Factor analysis

Although the literature on confirmatory factor analysis (FA) is really impressive and is being highly used in, for example, social sciences, we will only focus on exploratory FA, where our goal is to identify some unknown, not observed variables based on other empirical data.

The latent variable model of FA was first introduced in 1904 by Spearman for one factor, and then Thurstone generalized the model for more than one factor in 1947. This statistical model assumes that the manifest variables available in the dataset are the results of latent variables that were not observed but can be tracked based on the observed data.

FA can deal with continuous (numeric) variables, and the model states that each observed variable is the sum of some unknown, latent factors.

Note

Please note the that normality, KMO, and Bartlett's tests are a lot more important to check before doing FA compared to PCA; the latter is a rather descriptive method while, in FA, we are actually building a model...