Advanced Policy Evaluation and Government Programme Review Training Course

Course Introduction

The Advanced Policy Evaluation and Government Programme Review Training Course is an intensive professional programme designed to strengthen the capacity of government officials, policy analysts, programme managers, monitoring and evaluation specialists, researchers, and senior public-sector leaders to assess whether government policies and programmes are achieving their intended objectives. It provides practical frameworks for examining relevance, effectiveness, efficiency, coherence, sustainability, implementation quality, outcomes, impacts, and public value.

Governments invest significant financial, human, institutional, and technological resources in policies and programmes intended to improve economic development, public services, social welfare, infrastructure, health, education, environmental protection, employment, and institutional performance. However, expenditure and activity alone do not demonstrate success. Effective evaluation enables governments to understand what works, for whom, under what conditions, at what cost, and why particular interventions succeed, fail, or produce unintended consequences.

The programme covers the complete evaluation and programme-review cycle, beginning with evaluation scoping, policy and programme theory, evaluation questions, research design, indicator development, data collection, evidence appraisal, implementation assessment, outcome measurement, impact analysis, cost analysis, and synthesis of findings. Participants will learn how to select appropriate evaluation methodologies and produce credible findings that can directly support policy decisions, resource allocation, programme redesign, scaling, continuation, or termination.

A strong emphasis is placed on practical government programme review. Participants will examine how to conduct structured reviews of programme performance, institutional arrangements, budgets, delivery systems, stakeholder experiences, implementation risks, governance mechanisms, and achieved results. The course also addresses common challenges such as weak theories of change, inadequate baseline information, poor data quality, unclear indicators, attribution difficulties, fragmented responsibilities, limited evaluation capacity, political sensitivities, and resistance to critical findings.

The programme incorporates emerging developments in evaluation technology and evidence systems. Artificial intelligence, machine learning, predictive analytics, real-time dashboards, administrative data, geospatial analysis, digital monitoring, automated data collection, process mining, and evaluation platforms can significantly improve the speed and depth of government programme reviews. Participants will explore these opportunities while addressing responsible AI, privacy, data governance, algorithmic bias, research integrity, transparency, and the need for human judgement in interpreting evidence.

By the end of the programme, participants will be equipped to design rigorous evaluations, conduct government programme reviews, assess implementation and outcomes, analyse costs and benefits, communicate findings to decision-makers, and convert evaluation evidence into practical improvement actions. The course supports a culture of evidence-informed government in which evaluation is used not simply for accountability, but as a strategic instrument for learning, resource optimization, programme improvement, institutional performance, and measurable public impact.

Duration

10 days

Who Should Attend

• Ministers, permanent secretaries, principal secretaries, directors-general, commissioners, and senior government executives responsible for policy and programme performance.

• Senior monitoring and evaluation directors, managers, advisers, analysts, specialists, and programme-review professionals in public institutions.

• Government policy analysts, economists, researchers, statisticians, planners, and strategic advisers involved in evaluating public policies and programmes.

• Programme and project managers responsible for implementation, performance management, results measurement, programme improvement, and delivery assurance.

• Officials working in central policy units, planning departments, cabinet offices, delivery units, evaluation offices, and government performance institutions.

• Budget, finance, public investment, audit, procurement, and resource-allocation professionals who require evidence on programme value and effectiveness.

• Public-sector data analysts, data scientists, statisticians, information managers, and digital-government specialists supporting evaluation and performance systems.

• County, municipal, regional, and local-government officials responsible for evaluating public services, development programmes, projects, and institutional performance.

• Development partners, consultants, researchers, academics, and technical advisers supporting government monitoring, evaluation, policy review, and programme improvement.

• Regulatory and compliance professionals responsible for assessing whether government interventions achieve intended behavioural, economic, social, or administrative outcomes.

• Internal auditors, performance auditors, quality-assurance professionals, and institutional-review specialists involved in public-sector programme assessment.

• Public-sector leaders preparing for advanced responsibilities in policy evaluation, government programme management, performance improvement, strategic planning, and evidence-informed decision-making.

