Factor Analysis: A Short Introduction, Part 1 by Maike Rahn.
From the post:
Why use factor analysis?
Factor analysis is a useful tool for investigating variable relationships for complex concepts such as socioeconomic status, dietary patterns, or psychological scales.
It allows researchers to investigate concepts that are not easily measured directly by collapsing a large number of variables into a few interpretable underlying factors.
What is a factor?
The key concept of factor analysis is that multiple observed variables have similar patterns of responses because of their association with an underlying latent variable, the factor, which cannot easily be measured.
For example, people may respond similarly to questions about income, education, and occupation, which are all associated with the latent variable socioeconomic status.
I mention factor analysis as an example of
- reducing dimensionality
- exchanging a not easily measured latent variable for measurable ones
- attributing a relationship between a not easily measured latent variable and measurable ones
Factor analysis has been successfully used in a number of fields.
However, to reliably integrate information based on factor analysis you will need to probe the (often) unstated assumptions of such analysis.
PS: You may find the pointers in Wikipedia useful: Factor Analysis.