Course Introduction
Evaluation research provides systematic approaches for determining whether programs, policies, interventions, services, and organizational initiatives are being implemented as intended and whether they are producing meaningful results. This course equips participants with practical skills to design rigorous evaluation studies, formulate evaluation questions, select appropriate methodologies, collect credible evidence, analyze findings, and communicate results that can support learning, accountability, improvement, and evidence-informed decision-making.
The course develops a comprehensive understanding of evaluation design, including needs assessment, evaluability assessment, process evaluation, outcome evaluation, impact evaluation, economic evaluation, developmental evaluation, and utilization-focused approaches. Participants learn how to connect evaluation purposes with research questions, theories of change, logic models, indicators, assumptions, contextual factors, comparison strategies, data sources, and analytical methods while ensuring that evaluation designs remain practical and responsive to stakeholder information needs.
Participants examine quantitative, qualitative, and mixed-methods approaches for generating evaluation evidence. The course covers experimental and quasi-experimental designs, observational approaches, longitudinal assessment, surveys, interviews, focus groups, case studies, document analysis, administrative data, routine monitoring data, and participatory evaluation techniques. Attention is given to selecting designs according to the intervention, evaluation purpose, population, implementation context, available resources, ethical considerations, and strength of evidence required.
Strong emphasis is placed on data quality and evidence generation throughout the evaluation lifecycle. Participants learn to develop indicators, measurement instruments, sampling strategies, data collection protocols, data management systems, and quality assurance procedures. They also explore descriptive analysis, comparative analysis, causal inference, qualitative analysis, mixed-methods integration, contribution analysis, triangulation, sensitivity analysis, and interpretation of findings while recognizing uncertainty, limitations, implementation variation, and contextual influences.
The course addresses ethical and practical considerations that influence the credibility and usefulness of evaluation research. Participants examine informed consent, confidentiality, responsible data use, stakeholder participation, independence, evaluator positionality, conflicts of interest, transparency, equity, cultural responsiveness, and appropriate communication of findings. Strategies for documenting decisions, maintaining audit trails, ensuring analytical transparency, and strengthening reproducibility are integrated into the evaluation process.
Emerging approaches to evidence generation are also examined, including real-world evidence, adaptive evaluation, rapid-cycle evaluation, digital data collection, remote evaluation, learning-oriented evaluation systems, artificial intelligence-supported analysis, automated monitoring, and advanced data visualization. By the end of the course, participants will be better prepared to design credible evaluation studies, generate defensible evidence, assess program performance and outcomes, interpret complex findings, and translate evaluation results into practical recommendations for learning, improvement, accountability, and future decision-making.
Duration
5 days
Who Should Attend
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Evaluation researchers responsible for designing, implementing, analyzing, or managing program and policy evaluations.
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Monitoring and evaluation specialists seeking advanced skills in evaluation design, evidence generation, data analysis, and reporting.
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Program managers who need to assess implementation progress, outcomes, effectiveness, efficiency, and sustainability.
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Policy analysts evaluating government policies, public services, regulations, and large-scale development interventions.
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Impact evaluation specialists conducting experimental, quasi-experimental, longitudinal, and comparative studies.
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Quantitative researchers and statisticians supporting evaluation studies through advanced data analysis and statistical modeling.
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Qualitative researchers conducting interviews, focus groups, case studies, observations, and contextual evaluation research.
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Mixed-methods researchers integrating quantitative and qualitative evidence to develop comprehensive evaluation findings.
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Development practitioners and nonprofit professionals assessing projects, interventions, institutional performance, and community-level outcomes.
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Public-sector professionals responsible for evidence generation, performance assessment, accountability, and program improvement.
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Research coordinators and project managers overseeing evaluation data collection, field operations, quality assurance, and research documentation.
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Academic researchers and postgraduate students developing evaluation studies, dissertations, research proposals, and applied evidence-generation projects.
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Consultants and technical advisers designing evaluation frameworks, theories of change, indicators, analytical strategies, and evidence products.
