Latent Factor Analysis: 3 Dec 2014 – 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 that has been measured using a set of related observed indicators. Factor analysis is one way to derive a single 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 focus on confirmatory factor analysis, but talk a bit about the differences with exploratory factor analysis. 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 secondary data sets.
View original post 383 more words