The starting point for a factor analysis is a set of numerical variables, such as, for example, the sum of responses to a battery of statements. With the help of factor analysis, we investigate which variables capture similar aspects and then group them together (into a factor). For example, we measure attitudes towards technology based on a range of statements. By conducting a factor analysis, we gain an additional insight: Half of the items capture a latent feature that can be described as “affinity towards technology”, while the other half captures the “perceived complexity of technology”. Interestingly, those two features are independent of each other.
Factor analyses are used when dealing with a larger number of variables (observed features) and identifying the variables that have something in common. This is why this analytical method is often used when dealing with scale creation.
You want to track down the hidden common factors? We help you find those features that really are independent.