ABSTRACT
Factor analysis is a statistical method to identify unobservable underlying factors that possibly lie behind the observed values of several correlated continuous variables. The term factor analysis was coined by Charles Spearman in 1904, and the method is also generally ascribed to him [1]. How do you assess the health of a child? You will see his/her height, weight, skinfold, eyes, nails, etc. Health is not directly observable but is measured through a host of such indirect variables. Conversely, you may have a variety of variables, and you want to know what factors are underlying those variables to cause those values. There are situations where the observed variables can be considered to be made up of a small number of unobservable, sometimes abstract, factors. These factors can also be considered as constructs that are latent in the data. A latent construct can be measured indirectly by determining its influence on responses on measured variables. The purpose of factor analysis is to unravel these factors.
