This course introduces the fundamental principles of Latent Class Analysis (LCA). Latent class analysis is a statistical method for identifying unmeasured class membership among participants using categorical and/or continuous observed variables (latent profile analysis). For example, you may wish to categorise people based on their drinking behaviours (observations) into different types of drinkers (latent classes). This could lead to finding categories such as abstainers, social drinkers, and alcohol abusers. You can use multinomial logistic regression to create models to predict why individual fall into a particular classes (e.g., why do people become alcohol abusers). LCA can be used in many disciplines such as Health Sciences, Psychology, Education, Business and Social Sciences.
The seminar uses Mplus software to demonstrate the implementation of latent class analysis and the course is run by Dr Daniel Boduszek who has used latent class analysis in his research. The Quantitative Research Methods Training Unit (QRM-TU) will also invite Associate Members to lead the training.
The course is designed for researchers and postgraduate students who are engaged in research with large data sets. The prerequisite for taking this seminar is basic knowledge of regression analysis. Researchers from economics, public health, epidemiology, psychology, sociology, social work, medical research, education, and similar disciplines are welcome.
The Quantitative Research Methods Training Unit (QRM-TU), Ramsden Building, University of Huddersfield.
To find out when the next training session will be held, please visit our online store where you can also book your place.