Factor analysis is a multivariate analysis techniques that is
frequently used in scientific studies to determine the structure of
multivariate data. Factor analysis gathers interrelated variables
from an event with multiple variables and aims to determine a few
new variables with no relationships between them. In other
words, it is a method of removing a dependence structure and
reducing the dimensions of a data set (Hair et al. 1998).
Factor analysis assumes that observed variables are linear
combinations of some underlying (hypothetical or unobservable)
factors. This assumption is based on the fundamental assumption
that certain underlying factors, of which there are fewer than the
number of observed variables, are responsible for covariation
among the observed variables (Kim and Mueller, 1978a, 1978b). In
addition, it is assumed that random error terms are uncorrelated
with each other and with the identified factors (Everitt and Hothorn,
2011).