There are some tests that you can use to decide whether your factor analysis offers a good fit for your data or not. For example, there is a test called Kaiser-Meyer-Olkin Measure of Sampling Adequacy (in short: the KMO test), which reflects the sum of partial correlations relative to the sum of correlations. It varies between 0 and 1, where a value closer to 1 is better. It has been suggested to use 0.5 as a minimum requirement. Thus, if the value is lower than 0.5, factor analysis may be inappropriate.