The factor analysis literature includes a range of recommendations regarding the
minimum sample size necessary to obtain factor solutions that are adequately stable
and that correspond closely to population factors. A fundamental misconception
about this issue is that the minimum sample size, or the minimum ratio of sample
size to the number of variables, is invariant across studies. In fact, necessary sample
size is dependent on several aspects of any given study, including the level of
communality of the variables and the level of over determination of the factors. The
authors present a theoretical and mathematical framework that provides a basis for
understanding and predicting these effects. The hypothesized effects are verified by
a sampling study using artificial data. Results demonstrate the lack of validity of
common rules of thumb and provide a basis for establishing guidelines for sample
size in factor analysis