Lugano, Switzerland

Creating Groups from DatCluster Analysis and Latent Class Analysis

when 22 August 2022 - 27 August 2022
language English
duration 1 week
fee CHF 700

In this course, you will learn how to create groups from data.

For example, you might want to detect different types of web users based on a set of variables that contain information on aspects of online activities (e.g., content preferences, time spent online, etc.). Or you try to understand vaccination hesitancy by identifying groups of people with similar sets of concerns. In these kinds of scenario, we often face datasets with many observations and large numbers of potentially relevant variables. It would be impossible to find groups of similar cases by just browsing the data or skimming tables.

Discovering groups in your data can be achieved by performing cluster analysis or latent class analysis. Additionally, these techniques allow for a fruitful description of the phenomenon of interest and enable follow-up analyses, for example, on how group membership (e.g., type of web users) is associated with other variables (e.g., gender, SES, life satisfaction, personality).

Cluster analysis can be described as a bottom-up approach, where various algorithms are deployed to find similar cases (e.g., persons, organizations, schools, countries) in your data. Similar cases will be grouped to create a given number of maximally different clusters.
Latent class analysis, in contrast, can be seen to use a top-down approach where we assume a probabilistic model to explain group membership. Latent class analysis works with the actual distribution of your data using a statistical model. You can include covariates and the procedure will provide goodness of fit measures, which can be used to compare different solutions.

This course contains an introduction to both cluster analysis and latent class analysis. We will spend two days on cluster analysis, two on latent class analysis, and one day on more advanced techniques to group cases, including machine learning variants.

You will work on data provided by the course instructor. We encourage you strongly, however, to bring your data if possible.

Upon completion of this course, you will have a good understanding of cluster analysis and latent class analysis, their differences, advantages, and disadvantages. You will know how to run these models on your data.

Course leader

Robin Samuel, University of Luxemburg

Target group

While the course is designed to be introductory, participants should be familiar with univariate and bivariate statistics. If you have never been exposed to bivariate correlation and chi-square (e.g., in the context of cross-tabs, also known as contingency tables) this is probably not the course for you. Ideally, you will have some elementary knowledge of (OLS) regression as well.

We will use the software R. R allows running both cluster analyses and latent class analyses. While some familiarity with R would be useful, this is not strictly necessary as long as you have some knowledge of working with other statistical software packages using syntax (e.g., Stata or SPSS) and are willing to learn.

Course aim

This course contains an introduction to factor analysis and latent class analysis. Both analytical techniques allow detecting concepts or groups in your data that are not directly measurable or observable.

Credits info

The Summer School cannot grant credits. We only deliver a Certificate of attendance, i.e. we certify your

If you consider using Summer School workshops to obtain credits (ECTS), you will have to investigate at your home institution (contact the person/institute responsible for your degree) to find out whether they recognize the Summer School, how many credits can be earned from a workshop/course with roughly 35 hours of teaching, no graded work, and no exams.

Make sure to investigate this matter before registering, if this is important to you.

Fee info

CHF 700: Reduced fee: 700 Swiss Francs per weekly workshop for students (requires proof of student status).

These fees includes also participation in one of the preliminary workshops (two-day workshop preceding the Summer School).
To qualify for the reduced fee, you are required to send a copy of an official document that certifies your current student status or a letter from your supervisor stating your actual position as a doctoral or postdoctoral student. Send this letter/document by e-mail to
CHF 1100: Normal fee: 1100 Swiss Francs per weekly workshop for all others.
These fees includes also participation in one of the preliminary workshops (two-day workshop preceding the Summer School).


As the Summer School is financed through participant’s fees alone and has no funds of its own, it cannot offer any scholarship, grants or financial aid.