has_specified_output dimensionality reduction factor analysis term request from Ralf Weber and Gavin Lloyd, University of Birmingham Alejandra Gonzalez-Beltran Factor analysis is a dimension reduction data transformation that is used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. Factor analysis is related to principal component analysis (PCA), but the two are not identical. Both PCA and factor analysis aim to reduce the dimensionality of a set of data, but the approaches taken to do so are different for the two techniques. Factor analysis is clearly designed with the objective to identify certain unobservable factors from the observed variables, whereas PCA does not directly address this objective; at best, PCA provides an approximation to the required factors. Philippe Rocca-Serra adapted from Wikipedia: https://en.wikipedia.org/wiki/Factor_analysis last accessed: 04/07/2018 Cattell, R. B. (1952). Factor analysis. New York: Harper factanal(x, factors, data = NULL, covmat = NULL, n.obs = NA, subset, na.action, start = NULL, scores = c("none", "regression", "Bartlett"), rotation = "varimax", control = NULL, ...) https://stat.ethz.ch/R-manual/R-devel/library/stats/html/factanal.html loadings score