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2024/25 Taught Postgraduate Module Catalogue

TRAN5282M Choice Modelling and Stated Preference Survey Design

15 creditsClass Size: 50

Module manager: Stephane Hess

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2024/25

Pre-requisite qualifications

General mathematical/statistical skills essential, basic knowledge of R helpful

This module is not approved as an Elective

Module summary

Choice modelling techniques are used widely to produce insights into choice behaviour, often with a view to providing guidance to policy makers, e.g. as an input to cost-benefit analyses. The models can be estimated either on data containing real observed choices, or data collected in hypothetical choice scenarios. The latter approach, known as stated preference data, is widespread and an appealing solution in cases where data on real choices are difficult to obtain, for example when looking at behaviour in the presence of a new transport mode. This course covers the essential principles involved in the specification, estimation and interpretation of choice models, covering topics from basic structures right through to state-of-the-art techniques. Similarly, the course covers different available techniques for generating designs for stated preference surveys, and also addresses the topics of data collection.The course involves a mix of lectures and practicals, providing extensive hands-on experience with model estimation as well as survey design.


To provide a thorough grounding in choice modelling and stated preference survey design.

Learning outcomes
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:

1. Understand the basics of experimental design

2. Understand the theory behind the development and application of choice models

3. Create experimental designs

4. Analyse choice modelling data

5. Justify the decisions made in survey design and model specification

6. Interpret model outputs

Skills learning outcomes

On successful completion of the module students will have demonstrated the following skills learning outcomes:

7.Technical skills – model specification and estimation; generating experimental designs for stated choice surveys

8.Academic skills – reflection, critical thinking, academic writing, academic integrity

9.Digital skills – digital proficiency and productivity

10.Sustainability skills – critical thinking

11.Work ready skills – technical / IT skills, problem solving and analytical skills, creativity, critical thinking


Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module

Teaching methods

Delivery typeNumberLength hoursStudent hours
Private study hours120.00
Total Contact hours30.00
Total hours (100hr per 10 credits)150.00

Opportunities for Formative Feedback

Feedback provided during computer labs.

Methods of assessment

Assessment typeNotes% of formal assessment
Total percentage (Assessment Coursework)100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 10/04/2024


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