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Introduction to Latent Class Analysis

Date - Thursday, 16th November 2017 - Friday, 17th November 2017 (All Day)

Course Code HUB-11-16/17-P-R
Organised by NCRM, University of Southampton
Presenter Dr Alexandru Cernat
Date 16/11/2017 – 17/11/2017
Venue Building 39, University of Southampton, Highfield, Southampton, Hants,
Map View in Google Maps  (SO17 1BJ)
Contact Jacqui Thorp
Training and Capacity Building Co-ordinator
National Centre for Research Methods
University of Southampton
Tel: 02380 594069
Email: jmh6@soton.ac.uk
Description 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:

  • Refresher of basic concepts in categorical analysis: (marginal) probability, odds ratios, logistic regression;
  • Basic concepts and assumptions of latent class analysis;
  • Introduction to Latent GOLD software;
  • Model fit evaluation: global, local and substantive evaluation;
  • Classification of cases;
  • Apply these concepts to a number of models looking at: predicting class membership, relationships between latent classes, hidden Markov chains.

By the end of the course participants will:

  • Know what is Latent Class Analysis;
  • Be able to estimate and interpret results from Latent Class Analysis;
  • Be able to choose between alternative Latent Class Models;
  • Understand latent class classification and how to predict it;
  • Be able to investigate the relationship between latent class variables.

The course is aimed at people from all disciplines and types of institutions that want to learn about latent class analysis or, more generally, about latent variable modelling.


Knowledge of basic categorical analysis: (marginal) probabilities, odds ratios, logistic regression and of linear regression

Preparatory Reading

For an introduction to Latent Class Analysis:

Collins, L. M., & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis: with Applications in the Social, Behavioral, and Health Sciences (1 edition). Hoboken, N.J: Wiley-Blackwell.

Further reading

Applications of Latent Class Analysis:

Hagenaars, J., & McCutcheon, A. (Eds.). (2009). Applied Latent Class Analysis (1 edition). Cambridge; New York: Cambridge University Press.

Reading on categorical data analysis:

Agresti, A. (2007). An Introduction to Categorical Data Analysis (2nd Revised edition edition). Hoboken, NJ: John Wiley & Sons.

Level Intermediate (some prior knowledge)
Cost The fee per teaching day is:• £30 per day for UK/EU registered students
• £60 per day for staff at UK/EU academic institutions, UK/EU Research Councils researchers, UK/EU public sector staff and staff at UK/EU registered charity organisations and recognised UK/EU research institutions.
• £220 per day for all other participantsAll fees include event materials, lunch, morning and afternoon tea. They do not include travel and accommodation costs.

Website and registration
Region South West
Keywords Longitudinal Data Analysis, Latent Variable Models, Latent class analysis, Latent GOLD , , Categorical Data Analysis , , Multi-group Analysis , , Longitudinal Analysis
Related publications and presentations Longitudinal Data Analysis
Latent Variable Models
Latent class analysis