Wizyta dr Alexandru Cernat (University of Manchester)

Data: 3.10 (Czwartek)
Miejsce: 4.1 Budynek D (dawniej CEUE)
Godzina: 13:00-14:45
Zoom: https://ue-poznan-pl.zoom.us/j/91541105898?pwd=JaqNwhCmclaYcwiXHiJVkUSkCK9xiF.1 (meeting ID: 915 4110 5898, pass: 075437)

Tytuł: Introduction to Latent Class Analysis

Abstrakt:
Latent Class Analysis (LCA) is a branch of the more General Latent Variable Modelling approach. It is typically used to classify subjects (such as individuals or countries) in groups that represent underlying patterns from the data. In addition to this application LCA provides a flexible framework that can be used in a wide range of contexts: in longitudinal studies (e.g., mixture latent growth models, hidden Markov chains), in evaluation of data quality (e.g., extreme response style, cross-cultural equivalence), non-parametric multilevel, joint modelling for dealing with missing data.

In this course you will receive an introduction to the essential topics of LCA such as: what is LCA, how to run models, how to choose between alternative models, how to classify observations, how to evaluate and predict classifications. You will also apply this knowledge to a number of more advanced models that look at the relationship between latent variables and at longitudinal data.

Bio:
Alexandru Cernat is an associate professor in the social statistics department at the University of Manchester. He has a PhD in survey methodology from the University of Essex and was a post-doc at the National Centre for Research Methods and the Cathie Marsh Institute. His research and teaching focus on: survey methodology, longitudinal data, measurement error, latent variable modelling, new forms of data and missing data.

Webpage: https://www.alexcernat.com
Google Scholar: https://scholar.google.com/citations?user=GOxqSVcAAAAJ&hl=pl&oi=ao