Analyzing the factors
After generating the factors of our dataset, it would be interesting to understand the contribution of the original variables to the factors. Since factors are a linear combination of the original variables, they are typically less interpretable until they are analyzed. The goal is to understand the information that each factor conveys in order to name them accordingly. There are three concepts that aid the analysis of factors.
The first is the loadings; they express the relationship between the original variables and the underlying factors. In simple terms, it is basically the correlation coefficient between the variables and the underlying factors. The loading values range from -1 to 1, where values closer to -1 or 1 indicate that a factor has a significant influence on the variables.
The second is communality, which displays the proportion of each variable’s variance that is explained by the underlying factors.
The third is rotation, which rotates...
