The Use of Discrete Choice Experiments and MaxDiff (Best-Worst) Scaling

A discrete choice experiment (DCE) is a method for evaluating the influences on consumer decisions and behaviour. In a DCE consumers make choices between alternatives each of which is described by a range of attributes. Analysis determines the importance and impact of the attributes on the decisions. When one attribute is price then the analysis estimates the consumers’ willingness-to-pay and price elasticity for the other attributes.

Level 2 - runs over 5 days

Cam Rungie's research examines methods for analyzing decisions made by consumers. Through an ARC Discovery grant, and with colleagues Len Coote and Jordan Louviere, he further developed the analysis of discrete choice experiments. The past 30 years has seen big contributions in the study and understanding of discrete choice. Building on this work, his developments bring to the field the same forms of statistical analysis widely used elsewhere in the social sciences such as regression and factor analysis. Apart from this developmental research, he has extensive experience in applied research, marketing research and econometrics. He is an accomplished and passionate teacher of quantitative topics and supervisor of postgraduate research students. He has knowledge both the theory and application of discrete choice methods.

Course dates: Monday 8 February 2016 - 5 12 February 2016
Course status: Course completed (no new applicants)
Week 3
About this course: 

The course will be based around specific examples of DCEs and case studies in health, non-market valuation, transport and marketing. An example might be to measure the importance of each attributes of a health service in consumers’ decision to use the service. Other examples might be to measure the $ value to consumers of attributes such as the grape varieties and branding in the purchase of wine, or environmentally, the value of having plenty of fresh water in say the Murray River.


At the completion of the course candidates will be able to plan, conduct, discuss and critique DCEs. Technically this will include attribute selection, design, sampling, on-line survey tools, analysis using conditional logit, and interpretation. The course introduces choices processes and the fundamentals of random utility theory. In a well-designed DCE the analysis is straight forward. Extensive modelling is not required. The more important skills are planning and interpretation. Consequently, the course will provide basic training in the standard statistical packages for the design and analysis of DCEs. Brief overviews of discrete choice models, random coefficients, latent class and hierarchical Bayes will be included but detailed considerations of these advanced topics will be left to other courses.


DCEs are widely used in health economics, non-market valuation, transport research, marketing and the social sciences.

The course will include the use of MaxDiff, also known as Best-Worst Scaling, which is a special case of a DCE.

Course syllabus: 

Day 1
An introduction to a DCE. Case study. Candidates will complete a DCE, as respondents, and then analyse and interpret the results.


Day 2
The Steps in the DCE Process. Case study. Choice set design. Logit. Calculation and use of willingness-to-pay. Hands-on practice.


Day 3
Software for design, on-line surveys and analysis. Hands-on practice.

Day 4
Introduction to choice process, random utility theory, maximum likelihood, and choice models. Tutorial exercises. Discussion of random coefficients, latent class and Bayesian models.


Day 5
MaxDiff. The steps in the process. Case study. Hands-on practice. Interpretation.

Course format: 

This course is run in a classroom with participants using their own laptops.

Recommended Background: 

You should have an understanding of survey research and/or elementary statistics and data analysis, equivalent to the syllabuses of Fundamentals of Survey Research and/or Fundamentals of Statistics.

Recommended Texts: 

The following reference books are recommended:

  • Hensher D.A., J. M. Rose and W.H. Greene, 2005, Applied Choice Analysis: A Primer, Cambridge University Press, New York.
  • Louviere J.J., D.A. Hensher and J Swaite Jr, 2000, Stated Choice Methods: Analysis and Applications, Cambridge University Press, New York.
  • Orme, B.K., 2013, Getting Started with Conjoint Analysis: Strategies for Product Design and Pricing Research, Third Edition, Research Publishers, Madison. 
Course fees
Non Member: 
Full time student Member: 

Q: Do I need any prior knowledge before taking this course

A: You should have an understanding of survey research and/or elementary statistics and data analysis.


Q: How advanced should I be in my own research?

A: It is better if you have some understanding of the data you wish to collect in your research but this is not essential.


Q: Should I do any prior reading?
A: Prior reading is certainly not required but of course can be useful. See the books listed under recommended texts and readings and in particular Orme (2013).


Q: Do I need my own laptop computer?
A: Yes, there will be in-class exercises and readings requiring the use of a computer.


Q: Should it be operating Windows?

A: Yes, other platforms are not supported.


Q: Do I need any specific software?
A: No, but you will need the usual software including Excel, Word, a pdf reader and a web browser.


Q: Will I need to down load software during the course?

A: Yes, so be sure you have the appropriate privileges for your laptop.

Summer Program 2016

Lecture notes, readings and an annotate bibliography will be provided. There is not a text book.