This chapter introduces the broad concepts of factor analysis. 'Factor analysis' can be divided into two different statistical techniques such as exploratory factor analysis and confirmatory factor analysis. Well-known mathematical methods can be used to identify factors from groups of variables which tend to correlate together, and even the very largest factor analyses can be performed on a desktop computer. The amount of unique-factor variance in an item is reflected in its communality. A variable with a large communality has a large degree of overlap with one or more common factors. A low communality implies that all of the correlations between that variable and the common factors are small – that is, none of the common factors overlap much with that variable. It is sometimes possible to represent entire correlation matrices geometrically. The results obtained from a principal-components analysis always look more impressive than the results of a factor analysis.