process occurs that produces a matrix of correlations between the original
variables and the underlying common factors isolated in the analysis
(Figure 8.6b).
The matrix entries in Figure 8.6b is a rather ideal example in which each
variable has a high association with one of the factors, but a negligible rela-
tionship to the others. The factors themselves are statistically independent,
but they could be correlated at the same time. Statisticians apply further
computations (factor rotation) to tweak the factors in the correlated or
uncorrelated condition to produce the most interpretable set of final factors. ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month, and much more.