GESIS Training Courses
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Scientific Coordination

Dr.
Sebastian E. Wenz
Tel: +49 221 47694-159

Administrative Coordination

Loretta Langendörfer M.A.
Tel: +49 221 47694-143

Latent Class Analysis

About
Location:
Cologne
 
Course duration
Mo: 10:00-17:00 CET
Tu-Fr: 09:00-16:00 CET
 
General Topics:
Course Level:
Format:
Software used:
Duration:
5 days
Language:
Fees:
Students: 500 €
Academics: 750 €
Commercial: 1500 €
Keywords
Additional links
Lecturer(s): Daniel Oberski

About the lecturer - Daniel Oberski

Course description

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.)


    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 prerequisites.
  •  
    Organizational structure of the course
    Monday, Tuesday, Wednesday, Thursday:
    Morning: Lectures 3 hours.
    Afternoon: Tutorial 3 hours.
     
    Friday: Tutorial and consultancy slots; bring your own data.


    Prerequisites

    Participants:
  • require knowledge of basic statistical data analysis, up to and including linear and (preferably also) logistic regression.
  • should be 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).
  •  
    Software and hardware requirements
    Participants will need to bring their own laptops with R (latest version) and R studio (or any other R IDE; latest version) installed. Participants will need to be able to install packages (i.e., have the according admin rights on their machines) and connect to the GESIS guest or Eduroam network through a wifi connection. Participants are expected to be comfortable with R and 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.
    If you want to brush up your knowledge in R, you might be interested in also taking part in the online-workshop Introduction to R in the week before the Spring Seminar (22-24 Feb).


    Recommended readings