Cologne, Germany

Latent Class Analysis

when 13 March 2023 - 17 March 2023
language English
duration 1 week
credits 3 EC
fee EUR 500

Latent class analysis is the name social scientists originally gave to the study of “mixtures of Bernoulli models” - the search for hidden groups in categorical data. Since then, the term has grown to mean almost any kind of model in which there are thought to be different groups, and the problem is that we do not know which groups. Examples are “latent profile analysis”, “Gaussian Mixture Modeling”, “mixture structural equation modeling”, “model-based clustering”, Hidden Markov modeling, and many many more. Latent class(-type) models have found application in market segmentation, ideal point modeling in political science, diagnostic test evaluation without a gold standard, probabilistic record linkage, disease stratification, image recognition, and student mastery models (CDM), to name just a few.

In this course, I will assume you know something about statistics, including linear and logistic regression, as well as programming in R. Starting from that assumption, we will work to understand the goals and promises of latent class modeling, broadly understood, as well as its inner workings in simple cases. You will learn to formulate and work with several variations of latent class model, as well as:

- What such models could be used for;
- How to evaluate and compare them;
- How they can be interpreted and what you cannot get out of them;
- What software options there are.

We will use R throughout because it is the most accessible option. However, using R for latent class modeling can be challenging: It requires navigating different, sometimes changing, R packages of varying quality. Moreover, for some techniques in use within (among others) the social sciences, no ready-made R package is available at all, and the user is required to get “creative”. For this reason, you will probably enjoy this course more if you feel rather comfortable with R and are not easily frustrated by error messages. (Chocolate may be provided to lower stress levels during key parts of the course. Also: GESIS will offer an online introductory course on R shortly before the Spring Seminar [22-24 Feb] that could help to brush up on your own knowledge regarding R.)

Course leader

Daniel Oberski is full professor of Social & Health Data Science at the department of Methodology & Statistics, Faculty of Social & Behavioural Sciences, Utrecht University.

Target group

Typical profile:

- PhD candidates, postdocs in the social and health sciences or similar, who are familiar with data analysis & interested in using the techniques discussed in this course.
- Everyone is welcome who fulfils the the following prerequisites:
- Knowledge of basic statistical data analysis, up to and including linear and (preferably also) logistic regression.
- Comfortable with R, not afraid of error messages.

> GESIS will offer an online introductory course on R shortly before the Spring Seminar (22-24 Feb) that could help to brush up on your own knowledge regarding R.

Credits info

3 EC
Optional bookings:
- 3 ECTS credit points via the University of Cologne for enrolled PhD students (with proof of status) per Spring Seminar course for active participation (20 EUR administration fee).

Fee info

EUR 500: Student/PhD student rate.

The rate includes the tuition fee, course materials, and coffee/tea breaks.
EUR 750: Academic/non-profit rate.

The rate includes the tuition fee, course materials, and coffee/tea breaks.

Scholarships

None available