Factor analysis is a procedure used to determine the extent to which shared variance (the intercorrelation between measures) exists between variables or items within the item pool for a developing measure.
Factor analysis uses the correlation structure amongst observed variables to model a smaller number of unobserved, latent variables known as factors. Researchers use this statistical method when subject-area knowledge suggests that latent factors cause observable variables to covary. Use factor analysis to identify the hidden variables.
Analysts often refer to the observed variables as indicators because they literally indicate information about the factor. Factor analysis treats these indicators as linear combinations of the factors in the analysis plus an error. The procedure assesses how much of the variance each factor explains within the indicators. The idea is that the latent factors create commonalities in some of the observed variables.