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Friday 14 February 13:00–15:00 and 15:30–18:00
Saturday 15 February 09:00–12:30 and 14:00–17:30
This course is an introduction to the logic and basic notions of conjoint analysis (CJA). Particular attention will be paid to Choice-Based design: the analysis, the interpretation of the results, and the limitations of conjoint experiments. We will apply these notions by looking at different applications of conjoint analysis within the field of behavioural sciences. The course will focus in particular on:
1. What is a conjoint experiment?
2. The different type of conjoint analysis and their applicability in social sciences.
3. The advantages of conjoint experiments compared to other designs.
4. How to design and deploy a conjoint experiment
5. How to analyse and interpret the results of a conjoint experiment
1 credit (pass/fail grade). Attend at least 90% of course hours, participate fully in in-class activities, and carry out the necessary reading and/or other work prior to, and after, class.
Alberto Stefanelli is a FWO PhD Fellow at the Institute for Social and Political Opinion Research at KU Leuven and a Visiting Researcher at the Department of Political Science at Yale University and at the Department of Sociology at New York University
His research interests include radicalism, voting behaviour, democratic erosion, and political methodology.
Methods-wise, 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.
Estimating causal effects is a central aim of quantitative empirical analysis in social sciences. Recently, Conjoint Analysis and Choice-Based Conjoint Experiments have gained interest among social scientists to understand and predict people's preferences in a multi-dimensional and multi-choice environment. This course offers an applied introduction to Choice-Based Conjoint, along with hands-on experience in lab sessions.
By the end of the course, you will:
1. Have a basic understanding of the structure, logical underpinnings, basic notions, and analytical goals of conjoint analysis.
2. Identify areas of application where conjoint analysis could be successfully implemented.
3. Critically evaluate conjoint experiment applications and understand the advantages/disadvantages compared to traditional methods.
6. Implement your own conjoint experiment into an (online) survey platform.
7. Understand and be able to apply different techniques to analyse conjoint experiments.
8. Be able to efficiently visualise your results
10. Be well prepared for more advanced conjoint and factorial design courses or workshops.
The course is structured around six key topics:
Note The goal of this workshop is to 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.
The course assumes intermediate familiarity with the basis of experimental design, survey experiments and regression analysis.
The empirical analysis will be implemented using R. While example datasets and full syntax codes will be provided, intermediate knowledge of R is expected.
You should know how to:
More advanced knowledge of statistical computing, such as writing functions and loops, is helpful but not essential.
Day | Topic | Details |
---|---|---|
1 | Introduction History Utility Function and Design |
1.5h – Session I
1.5h – Session II
1.5h – Session III
|
2 | Deployment, Analysis and Visualisation |
1.5h – Session IV
1.5h – Session V
1.5h – Session VI
|
Day | Readings |
---|---|
1 |
Morton, R.B. & Williams, K. (2010) Gustafsson, A., Herrmann, A., Huber, F. (Eds.) (2010) Auspurg, K. & Hinz, T. (2015) Knudsen, E., & Johannesson, M. P. (2018) Hainmueller, J., & Hopkins, D. J. (2015) |
2 |
Hainmueller, J., Hangartner, D., & Yamamoto, T. (2015) Horiuchi, Yusaku, Daniel M Smith and Teppei Yamamoto. 2015 Strezhnev, A., Hainmueller, J., Hopkins, D. J., & Yamamoto, T. (2013) Leeper, T. J., Hobolt, S. B., & Tilley, J. (2018) Kaczmirek, L. (2015) Toepoel, V. (2016) Callegaro, M., Manfreda, K. L., and Vehovar, V. (2015) |
Please bring your laptop with the latest version of R (3.6.x, Planting of a Tree), R Studio (1.2.x) and Python 2.7 installed. Note that Python 2.7 and Python 3.7 are not the same. Unfortunately, the softwere required by the randomisation mechanism is still running on Python 2.7.
Make sure they work and that you can run a script before coming to class since we will have no time to resolve technical issues. If you have already collected data, bring it along. If not, you’ll get a toy dataset to play with. Be sure to have installed in R the cjoint and cregg packages together with any other package that you use for data management/cleaning/visualisation (e.g. dplyr,ggplot, etc).
This course will use R, which is a free and open-source programming language primarily used for statistics and data analysis. Although you are allowed to use other solutions, we will also use RStudio, which is an easy-to-use interface to R.
If you encounter any problems with R Studio on your local machine, use R Studio Cloud.
Please bring your laptop.
There are no specific requirements other than being able to use a browser (for dataset downloads and R Studio Cloud) and having installed R Studio and Python 2.7.
Survey Design
Logistic Regression and General Linear Models
Methods of Modern Causal Analysis Based on Observational Data
Multi-Level Modelling