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Discrete Choice Analysis

The Discrete Choice Analysis program provides an introduction to the main techniques of discrete choice analysis and the design of stated choice experiments.

Almost without exception, everything we undertake involves a choice. In recent years there has been a growing interest in the development and application of quantitative statistical methods to study choices made by individuals or groups with the purpose of gaining a better understanding both of how choices are made and of forecasting future choices.

Discrete choice analysis and stated choice methods are widely used across diverse fields to study the behavioural responses of individuals, households and other organisations.

This course is designed to provide both theory and practical experience in the building and estimating of simple and more advanced choice models, as well as in generating stated choice experimental designs.

The course also covers future developments in the field of discrete choice analysis; for example, nonlinear in parameters models, risk attitude, and perceptual conditioning. While we will cover theory, we will also spend significant time in a computer lab to build models using real data and generate simple surveys.

The techniques you will gain in this course are transferable to other areas of research.

This is both a practical and theory based course. We will be teaching how to estimate discrete choice models using software, and how to interpret the outputs using a real-life data set for this. We will cover:

  • multinomial logit, nested logit, latent class, error components, and mixed logit (random parameters) models
  • widely used statistical tests, such as Delta, Wald and the Krinksy and Robb procedure
  • an introduction to the design of stated choice surveys, including generation of orthogonal and efficient experimental designs (we will use a variety of applications to illustrate the techniques).

The course also includes presentations of the background theory for discrete choice modelling, different methods for combining survey data, and the most recently developed modelling techniques including nonlinear in parameters models, cumulative prospect theory models and process heuristics.

The course explores the entire process, including experimental design, model building, and model estimation (with both stated preference and revealed preference data). Recent advances in tools and methods have been used to model individual behaviour and to analyse market shares and change in demand in response to pricing and income and changes in available choice sets and choice characteristics.

Presentations are augmented by hands-on problems with real data sets. Applications in model estimation will be developed using NLOGIT 6.0 / LIMDEP 11.0 software (Econometric Software, Inc). A 10 per cent discount on the price for this software will be available to course participants wishing to purchase. Experimental designs will be generated using the Ngene 1.2 software (ChoiceMetrics Pty Ltd).

This practical course will be useful for research across a broad range of fields in which consumer demand and choice is of interest, including:

  • accounting
  • economics
  • engineering
  • environmental science
  • finance
  • health services
  • logistics
  • marketing
  • planning
  • transportation 
  • tourism. 

The course is intended for academics and practitioners in government and industry. An appreciation of basic statistical concepts is useful, but not essential for this course. Please contact Andrew Collins if you have any concerns.

Many attendees have come with no background in discrete choice modelling, but have completed the course at a level that has enabled them to develop and estimate a range of choice models immediately.

This course will be presented by four of the world’s leading academics in the field of discrete choice analysis:

In 2021, the Discrete Choice Analysis program is being held from Monday 26 to Friday 30 July. 

DCA fees Early Bird Normal
Industry and government 3465 3850
Academic 2475 2750
Student 1980 2200

Early bird registration prices end on 28th May 2021.

All fees are inclusive of GST and including course materials (including "Applied Choice Analysis: A Primer" by David Hensher, John Rose and Bill Greene, Cambridge University Press, 2nd Edition 2015).

Group discounts are available for three or more persons from the same organisation. Please email: business.itls@sydney.edu.au for further information.

Cancellations: No refund will be available for cancellations made after Monday 12 July 2021.

DCA 2021 has ended.

Please email business.itls@sydney.edu.au to place your name for “Expression of interest” for DCA 2022.

  • "Fantastic course for anyone interested in or practising in discrete choice analysis. It was very well structured and had a good balance of theory/lectures and hands-on workshops. Having the material presented by leaders in the field also meant that highly technical and difficult material was conveyed clearly and in an intuitive and easy (relatively!) to follow way."
  • "Thanks to the team for making my journey into discrete choice analysis an enjoyable one. You have a really good process, the presenters know their stuff, this is really state-of-the-art in postgraduate/executive education. I found the content, scope and pace demanding (the course would be a waste of time otherwise) but the structure and approach of the presenters provided a good remedy for this. Again, many thanks for a challenging and enjoyable week." Dr Chris Batstone, Senior Resource and Environmental Economist, The Cawthron Institute, New Zealand
  • "I'm really pleased I attended and am confident in taking my analysis forward more robustly. I've already started using the reference material and NLOGIT for some of my analysis. Thank you again for such a well-run, well-taught course. I'd recommend it to anyone doing choice modelling." Anna Robak, PhD Candidate, Centre for Regulation and Market Analysis, The University of South Australia
  • "I thoroughly enjoyed the five-day course. It's not often I attend a course and learn something in every session. I certainly did that with this one. Now all I have to do is apply what I have learnt by developing choice experiments for my research." Julia Logan, Head of Department, Patient Information Management Services, Child and Adolescent Health Service, Princess Margaret Hospital for Children, Perth