Use Factor Analysis to assess the structure of your data by evaluating the correlations between variables. Factor analysis summarizes data into a few dimensions by condensing a large number of variables into a smaller set of latent factors that you do not directly measure or observe, but which may be easier to interpret. Using this analysis, you can model each original variable as a linear function of these underlying factors. Factor analysis is commonly used in the social sciences, market research, and other industries that use large data sets.
For example, a credit card company uses factor analysis to ensure that a customer satisfaction survey address three factors before sending the survey to a large number of customers. If the survey does not adequately measure the three factors, then the company should reevaluate the questions and retest the survey before sending it to customers.
To perform a factor analysis, choose.
If you want to create new variables that are expressed as linear combinations of the original variables, use Principal Components Analysis.