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From A-to-Z: Executing experimental methods with a focus on discrete choice experiments

Course code
Course type
Doctoral Program Lecture
Weekly Hours
FS 2020
Prof. Dr. Christian Schlereth
Please note that exchange students obtain a higher number of credits in the BSc-program at WHU than listed here. For further information please contact directly the International Relations Office.
Day 1:
  • Introduction to experimental methods and discrete choice experiments (Session 1 & 2)
  • Theoretical roots & experimental set-up decisions (Session 3)
  • Hands on: Questionnaire design and survey setup (Session 4)

Day 2:

  • Properties of suitable design and design generation techniques (Session 5)
  • Estimation I: Basic techniques using Maximum Likelihood (Session 6)
  • Estimation II: Advanced techniques using hierarchical bayes (Session 7)
  • Hands on: Design generation & estimation (Session 8)

Day 3:

  • Test for validity, interpretation and counterfactual simulations (Session 9)
  • Advanced topics (Session 10)
  • Hands on: Completion of study-preparations (Session 11)
  • Presentation: Defense of planned study (Session 12)

Relevance of the course

Discrete Choice Experiments (DCEs) are an essential evidence based research tool to better understand and predict individual and group decisions made by managers, organizations, and consumers. DCEs are suited to empirically study frequently asked research questions from many disciplines, such as, e.g., consumers’ trade-off between product characteristics and price in Marketing; households’ trade-off between interest rates and risk in Household Finance; and app-users’ trade-off between capabilities and privacy concerns in Information Systems. This PhD course will enable researchers to set up and apply their own discrete choice experiment to their research questions.

Even though this course has a particular focus on preference measurement using discrete choice experiments, most of its studied and applied contents are generalizable to other experimental methods.


During the course, you will apply the following software:

Prior experience with some of the software mentioned above is certainly helpful, but not a pre-condition. I developed this course such that also participants with no prior experience with any of the software can also follow.


Before the course, please prepare the following:

About 2 weeks, before the course starts, I will provide a guidance on selecting a topic for your study.

Date Time
Tuesday, 30.06.2020 12:00 - 18:45
Wednesday, 01.07.2020 08:30 - 16:30
Wednesday, 08.07.2020 08:30 - 16:30
This course will be of interest to researchers from any discipline (e.g., Marketing, Entrepreneurship, Finance, and Information Systems) with no or limited experience in the application of experiments and DCEs in particular. It will enable participants to:
  • Identify suitable experimental methods
  • Gain a thorough understanding of the steps required for the planning and execution of experiments
  • Gain hands-on experience on each step of setting up a discrete choice experiments.
  • Understand the theoretical basis
  • Learn about design generation techniques, study set-up, and interpretation of results
  • Ability to critically reflect methodological issues
Jordan Louviere, David Hensher, Joffre Swait (2000), Stated choice methods: Analysis and application.Kenneth Train (2008), Discrete choice methods using simulations, second edition.Additional reading material will be provided in class
Beside attendance in this course, participants have to demonstrate their ability to conduct their own discrete choice experiment (either alone or in groups of 2 people) on the topic of their choice and submit a research report. Two types of submissions are possible:

Small empirical project

  • Conduct a small discrete choice experiment with >30 respondents. Submit this project as a report of up to 15 pages (20 pages for groups; times new roman, 12pt, either English or German) containing a motivation for the research question(s), brief summary of the related literature, study set-up, results, and interpretation.

Full research project

  • This type of submission is suitable if data were already collected and paper is intended to be submitted to a journal with a review process. In this case, the paper may exceed the limit of 15 or 20 pages.

For both submission types, participants are encouraged to carefully select the topic of their study by aligning it with their own doctoral research. The course is designed such that the most challenging part – namely the careful planning of the study set-up – is done and supported by me throughout the hands on sessions at the end of each day. Participants also have to present and discuss their plans of the study at the end of the third day, before collecting data. This approach serves to

  • Foster learning and understanding of the presented theory, methods and software
  • Increase the quality of the study
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