Factor analysis is a
statistical method used to describe
variability among observed, correlated
variables in terms of a potentially lower number of unobserved variables called
factors. For example, it is possible that variations in say six observed variables mainly reflect the variations in two unobserved (underlying) variables. Factor analysis searches for such joint variations in response to unobserved
latent variables. The observed variables are modelled as
linear combinations of the potential factors, plus "
error" terms. The information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a dataset. Factor analysis originated in
psychometrics and is used in behavioral sciences,
social sciences,
marketing,
product management,
operations research, and other fields that deal with data sets where there are large numbers of observed variables that are thought to reflect a smaller number of underlying/latent variables.