Introduction to Latent Class Analysis (few places remaining)
|The University of Manchester|
Dr. Alexandru Cernat
14/03/2019 - 15/03/2019
Cathie Marsh Institute
View in Google Maps (M13 9PL)
Claire Spencer, 0161 275 4579, firstname.lastname@example.org
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 models, 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 class variables and at longitudinal data.
The course covers:
By the end of the course participants will:
Knowledge of basic categorical analysis: (marginal) probabilities, odds ratios, logistic regression.
Day 1 – introduction to LCA
Day 2 – applications of LCA
Intermediate (some prior knowledge)
For UK registered postgraduate students £30 per day
Website and registration
Quantitative Data Handling and Data Analysis, Latent class growth analysis, Latent Variable Models, Latent class analysis, Latent profile analysis, Latent trait analysis
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