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Data managers and research data specialists responsible for evaluation datasets, data quality, documentation, security, and analytical readiness.
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Organizational leaders and decision-makers seeking stronger evidence for program improvement, resource allocation, strategic planning, and institutional learning.
Course Objectives
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Explain the principles, purposes, approaches, and methodological foundations of evaluation research and distinguish needs, process, outcome, impact, developmental, and economic evaluation.
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Design coherent evaluation studies that connect evaluation purposes, research questions, theories of change, logic models, indicators, data sources, comparison strategies, and analytical approaches.
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Select appropriate experimental, quasi-experimental, observational, qualitative, and mixed-methods evaluation designs according to intervention characteristics, context, resources, and evidence requirements.
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Develop evaluation frameworks, theories of change, logic models, indicators, measurement plans, and evidence-generation strategies that clearly connect activities, outputs, outcomes, impacts, assumptions, and contextual factors.
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Apply appropriate sampling and recruitment strategies for evaluation populations, comparison groups, implementation sites, beneficiaries, service providers, stakeholders, and other relevant research participants.
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Design robust data collection systems using surveys, interviews, focus groups, observations, administrative records, monitoring systems, routine datasets, and other credible evidence sources.
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Apply quantitative, qualitative, and mixed-methods analytical techniques to examine program performance, implementation processes, outcomes, impacts, contextual variation, mechanisms, and differences between relevant groups or settings.
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Assess evaluation data quality, validity, reliability, completeness, comparability, and analytical limitations while applying appropriate quality assurance and evidence verification procedures.
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Integrate evaluation findings with stakeholder perspectives, contextual information, implementation evidence, equity considerations, and uncertainty to develop balanced and defensible interpretations.
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Translate evaluation evidence into clear reports, recommendations, learning products, policy messages, and decision-support outputs that strengthen accountability, program improvement, organizational learning, and evidence-informed planning.
Comprehensive Course Outline
Module 1: Foundations of Evaluation Research
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Defining evaluation research and examining its purposes in accountability, learning, program improvement, resource allocation, and evidence-informed decision-making.
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Distinguishing needs assessment, process evaluation, outcome evaluation, impact evaluation, economic evaluation, developmental evaluation, and other evaluation approaches.
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Understanding the relationship between monitoring, evaluation, research, quality improvement, implementation research, and performance management.
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Identifying evaluation stakeholders, information needs, intended users, decision contexts, ethical requirements, and practical constraints affecting evaluation design.
Module 2: Evaluation Questions, Theories of Change, and Logic Models
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Developing clear evaluation questions that align with program objectives, stakeholder information needs, intended decisions, and available evidence.
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Constructing theories of change and logic models linking inputs, activities, outputs, outcomes, impacts, assumptions, risks, and contextual influences.
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Translating program theories into measurable evaluation indicators and evidence requirements across different stages of implementation and outcome development.
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Assessing program evaluability by examining intervention clarity, implementation readiness, measurable outcomes, data availability, stakeholder expectations, and contextual conditions.
Module 3: Evaluation Research Design and Method Selection
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Selecting appropriate experimental, quasi-experimental, observational, qualitative, and mixed-methods designs according to evaluation objectives and evidence requirements.
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Designing randomized evaluations while addressing assignment, recruitment, compliance, attrition, ethical considerations, contamination, and practical implementation constraints.
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Applying quasi-experimental approaches including difference-in-differences, regression discontinuity, matching, interrupted time series, and comparative designs where appropriate.
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Developing longitudinal and repeated-measures evaluation designs for assessing changes in outcomes, implementation processes, and program effects over time.
Module 4: Sampling, Measurement, and Indicator Development
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Developing sampling strategies for beneficiaries, comparison groups, implementation sites, service providers, communities, organizations, and other evaluation populations.
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Designing valid and reliable evaluation measures for program exposure, implementation quality, outputs, outcomes, impacts, satisfaction, reach, and sustainability.
