Factor analysis is a statistical method used to describe variability among observed variables in terms of a potentially lower number of unobserved variables called factors. In other words, it is possible, for example, that variations in three or four observed variables mainly reflect the variations in a single unobserved variable, or in a reduced number of unobserved variables. Factor analysis searches for such joint variations in response to unobserved latent variables. Although many advantages, it also has some limitations which confine its use in certain conditions. The following are the limitations of factor analysis:
- Its usefulness depends on the researchers' ability to develop a complete and accurate set of product attributes - If important attributes are missed the value of the procedure is reduced accordingly.
- Naming of the factors can be difficult - multiple attributes can be highly correlated with no apparent reason.
- If the observed variables are completely unrelated, factor analysis is unable to produce a meaningful pattern (though the eigen values will highlight this: suggesting that each variable should be given a factor in its own right).
- If sets of observed variables are highly similar to each other but distinct from other items, Factor analysis will assign a factor them, even though this factor will essentially capture true variance of a single item.
- In factor analysis, each orientation is equally acceptable mathematically. But different factorial theories proved to differ as much in terms of the orientations of factorial axes for a given solution as in terms of anything else, so that model fitting did not prove to be useful in distinguishing among theories. This means all rotations represent different underlying processes, but all rotations are equally valid outcomes of standard factor analysis optimization. Therefore, it is impossible to pick the proper rotation using factor analysis alone.
- Factor analysis can be only as good as the data allows. In psychology, where researchers often have to rely on less valid and reliable measures such as self-reports, this can be problematic.
- Interpreting factor analysis is based on using a "heuristic", which is a solution that is convenient even if not absolutely true. More than one interpretation can be made of the same data factored the same way, and factor analysis cannot identify causality.
Even though there are various limitations, factor analysis is majorly used in today’s scenario for analysis of data due to its wider scope.
Prashant Pandey – 13155
Operations – Group 1