Course Objectives

• Develop advanced capabilities for designing and managing rigorous evaluations of government policies, programmes, projects, reforms, and public-sector interventions.

• Assess policy and programme relevance, effectiveness, efficiency, coherence, sustainability, implementation quality, outcomes, impacts, and contribution to public value.

• Develop clear evaluation questions, theories of change, logic models, results frameworks, indicators, assumptions, evaluation criteria, and evidence requirements.

• Select appropriate qualitative, quantitative, mixed-methods, experimental, quasi-experimental, comparative, and theory-based evaluation approaches.

• Strengthen the ability to assess programme implementation, institutional capacity, governance arrangements, resource utilization, operational performance, and delivery bottlenecks.

• Apply advanced methods for measuring outcomes and impacts while addressing attribution, contribution, counterfactuals, selection bias, confounding factors, and contextual influences.

• Evaluate programme costs, efficiency, value for money, fiscal implications, resource allocation, cost-effectiveness, and potential opportunities for optimization or reprioritization.

• Use administrative data, surveys, qualitative evidence, geospatial information, digital monitoring, performance dashboards, and other data sources to strengthen evaluation quality.

• Apply artificial intelligence, predictive analytics, machine learning, process mining, and automated evidence tools responsibly within modern government evaluation systems.

• Develop high-quality evaluation reports, executive summaries, management responses, recommendations, decision papers, and evidence products for senior government stakeholders.

• Strengthen the use of evaluation findings for policy revision, programme redesign, resource allocation, scaling, institutional learning, accountability, and continuous improvement.

• Establish sustainable evaluation systems that promote evidence-informed decision-making, learning cultures, transparency, accountability, adaptive management, and measurable government performance.

Comprehensive Course Outline

Module 1: Foundations of Policy Evaluation and Government Programme Review

• Examine the purpose, principles, functions, standards, and strategic importance of evaluating government policies, programmes, projects, and institutional interventions.

• Distinguish monitoring, evaluation, audit, inspection, research, performance review, programme assessment, impact assessment, and policy analysis.

• Examine how evaluation contributes to accountability, organizational learning, resource optimization, programme improvement, policy adaptation, and public-sector performance.

• Assess emerging evaluation challenges involving complex policies, rapid technological change, climate risks, AI, interconnected programmes, fiscal constraints, and increasing demands for measurable public results.

Module 2: Policy and Programme Theory, Logic Models and Theories of Change

• Develop clear programme theories explaining how government interventions are expected to produce outputs, behavioural changes, outcomes, impacts, and longer-term public benefits.

• Construct logic models linking inputs, activities, outputs, outcomes, impacts, assumptions, external factors, risks, and causal mechanisms.

• Identify weaknesses in programme logic involving unrealistic assumptions, unclear causal pathways, missing activities, inappropriate indicators, and weak connections between interventions and expected results.

• Explore emerging approaches involving systems mapping, causal-loop diagrams, AI-assisted theory development, dynamic programme models, and computational policy simulation.

Module 3: Evaluation Questions, Scope and Evaluation Design

• Develop evaluation questions that are clear, decision-relevant, measurable, feasible, and aligned with the information needs of government decision-makers.

• Define evaluation scope by considering policy objectives, programme components, target populations, geographic coverage, implementation periods, resources, and intended uses of findings.

• Select evaluation designs according to the purpose, context, intervention characteristics, evidence availability, ethical requirements, resources, and required level of causal inference.

• Explore emerging evaluation-design approaches involving adaptive evaluation, rapid-cycle evaluation, real-time learning, digital experimentation, and AI-supported research planning.

Module 4: Evaluation Data, Indicators and Measurement Frameworks

• Develop performance and evaluation indicators that accurately capture implementation progress, outputs, outcomes, impacts, quality, efficiency, equity, and sustainability.

• Assess indicator quality using relevance, validity, reliability, sensitivity, measurability, comparability, timeliness, and practical usefulness for decision-making.