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Developing performance indicators and measurement frameworks that balance relevance, feasibility, sensitivity, comparability, and stakeholder information requirements.
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Addressing measurement error, instrument adaptation, cultural responsiveness, missing observations, response bias, and comparability across groups and evaluation periods.
Module 5: Evaluation Data Collection and Management
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Designing evaluation data collection protocols using surveys, interviews, focus groups, observations, administrative records, monitoring systems, and digital data sources.
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Integrating primary and secondary data sources while assessing their relevance, completeness, consistency, provenance, measurement characteristics, and potential limitations.
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Establishing evaluation databases, data dictionaries, coding systems, documentation procedures, quality checks, secure storage, and controlled access arrangements.
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Implementing fieldwork quality assurance through training, supervision, pilot testing, validation checks, monitoring procedures, and systematic documentation of data collection decisions.
Module 6: Quantitative Evaluation Data Analysis
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Conducting descriptive analysis of evaluation data to understand distributions, baseline characteristics, program reach, implementation patterns, outcomes, and changes over time.
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Applying appropriate statistical methods to compare groups, periods, implementation sites, exposure levels, and other relevant evaluation conditions.
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Using regression, longitudinal, multilevel, causal inference, and related analytical approaches to estimate relationships and evaluate potential program effects.
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Addressing confounding, selection bias, attrition, missing data, clustering, heterogeneous effects, sensitivity analysis, and uncertainty when interpreting quantitative evaluation findings.
Module 7: Qualitative and Mixed-Methods Evidence Generation
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Designing qualitative evaluation strategies to understand participant experiences, implementation processes, contextual influences, mechanisms, barriers, facilitators, and unintended consequences.
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Applying systematic coding, thematic analysis, framework analysis, case comparison, and other qualitative approaches for evaluating complex interventions and organizational processes.
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Integrating quantitative and qualitative findings through triangulation, joint displays, sequential designs, complementary analysis, and integrated interpretation.
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Using mixed-methods evidence to explain observed outcomes, understand differences between groups or sites, and identify mechanisms and contextual conditions influencing results.
Module 8: Impact, Contribution, and Economic Evaluation
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Examining causal inference principles and contribution-based approaches for assessing whether observed changes can reasonably be associated with an intervention or program.
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Assessing intervention effects while considering implementation variation, external influences, contextual change, alternative explanations, and limitations of available comparison strategies.
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Introducing cost-effectiveness, cost-benefit, cost-utility, and resource-use analysis for evaluations requiring assessment of economic value and resource implications.
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Conducting sensitivity and scenario analyses to examine how alternative assumptions, costs, outcomes, and uncertainty influence economic and impact evaluation findings.
Module 9: Emerging Evaluation Methods and Evidence Technologies
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Exploring rapid-cycle evaluation, adaptive evaluation, developmental evaluation, real-time learning systems, and approaches designed for complex and continuously changing interventions.
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Examining digital evaluation methods including remote data collection, mobile surveys, digital trace data, automated monitoring, dashboards, and technology-enabled evidence generation.
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Investigating artificial intelligence-assisted qualitative analysis, machine learning applications, automated data processing, and advanced analytics while considering transparency and human oversight.
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Addressing emerging issues in equity-centered evaluation, participatory evaluation, real-world evidence, data governance, responsible technology use, sustainability, and evaluation in resource-constrained settings.
Module 10: Evaluation Quality, Reporting, and Evidence Use
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Assessing evaluation quality through validity, reliability, credibility, transparency, methodological rigor, stakeholder relevance, reproducibility, and appropriate documentation of limitations.
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Developing transparent evaluation reports that distinguish findings, interpretations, assumptions, contextual explanations, limitations, and recommendations for different audiences.
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Communicating evaluation evidence through policy briefs, dashboards, presentations, learning products, technical reports, publications, and decision-oriented evidence summaries.
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Translating evaluation findings into program improvements, policy learning, resource decisions, organizational changes, future research questions, and practical evidence-use strategies.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
