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.

## Where to find this analysis

To perform a factor analysis, choose .

## When to use an alternate analysis

If you want to create new variables that are expressed as linear combinations of the original variables, use Principal
Components Analysis.