
Program Evaluation and Grant-Funded Services
Master every stage of programme evaluation — from logic models to final reports — within the real-world demands of grant-funded services. This course equips nonprofit professionals, programme managers, and evaluators with the practical tools to design evaluations, collect credible data, and satisfy funder requirements with confidence.
What you will learn:
Construct logic models and theories of change that align programmes with measurable outcomes.
Design rigorous evaluation plans suited to experimental, quasi-experimental, and mixed-methods approaches.
Develop and validate data collection instruments for both qualitative and quantitative evaluation needs.
Apply descriptive and inferential statistics to analyse programme data and interpret findings accurately.
Produce formal evaluation reports and executive summaries tailored to funders and programme stakeholders.
Integrate evaluation evidence into grant compliance systems, progress reporting, and continuous programme improvement.
How you study in practice Program Evaluation and Grant-Funded Services
How you practise Program Evaluation and Grant-Funded Services
For companies looking to train their team
With Dedika for businesses, the course includes exercises and examples tailored to your own business and the specific needs of your company.
Course content
8 Chapters • 35 LessonsDuration between 4 and 360 hours (you decide)
Chapter 1HideHide detailsSee detailsFoundations of Programme Evaluation
Foundations of Programme Evaluation
Lesson 1 • Evaluation Within Grant-Funded Programmes
Explains how funders mandate evaluation and how evaluation requirements shape programme design. Connects foundational concepts directly to grant-funded service contexts.
Lesson 2 • Ethical Standards in Evaluation
Introduces professional ethical standards governing evaluator conduct, confidentiality, and fairness. Establishes non-negotiable principles applied throughout the course.
Lesson 3 • Defining Programme Evaluation
Establishes what programme evaluation is, why it exists, and who uses it. Grounds all subsequent evaluation work in shared terminology and purpose.
Lesson 4 • Types and Models of Evaluation
Surveys major evaluation models including logic-model-based, theory-driven, and participatory approaches. Enables informed model selection for diverse programme contexts.
Chapter 2HideHide detailsSee detailsLogic Models and Programme Theory
Logic Models and Programme Theory
Lesson 1 • Building a Logic Model from Scratch
Applies component knowledge to construct a complete logic model for a real or simulated programme. Produces a practitioner-ready artifact used in later evaluation planning.
Lesson 2 • Using Logic Models to Guide Evaluation
Demonstrates how a finalised logic model drives evaluation question development and indicator selection. Bridges programme theory to measurable evaluation design.
Lesson 3 • Developing a Programme Theory of Change
Guides construction of a theory of change that explains causal pathways from activities to impact. Strengthens the evaluative rationale behind programme design.
Lesson 4 • Components of a Logic Model
Breaks down each logic model element and its evaluative function. Provides the structural vocabulary needed to build and critique programme models.
Chapter 3HideHide detailsSee detailsEvaluation Design and Planning
Evaluation Design and Planning
Lesson 1 • Writing the Evaluation Plan
Structures all design decisions into a formal evaluation plan document meeting funder standards. Produces the primary deliverable for grant-funded evaluation compliance.
Lesson 2 • Sampling and Participant Selection
Covers probability and purposive sampling strategies appropriate for programme evaluation contexts. Ensures data collection reaches the right participants for valid conclusions.
Lesson 3 • Selecting an Evaluation Design
Compares experimental, quasi-experimental, and non-experimental designs for feasibility and rigour. Guides appropriate design choice given real-world programme constraints.
Lesson 4 • Formulating Evaluation Questions
Teaches criteria for crafting focused, answerable evaluation questions tied to logic model outcomes. Well-formed questions anchor all subsequent design decisions.
Chapter 4HideHide detailsSee detailsData Collection Methods and Instruments
Data Collection Methods and Instruments
Lesson 1 • Quantitative Data Collection Tools
Covers surveys, administrative records, and standardised scales as quantitative instruments. Connects tool selection to the need for measurable, comparable outcome data.
Lesson 2 • Data Management and Protection
Establishes protocols for storing, organising, and protecting participant data throughout the evaluation. Meets funder and ethical requirements for data security and confidentiality.
Lesson 3 • Culturally Responsive Data Collection
Adapts instruments and procedures to serve diverse participant populations equitably. Reduces measurement bias and improves data quality across cultural contexts.
Lesson 4 • Instrument Development and Validation
Guides the process of writing, testing, and refining data collection instruments for reliability and validity. Ensures instruments produce trustworthy data before full deployment.
