
Designing effective project evaluations
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Workshop 3.1:
Purpose and types of evaluations
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Workshop 3.2:
Criteria for effective evaluations
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Workshop 3.3:
Developing evaluation questions
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Workshop 4.1:
Identifying data sources for evaluations
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Workshop 4.2:
Methods of data collection
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Workshop 4.3:
Data analysis methods
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Workshop 4.4:
Completing the evaluation matrix
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Workshop 5.1:
Task distribution
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Workshop 5.2:
Developing an evaluation work plan
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Workshop 5.3:
Budgeting for evaluations
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Workshop 6.1:
Identifying evaluation indicators
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Workshop 6.2:
Utilizing evaluation tools
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Workshop 6.3:
Incorporating monitoring data into evaluations
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Workshop 6.4:
Managing mixed methods of data collection
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Workshop 7.1:
Converting paper-based questions into ODK forms
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Workshop 7.2:
Configuring ODK servers
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Workshop 7.3:
Collecting field data using ODK collect
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Workshop 7.4:
Exporting data for analysis
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Workshop 8.1:
Introduction to statistical concepts
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Workshop 8.2:
Creating variables and data entry
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Workshop 8.3:
Data reconstruction and manipulation
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Workshop 8.4:
Descriptive statistics and data weighting
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Workshop 8.5:
Inferential statistics: Hypothesis testing, T-test, ANOVA, regression analysis
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Workshop 9.1:
Principles of qualitative data analysis
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Workshop 9.2:
Preparing data for qualitative analysis
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Workshop 9.3:
Linking and integrating multiple datasets
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Workshop 9.4:
Thematic and content analysis of qualitative data
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Workshop 9.5:
Using NVivo for qualitative data analysis
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Workshop 9.6:
Data triangulation
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Workshop 10.1:
Consolidating the evaluation matrix
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Workshop 10.2:
Data triangulation for accurate results
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Workshop 10.3:
Answering evaluation questions
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Workshop 11.1:
Drawing conclusions
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Workshop 11.2:
Formulating recommendations
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Workshop 11.3:
Preparing the final evaluation report
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Workshop 11.4:
Disseminating evaluation results
