Latent Class Analysis
Date:
05/08/2014
Organised by:
University of Ulster
Presenter:
Dr Gillian Shorter (University of Ulster)
Level:
Entry (no or almost no prior knowledge)
Contact:
Dr Gillian Shorter gw.shorter@ulster.ac.uk
Map:
View in Google Maps (BT48 7JL)
Venue:
University of Ulster, Magee campus,
Northland Road, Londonderry
Description:
Latent class analysis (LCA) is an important approach for understanding diverse behaviours in population by helping to characterise the nature of complex groups. This one day course will teach respondents how to conduct LCA in categorical data, and explore the nature of these groups using correlated variables. In addition to LCA, other related techniques will be discussed, such as Latent Profile Analysis, longitudinal methods and latent class factor analysis. The course aims to provide the participants with skills to perform a LCA with regression, and understand the wide utility of this technique.
Background reading
McCutcheon, A.L. (1987). Latent Class Analysis (Quantitative Applications in the Social Sciences). Sage: Thousand Oaks, CA.
Smith, G.W. (Shorter, G.W.), Farrell, M., Bunting, B.P., Houston, J.E., Shevlin, M. (2011). Patterns of polydrug use in Great Britain: findings from a National Household Population Survey. Drug and Alcohol Dependence. 113 (2-3):222-8.
Cost:
£120 per day; Reduced to £60 per day for postgraduates, unwaged, or those who work for Charitable organisations (on proof of status). If you book 4 or more days from the list http://science.ulster.ac.uk/bamfordcentre/summer-school/index.php contact flexed@ulster.ac.uk before you book to arrange an extra day training at no additional cost.
Website and registration:
http://store.ulster.ac.uk/browse/extra_info.asp?compid=1&modid=5&deptid=15&catid=4&prodid=29
Region:
Northern Ireland
Keywords:
Cross-Sectional Research, Quantitative Data Handling and Data Analysis, Regression Methods, Latent Variable Models, Logistic regression , Latent class analysis
Related publications and presentations:
Cross-Sectional Research
Quantitative Data Handling and Data Analysis
Regression Methods
Latent Variable Models