Latent Factor Analysis Short Course
Time: 09:30 - 16:30
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This short course covers latent variables and factor analysis at an introductory and intermediate level. A latent variable is something invisible (such as a concept, an attitude, or an illness) that cannot be measured directly, but that has been measured using a set of related observed indicators.
Factor analysis is one way to derive a factor from a set of variables, and is thus called a data reduction method. Other data reduction methods include principal components analysis, which is very closely related to factor analysis, and multiple correspondence analysis.
We will cover both exploratory and confirmatory factor analysis, and highlight their differences. More advanced topics, such as testing for measurement equivalence, will also be touched upon. The course is suitable both for primary-data collection researchers (who may need to write a suitable questionnaire), and for those who want to analyse data sets, with a focus on measurement issues.
On completing the course, participants will have covered the following:
- Master the basics of factor analysis
- Learn to test how many factors are needed in a factor model
- Explore more advanced confirmatory models for coping with measurement artefacts and test for measurement equivalence
- Help participants become familiar with examples from social science research where latent variables are useful
Course fee: £140 for those from educational, government and charitable institutions; £195 others.