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Visual Conjoint Designs

Member rate £492.50
Non-Member rate £985.00

Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked

*If you attended our Methods School in July/August 2023 or February 2024.

Course Dates and Times

Date: Monday 29 July – Friday 2 August 2024
Time: 9:30 – 12:30 CEST

Julia Schulte-Cloos

Philipps-Universität Marburg

Conjoint designs have become one of the most important tools in the social scientist's toolbox today, allowing researchers to understand political preferences and attitude formation in a wide range of contexts and conditions. This course introduces you to visual conjoint designs, which build on the key concepts of traditional conjoint designs but incorporate visual rather than textual stimuli, such as social media profiles, candidate images, or real online environments. You will learn how to design a visual conjoint experiment, how to use combinatorial and randomised designs to control choice sets and attributes when relying on visual material, and how to create and deploy your own visual conjoint experiments using free and open source software.

Purpose of the course

By the end of this course, you will have a comprehensive understanding of visual conjoint designs, their similarities and differences to traditional tabular conjoint designs and the main use cases of visual conjoint designs in the social sciences. You will understand the opportunities offered by choice-based visual conjoint experiments, recognise the requirements for conducting a visual conjoint research project, be able to design, conduct and deploy your own small-scale visual conjoint experiment, and master the skills necessary to analyse real data sets from visual conjoint experiments.

ECTS Credits

3 ECTS credits awarded for engaging fully in class activities.
1 additional ECTS credit awarded for completing a post-course assignment.

Instructor Bio

Julia Schulte-Cloos is Assistant Professor of Political Science at the University of Marburg. Her research interests include political behaviour, party competition, European politics, political communication, political sociology and computational social science. Methodologically, she is interested in data visualisation, machine learning and deep learning to uncover relationships in observational data or to generate experimental stimuli, designing complex online lab experiments to study attitudes in conjunction with behavioural measures, and developing open source tools to facilitate computational reproducibility.


Key topics covered

The course is structured around the key components of the research workflow of a visual conjoint design:

Topic 1: Design of the visual conjoint framework
  • Designing the attributes
  • Creating visual stimuli and mimicking real online environments
Topic 2: Randomisation strategy and choice set design
  • Constructing choice sets based on random designs
  • Constructing choice sets based on combinatorial designs
  • Constructing choice sets in an adaptive style
Topic 3: Running and deploying the conjoint experiment
  • Running your visual conjoint experiment locally
  • Deploying your visual conjoint experiment online
  • Integration with other survey frameworks
Topic 4: Analysing the experimental data
  • Effectively visualising the effects
  • Relying on advanced techniques for analysing effect heterogeneity

How the course will work online

The course consists of five online live sessions of 3 hours each. During these live sessions we will cover key concepts in visual conjoint research, engage in individual and collaborative coding tasks, and work on a data collection-ready MVP version of a visual conjoint design.

The instructor will also conduct live Q&A sessions for one-to-one consultations.

Prerequisite Knowledge

We will rely on open source software at every stage of the research workflow, from the conjoint conceptualisation stage, through the design and randomisation stage, to the actual deployment and hosting of a conjoint experiment. While this course relies heavily on various js libraries, no prior knowledge of JavaScript or TypeScript is required to take the course. For data analysis, we will rely on the R programming language and the tidyverse framework, so basic knowledge of these two frameworks is helpful, but not a prerequisite.

Learning commitment

As a participant in this course, you will engage in a variety of learning activities designed to deepen your understanding and mastery of the subject matter. While the cornerstone of your learning experience will be the daily live teaching sessions, which total three hours each day across the five days of the course, your learning commitment extends beyond these sessions.

Upon payment and registration for the course, you will gain access to our Learning Management System (LMS) approximately two weeks before the course start date. Here, you will have access to course materials such as pre-course readings. The time commitment required to familiarise yourself with the content and complete any pre-course tasks is estimated to be approximately 20 hours per week leading up to the start date.

During the course week, you are expected to dedicate approximately two-three hours per day to prepare and work on assignments.

Each course offers the opportunity to be awarded three ECTS credits. Should you wish to earn a 4th credit, you will need to complete a post-course assignment, which will involve approximately 25 hours of work.

This comprehensive approach ensures that you not only attend the live sessions but also engage deeply with the course material, participate actively, and complete assessments to solidify your learning.


This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.