Introduction to Discrete Choice Experiments (DCE) in Stata


Format: Online
Language: English / Bahasa Indonesia

Course Description

This course will introduce Discrete Choice Experiments (DCE) and how to execute them using Stata. DCE is a widely used method in economics, ecology, health, marketing, and social sciences to understand individual preferences and choices. Participants will learn the theoretical foundations of DCE and practical skills to design, analyze, and interpret DCE outcome analysis using Stata. The course will be conducted in two two-day classes.


Basic knowledge of statistics and econometrics is recommended but not required.
Laptop or Computer

Supporting Materials

  • Hole, A. R. (2007). Fitting mixed logit models by using maximum simulated likelihood. The stata journal, 7(3), 388-401.
  • Train, K. E. (2003). Discrete Choice Methods with Simulation. Cambridge University Press.
    Colombo, S., Hanley, N., & Louviere, J. (2009). Modeling preference heterogeneity in stated choice data: An analysis for public goods generated by agriculture. Agricultural Economics, 40(3), 307–322. 1574-0862.2009.00377
  • McFadden, D. L., and K. E. Train. 2000. Mixed MNL models for discrete response. Journal of Applied Econometrics 15: 447–470.;2-1.
  • Ndunda, E. N., & Mungatana, E. D. (2013). Evaluating the welfare effects of improved wastewater treatment using a discrete choice experiment. Journal of Environmental Management, 123, 49–57.

Rancangan Jadwal

Day 1

Session 1 – Introduction to Discrete Choice Experiment (DCE)

  • Overview of DCE: What are Discrete Choice Experiments, and How to select an appropriate DCE for our research?
  • Understanding Basic Concepts: Attributes, levels, choice sets, and alternatives
  • Examples of DCE applications

Session 2 – Designing a Discrete Choice Experiment

  • Identifying research questions and objectives
  • Defining attributes and levels
  • Creating choice sets
  • Experimental design principles
  • Practical exercise: Design a DCE

Session 3 – Data Collection and Survey Design

  • Survey questionnaire design
  • Random utility theory and the choice model

Session 4 – STATA installation
• STATA installation
• Setting up data in STATA

Day 2

Session 5 – Modeling Individual Choices

  • Model estimation in Stata
  • Model interpretation and coefficients
  • Practical exercise: Estimating a choice model in Stata

Session 6 – Advanced Choice Models

  • Multinomial Logit (MNL) models
  • Mixed Logit models

Session 7 – Attribute and Policy Analysis

  • Marginal effects and willingness-to-pay (WTP)
  • Interpretation of results & saving outcomes
  • Practical exercise: Analyzing and interpreting DCE results
  • Feedback and discussion.