• Integrate administrative records, surveys, financial data, programme databases, service-delivery information, geospatial data, and qualitative evidence into evaluation systems.

• Explore emerging measurement technologies involving real-time administrative data, automated data collection, digital sensors, geospatial analytics, AI-assisted data validation, and integrated dashboards.

Module 5: Quantitative Evaluation Methods and Impact Measurement

• Apply quantitative evaluation techniques including descriptive analysis, trend analysis, regression concepts, comparative analysis, statistical testing, and outcome measurement.

• Examine experimental and quasi-experimental approaches for estimating programme effects, including randomization, comparison groups, difference-in-differences, matching, and interrupted time series.

• Address threats to validity involving selection bias, attrition, confounding, measurement error, spillovers, non-compliance, and changes in implementation conditions.

• Explore emerging quantitative evaluation technologies involving machine learning, predictive modelling, automated statistical analysis, synthetic controls, and large-scale administrative-data evaluation.

Module 6: Qualitative Evaluation and Stakeholder-Centred Programme Review

• Apply interviews, focus groups, case studies, observation, document analysis, participatory approaches, and other qualitative methods to understand implementation and programme experiences.

• Assess stakeholder perspectives involving beneficiaries, service providers, government officials, communities, businesses, civil society, implementation partners, and other affected groups.

• Analyse qualitative evidence systematically through coding, thematic analysis, triangulation, comparative cases, narrative analysis, and interpretation of contextual factors.

• Explore emerging qualitative evaluation technologies involving automated transcription, natural-language processing, AI-assisted coding, sentiment analysis, and large-scale text analysis.

Module 7: Programme Implementation and Process Evaluation

• Assess whether programmes are being implemented as designed and identify deviations, bottlenecks, capacity constraints, coordination problems, and operational weaknesses.

• Examine implementation fidelity, reach, coverage, service quality, timeliness, institutional readiness, beneficiary access, staff capability, and resource availability.

• Identify how contextual factors, organizational culture, leadership, procurement, financing, regulations, technology, and stakeholder behaviour influence implementation results.

• Explore emerging process-evaluation technologies involving process mining, digital workflow analysis, real-time implementation monitoring, AI-supported anomaly detection, and automated delivery alerts.

Module 8: Efficiency, Cost-Effectiveness and Value-for-Money Evaluation

• Assess programme efficiency by comparing resources used with outputs and outcomes while considering quality, coverage, timeliness, and implementation conditions.

• Apply cost-effectiveness, cost-benefit, economic-impact, fiscal-impact, and value-for-money approaches to major government programmes and public investments.

• Identify opportunities for reducing waste, duplication, inefficient processes, excessive costs, underutilized resources, and poorly targeted programme activities.

• Explore emerging economic-evaluation technologies involving AI-assisted modelling, automated cost analysis, predictive expenditure analytics, microsimulation, and dynamic scenario modelling.

Module 9: Equity, Inclusion and Distributional Evaluation

• Assess how policies and programmes affect different population groups according to income, geography, gender, age, disability, vulnerability, access, and other relevant dimensions.

• Examine whether programme benefits, costs, opportunities, services, and risks are distributed fairly across target populations and regions.

• Apply distributional analysis, disaggregated indicators, beneficiary research, equity-focused evaluation, and inclusion-sensitive evaluation frameworks.

• Explore emerging approaches involving geospatial analytics, population modelling, AI-supported segmentation, digital inclusion indicators, and real-time equity monitoring.

Module 10: Data Quality, Evidence Appraisal and Evaluation Integrity

• Establish data-quality frameworks addressing accuracy, completeness, consistency, timeliness, reliability, validity, comparability, security, and appropriate documentation.

• Identify risks involving missing information, inconsistent definitions, weak baselines, unreliable administrative records, selective reporting, data manipulation, and unsupported conclusions.

• Apply evidence-triangulation methods to strengthen confidence in evaluation findings by integrating multiple data sources, methodologies, stakeholder perspectives, and analytical approaches.

