First, you have to check your data to see that the assumptions behind the factor analysis hold. If your data “passes” these assumptions, you will have a valid result.

Checklist

Ratio/interval/ordinal variables	Your variables should be continuous (i.e. interval/ratio) or ordinal (but still approximately continuous). For example: Income, height, weight, number of years of schooling, or ratings.
Linear associations	The variables in the factor analysis should be associated with each other in a linear fashion (use scatterplots to check, see Scatterplot).
Sample size	Factor analysis requires rather large samples. However, recommendations on this topic vary greatly. Some recommendations highlight the absolute sample size (here, lower limits range from n=100 to n=500) whereas others say that subject-to-variable ratio is important (and here, ratios from 2:1 to 20:1 are suggested).
No outliers	An outlier is an extreme (low or high) value. For example, if most individuals have a test score between 40 and 60, but one individual has a score of 96 or another individual has a score of 1, this will distort the test.

Suppose that we have asked a bunch of individuals six questions about their health. We conduct a factor analysis to see how many dimensions these questions reflect: do all questions reflect only one dimension (namely “health”) or can they be categorised into two or more dimensions (i.e. different types of health)?