Introduction to Conjoint Experiments

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.

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.