• Examine emerging integrity challenges involving synthetic data, automated analysis, AI-generated evidence, algorithmic bias, fabricated sources, privacy, cybersecurity, and responsible data use.

Module 11: Policy Review, Regulatory Evaluation and Programme Relevance

• Assess whether government policies and programmes remain relevant to current social, economic, institutional, technological, environmental, and political conditions.

• Review policy assumptions, objectives, instruments, implementation arrangements, target groups, institutional responsibilities, and interactions with other government interventions.

• Identify obsolete, ineffective, duplicative, poorly targeted, excessively costly, or unintended policy and programme components requiring reform or replacement.

• Explore emerging policy-review methods involving AI-assisted regulatory analysis, policy coherence mapping, automated evidence synthesis, horizon scanning, and continuous policy intelligence.

Module 12: Advanced Programme Review and Institutional Performance Assessment

• Conduct comprehensive programme reviews examining strategic alignment, governance, implementation, financial management, institutional capacity, risk management, performance, and sustainability.

• Assess the effectiveness of programme governance structures, leadership arrangements, accountability mechanisms, partnerships, procurement systems, and coordination processes.

• Develop review frameworks for determining whether programmes should be continued, redesigned, expanded, consolidated, transferred, integrated, or discontinued.

• Explore emerging review technologies involving digital programme twins, AI-assisted institutional diagnostics, automated performance benchmarking, predictive risk assessment, and integrated review dashboards.

Module 13: Evaluation Reporting, Executive Communication and Recommendation Development

• Prepare evaluation reports that present methodology, evidence, findings, limitations, conclusions, lessons, recommendations, management responses, and implications for future policy decisions.

• Develop concise executive summaries and decision products that translate technical evaluation findings into clear information for ministers, senior officials, boards, and programme leaders.

• Formulate recommendations that are evidence-based, feasible, prioritized, measurable, time-bound, institutionally appropriate, and linked to identified problems.

• Explore emerging reporting technologies involving automated visualization, interactive dashboards, AI-assisted report production, evidence maps, executive briefing systems, and natural-language analytics.

Module 14: Evaluation Use, Learning and Adaptive Programme Management

• Establish mechanisms for ensuring evaluation findings are actively considered in policy decisions, budgeting, programme redesign, implementation adjustments, and institutional planning.

• Develop learning systems that capture lessons from implementation, document successful practices, identify failures, and support replication or adaptation of effective interventions.

• Apply adaptive-management approaches that allow government programmes to respond to emerging evidence, changing conditions, implementation experience, and stakeholder feedback.

• Explore emerging learning technologies involving AI knowledge systems, automated lesson extraction, organizational knowledge graphs, decision-support tools, and real-time programme-learning platforms.

Module 15: Evaluation Governance, Institutional Capacity and Evaluation Systems

• Design government evaluation functions with clear mandates, standards, governance arrangements, quality-assurance mechanisms, independence, accountability, and appropriate decision-use requirements.

• Develop institutional evaluation capacity through workforce development, methodological standards, evaluation guidelines, data systems, knowledge management, partnerships, and professional development.

• Establish evaluation calendars, review priorities, quality controls, dissemination processes, management-response mechanisms, and systems for tracking recommendation implementation.

• Explore emerging evaluation-system models involving centralized evidence platforms, AI-enabled evaluation offices, integrated government data environments, and continuous performance intelligence.

Module 16: Integrated Policy Evaluation and Government Programme Review Strategy

• Integrate evaluation design, data management, implementation review, impact analysis, efficiency assessment, equity analysis, reporting, learning, and decision support into a coherent government evaluation system.

• Conduct institutional maturity assessments covering evaluation governance, methodologies, workforce capabilities, data availability, technology, quality assurance, evidence use, and organizational learning.

• Develop practical evaluation strategies and review roadmaps linking priority policies and programmes with evaluation questions, methodologies, resources, timelines, responsibilities, and expected decisions.

• Prepare government evaluation systems for emerging environments involving AI-enabled evaluation, real-time evidence, predictive programme management, automated monitoring, and adaptive public-sector decision-making.

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.