Date: 6 July to 17 July 2026
Venue: Amathuba Online Learning Platform
Time: Refer to programme
Course fee is: R 9,450 per person
Only one discount will be applied per individual
| Category | Discount | Eligibility |
| Early-bird registration | 25% | Register + pay 12 weeks before start |
| Africa-based students | 15% | Enrolled in an African university |
| International students | 5% | Enrolled outside Africa |
| LMIC professionals | 15% | Based in LMIC African institutions |
| Group or institutional registrations: 3+ | 15% | Same institution |
This course aims to foster students' understanding of the concepts and mechanisms of the Discrete Choice Experiment (DCE) research method and its applications in health economics, particularly in MLICs.
This course introduces the fundamentals of Discrete Choice Experiment (DCE) methods and how they can be implemented as a quantitative ex-ante approach to investigate people's stated preferences among alternative healthcare interventions, services or policies.
Who Should Attend?
Individuals interested in DCEs with an application in health. This includes health economists, public health professionals, policy analysts, researchers, and students who wish to utilize DCEs within the health sector or other fields.
Entry Requirements: A degree in health sciences. economics, environmental health, social
sciences or equivalent foreign higher education. English proficiency is required.
Course Content:
The course is delivered over two weeks through a structured combination of lectures and interactive seminars. Each session builds progressively from foundational theory to applied modelling and policy interpretation, ensuring participants develop both conceptual understanding and practical skills.
Week 1: Foundations of Discrete Choice Experiments in Health Economics
• Session 1: Introduction to Discrete Choice Experiments in Health
• This session introduces stated preference methods and situates DCEs within health economics. Participants learn the historical development of DCEs, their theoretical foundations, and their relevance for health policy and economic evaluation. Practical health policy examples illustrate how DCEs inform decision making where market data are unavailable.
• Session 2: Economic Theory and Model Assumptions
• Participants explore the economic assumptions underlying DCEs, including utility theory and preference revelation. The session explains key parameters used in DCE analysis and links theory to empirical implementation through guided exercises and applied examples.
• Session 3: Experimental Design for Health DCEs
• This session focuses on the construction of choice experiments. Participants learn how to define attributes and levels, structure choice sets, and understand the implications of design choices for statistical efficiency and respondent burden. Health specific case studies guide applied design thinking.
• Session 4: Advanced Experimental Design Techniques
• Building on the previous session, this module covers full factorial designs, fractional designs, blocking, and efficiency considerations. Participants assess real health DCE designs and evaluate trade-offs between methodological rigor and feasibility.
• Session 5: Data Collection and Data Management for DCEs
• Participants learn how to plan and implement DCE data collection in health research. Topics include survey modes, covariates, ethical considerations, and data preparation. Practical sessions introduce data organisation and management using Stata, with hands on exercises using sample datasets.
Week 2: Modelling, Behavioural Insights, and Policy Applications
• Session 6: Data Formats and Sample Size Determination
• This session examines long and wide data formats used in DCE analysis and their implications for modelling. Participants learn how to determine appropriate sample sizes for health DCEs and apply sample size calculations in practical exercises.
• Session 7: Discrete Choice Models in Health Economics
• Participants are introduced to the Multinomial Logit model and its application in health economics. The session then extends to Random Parameter Logit and Latent Class models, highlighting how these approaches capture preference heterogeneity and support welfare analysis.
• Session 8: Behavioural Insights and Health Policy Applications
• This session integrates behavioural economics with DCE methods. Participants explore how nudges, social norms, and behavioural responses interact with health policy instruments such as taxation and regulation. Real world health policy examples demonstrate how DCEs generate policy relevant insights.
• Session 9: Practical Applications of DCEs in Health Policy
• Through detailed case studies, participants analyse how DCEs inform treatment choices, insurance design, and service delivery decisions. Ethical considerations in health DCEs are discussed, with guidance on responsible study design and interpretation.
• Session 10: Applied DCE Design and Participant Presentations
• In the final session, participants design a simple DCE based on a health policy problem. The course concludes with participant presentations, peer feedback, and a synthesis of key concepts. This session reinforces practical competence and prepares participants to apply DCE methods in their own research or policy work.
A digital certificate of attendance will be issued to participants on completion of the course.