Discrete Choice Modelling, 7.5 credits

Modellering av diskreta val, 7.5 hp


Main field of study

Computational Social Science

Course level

Second cycle

Course type

Single subject and programme course


Richard Öhrvall

Course coordinator

Richard Öhrvall

Director of studies or equivalent

Carl Nordlund


ECV = Elective / Compulsory / Voluntary
Course offered for Semester Weeks Language Campus ECV
F7MCD Computational Social Science, Master's Programme - First and main admission round 2 (Spring 2024) 202404-202413 English Norrköping, Norrköping C
F7MCD Computational Social Science, Master's Programme - Second admission round (open only for Swedish/EU students) 2 (Spring 2024) 202404-202413 English Norrköping, Norrköping C

Main field of study

Computational Social Science

Course level

Second cycle

Advancement level


Course offered for

  • Master´s Programme in Computational Social Science

Entry requirements

A bachelor's degree or equivalent in the humanities, social-, cultural-, behavioural-, natural-, computer-, or engineering-sciences.
English corresponding to the level of English in Swedish upper secondary education (English 6/B).

Intended learning outcomes

After completion of the course, the student should on an advanced level be able to:

  • Identify problems most suitably modeled with discrete choice methods;
  • Describe which models are suitable for specific applications using panel or cross-sectional data;
  • Develop appropriate discrete choice model specifications;
  • Create appropriate data structures for estimating discrete choice models;
  • Critically review and interpret model results of statistically complex discrete choice models;
  • Use statistical software to estimate discrete choice models, calculate predictions, and interpret and analyze results. 

Course content

This course enables students to perform their own empirical research using discrete choice methods. Students learn how to create discrete choice datasets, estimate discrete choice models, including binomial, multinomial, and conditional logistic regression, and interpret model output. The focus will be on the practical aspects of modeling. During intensive computer labs, hands on experience will be provided using real data drawn from examples in the areas of consumer choice, migration, and labor market mobility. More advanced models for handling panel data and unobservable heterogeneity, as well as identification of latent groups will be examined and deployed. Applications to counterfactual and agent-based simulation will also be explored during lab sessions.


Teaching and working methods

The teaching consists of lectures, readings, computor labs and seminars. Homework and independent studies are a necessary complement to the course.

Language of instruction: English


The course is examined through written assignments, active participation on seminars, computer labs and a final written individual assignment.

Detailed information about the examination can be found in the course’s study guide. 

If special circumstances prevail, and if it is possible with consideration of the nature of the compulsory component, the examiner may decide to replace the compulsory component with another equivalent component.

If the LiU coordinator for students with disabilities has granted a student the right to an adapted examination for a written examination in an examination hall, the student has the right to it.

If the coordinator has recommended for the student an adapted examination or alternative form of examination, the examiner may grant this if the examiner assesses that it is possible, based on consideration of the course objectives.

An examiner may also decide that an adapted examination or alternative form of examination if the examiner assessed that special circumstances prevail, and the examiner assesses that it is possible while maintaining the objectives of the course.

Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.

Students who have passed an examination may not retake it in order to improve their grades.



Other information

Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus. 

The course is conducted in such a way that there are equal opportunities with regard to sex, transgender identity or expression, ethnicity, religion or other belief, disability, sexual orientation and age.

If special circumstances prevail, the vice-chancellor may in a special decision specify the preconditions for temporary deviations from this course syllabus, and delegate the right to take such decisions.


Institutionen för ekonomisk och industriell utveckling
Code Name Scope Grading scale
HEM2 Take Home Exam 3.5 credits EC
ASS1 Assignments 4 credits EC

Regulary literature


Long, J. Scott., (1997) Regression models for categorical and limited dependent variables SAGE Publications, Inc.

ISBN: 9780803973749, 0803973748

Additional literature


Train, Kenneth, (2009) Discrete choice methods with simulation 2nd ed. Cambridge University Press

ISBN: 0511592493, 9780511592492, 9780521766555, 9780521747387


Wickham, Hadley, Mine Çetinkaya-Rundel & Garret Grolemund, (2023) R for data science : import, tidy, transform, visualize and model data. 2nd O'Reilly Media

ISBN: 9781491910399


This tab contains public material from the course room in Lisam. The information published here is not legally binding, such material can be found under the other tabs on this page.

There are no files available for this course.