This paper presents an analysis, based on simulation, of the stability
of principal components. Stability is measured by the expectation of
the absolute inner product of the sample principal component with the
corresponding population component. A multiple regression model to pre
dict stability is devised, calibrated, and tested using simulated Norm
al data. Results show that the model can provide useful predictions of
individual principal component stability when working with correlatio
n matrices. Further, the predictive validity of the model is tested ag
ainst data simulated from three non-Normal distributions. The model pr
edicted very well even when the data departed from normality, thus giv
ing robustness to the proposed measure. Used in conjunction with other
existing rules this measure will help the user in determining interpr
etability of principal components.