Introduction to Latent Class Analysis
Alexandru Cernat, University of Manchester
9.00-12.00 July 17th 2017
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 evaluation of data quality (e.g., extreme response style, cross-cultural equivalence), in longitudinal studies (e.g., mixture latent growth models, hidden Markov chains), in non-parametric multilevel models, or in 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 and how to predict class membership. You will also see a number of more advanced models that look at longitudinal data. During the course the instructor will show applications of LCA using Latent Gold. The students will also receive data and syntax that runs some of these models using the free software R so they can use it at a later date.
About the instructor:
Dr. Alexandru Cernat is a lecturer in Social Statistics at the University of Manchester. Previously he was a Research Associate at the Cathie Marsh Institute for Social Research and the National Centre for Research Methods, University of Manchester where he investigated non-response in longitudinal studies with a special focus on biomarker data. He has received a PhD in survey methodology from the University of Essex working on the topic of mixed mode designs in longitudinal studies.