Lesson 5 • Qualitative Data Collection Methods
Introduces interviews, focus groups, and observation as qualitative tools for capturing programme experience. Qualitative data enriches outcome findings with contextual depth.
Chapter 5HideHide detailsSee detailsQuantitative Data Analysis for Evaluation
Quantitative Data Analysis for Evaluation
Lesson 1 • Visualising Quantitative Findings
Teaches chart and graph selection for communicating numeric results to diverse audiences. Effective visualisation increases stakeholder understanding and use of evaluation data.
Lesson 2 • Descriptive Statistics for Programme Data
Applies measures of central tendency, variability, and frequency to summarise programme outputs and outcomes. Descriptive results form the baseline narrative of any evaluation report.
Lesson 3 • Inferential Statistics in Evaluation
Introduces hypothesis testing, t-tests, chi-square, and regression for evaluating programme effects. Enables evidence-based conclusions about whether programmes produce intended effects.
Lesson 4 • Preparing Data for Analysis
Covers data cleaning, coding, and entry verification as prerequisites to accurate analysis. Clean data is the foundation of credible evaluation findings.
Chapter 6HideHide detailsSee detailsQualitative Data Analysis for Evaluation
Qualitative Data Analysis for Evaluation
Lesson 1 • Ensuring Rigour in Qualitative Analysis
Applies trustworthiness criteria including credibility, transferability, and confirmability to qualitative work. Rigour strategies defend findings against stakeholder scepticism.
Lesson 2 • Thematic Analysis Techniques
Guides the process of grouping codes into themes that answer evaluation questions. Themes become the core narrative structure of qualitative evaluation findings.
Lesson 3 • Coding Qualitative Data
Teaches deductive and inductive coding strategies for identifying patterns in qualitative data. Coding transforms raw text into structured, analysable units of meaning.
Lesson 4 • Organising and Preparing Qualitative Data
Covers transcription, data organisation, and initial familiarisation with qualitative datasets. Proper preparation enables systematic and trustworthy analysis.
Chapter 7HideHide detailsSee detailsReporting and Communicating Evaluation Results
Reporting and Communicating Evaluation Results
Lesson 1 • Presenting Findings to Stakeholders
Prepares evaluators to deliver oral presentations and facilitate discussion of evaluation results. Effective presentation skills increase stakeholder buy-in and programme improvement action.
Lesson 2 • Structuring the Evaluation Report
Defines the standard components of a formal evaluation report and their logical sequence. A well-structured report enables funders and programme staff to act on findings.
Lesson 3 • Dissemination and Knowledge Sharing
Covers strategies for sharing evaluation findings beyond the immediate funder relationship. Broad dissemination amplifies programme learning and contributes to the field.
Lesson 4 • Data Visualisation in Reports
Integrates charts, infographics, and tables into narrative reports for clarity and engagement. Visual elements must accurately represent data without distorting findings.
Lesson 5 • Writing for Different Audiences
Adapts report language, depth, and format to funders, programme staff, and community stakeholders. Audience-appropriate writing maximises the use and impact of evaluation results.
Chapter 8HideHide detailsSee detailsGrant Management and Evaluation Compliance
Grant Management and Evaluation Compliance
Lesson 1 • Progress and Interim Reporting
Covers the structure and content of interim progress reports required by funders at regular intervals. Timely, accurate interim reports maintain funder trust and grant standing.
Lesson 2 • Understanding Grant Requirements
Decodes funder evaluation requirements embedded in grant agreements and notice of funding documents. Accurate interpretation prevents compliance failures and reporting gaps.
Lesson 3 • Using Evaluation for Programme Improvement
Applies evaluation findings to continuous quality improvement within the grant period. Closes the loop between data collection and programme decision-making for sustained impact.
Lesson 4 • Performance Measurement Systems
Builds systems for tracking performance measures continuously across the grant period. Ongoing measurement enables timely course corrections and accurate progress reporting.
Lesson 5 • Managing Evaluator-Funder Relationships
Develops communication strategies for navigating funder expectations, site visits, and feedback cycles. Strong relationships support evaluation flexibility and programme sustainability.
Your valid completion certificate
This course is for you:
Nonprofit program coordinators: responsible for funder reporting but lacking evaluation training.
Grant managers: overseeing compliance requirements without a structured evaluation process.
Social workers: moving into programme oversight roles that demand outcome measurement skills.
Public health professionals: needing to document and communicate community intervention results.
Career changers: entering the nonprofit sector from research, education, or government backgrounds.
Graduate students: studying public administration, social work, or community development programmes.
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