Introduction to Conjoint Experiments

online course

Learn about the logic and basic notions of conjoint analysis (CJA), and how to apply these by looking at different applications of conjoint analysis within the field of behavioural sciences.

Need to Know

You must have intermediate familiarity with the basis of experimental design, survey experiments and regression analysis. While example datasets and full syntax codes will be provided, intermediate knowledge of R is expected.

You need to know how to:
- read datasets in R;
- work with data frames;
- perform basic data manipulation; and
- run basic statistical analyses such as linear or logistic regression.

More advanced knowledge of statistical computing, such as writing functions and loops, is helpful but not essential.

Note: This course will give an applied introduction to conjoint experiments. If you are already familiar with conjoint analysis or you are interested in the broader theory behind conjoint and factorial experiments, this is not the right course for you.

In Depth - Key topics covered

The course is structured around eight key topics:
1. General idea of conjoint experiments. The logic underlining conjoint experiments, their development, and the reasons behind the rising interest in them within the social sciences.
2. Outcome framework at the base of modern causal analysis. In particular, the fundamental problem of causal inference (Holland, 1986) within the framework of conjoint analysis.
3. Different ways to measure individual preferences in a conjoint experiment. The focus will be on Choice-Based Conjoint measurement but other measurements will be discussed (e.g. Rating, Ranking, Combined, and Adaptive).
4. Overview of different types of conjoint design, their use, and their limitations. Alternatives, Choice Sets and Context design will be explained, paying particular attention to the design of conjoint alternatives.
5. The construction and implementation of a conjoint experiment. You will learn how to design alternatives with attributes/levels constraints and randomisation using a ready to use JavaScript/Python program and R.
6. Introduction to simple workflows to deploy a conjoint design using Qualtrics.
7. Overview of different methods to analyse a conjoint experiment with specific focus on AMCEs, marginal mean and omnibus F-test. We will briefly discuss subgroup differences and visualisation.
8. Recent advances in conjoint analysis including power analysis and the usage of mixture modelling to discover treatment heterogeneity.

Note: This course will give an applied introduction to conjoint experiments. If you are already familiar with conjoint analysis or you are interested in the broader theory behind conjoint and factorial experiments, this is not the right course for you.

Course leader

Alberto Stefanelli is a FWO PhD Fellow at KU Leuven. He is particularly interested in graphical causal models, standardisation techniques and matching algorithms, text analysis, experimental and semi-experimental design, and machine and deep learning.

Target group

With a maximum of 16 participants, our teaching team can provide personalised attention to each individual, catering to their specific needs. The course is designed for a demanding audience, including researchers, professional analysts, and advanced students.

Course aim

This course offers an applied introduction to Choice-Based Conjoint, along with hands-on experience in lab sessions and aims to:
- provide you with a basic understanding of the structure, logical underpinnings, basic notions, and analytical goals of conjoint analysis.
- identify areas of application where conjoint analysis could be successfully implemented.
- critically evaluate conjoint experiment applications and understand the advantages/disadvantages compared to more traditional methods.
- implement your own conjoint experiment into an (online) survey platform.
- to understand and be able to apply different techniques to analyse conjoint experiments.
- be able to easily visualise the result of a conjoint experiment.
- prepare you for more advanced conjoint (and factorial experiments) courses or workshops.

Credits info

4 EC
You can earn up to four credits for attending this course.
3 ECTS credits – Attend 100% of live sessions and engage fully with class activities.
4 ECTS credits – Attend 100% of live sessions, engage fully with class activities and complete a post-class assignment.

Fee info

GBP 492: ECPR member - check whether your institution is a member here: https://ecpr.eu/Membership/CurrentMembers
GBP 985: ECPR non-member

Scholarships

Funding applications for the 2024 ECPR Methods School summer programme are now opening for applications. Apply before mid-April 2024. For more details on funding opportunities for ECPR's other events, please visit https://ecpr.eu/Funding/Funding