Abstract
Theoretical problems with the factor analysis model have resulted in increased interest in component analysis as an alternative. It is therefore of interest to assess empirically some of the asserted differences between the methods. One of the positive features sometimes attributed to factor analysis, as opposed to component analysis, is superior stability of results under sampling from a population of variables. The comparative stability of three methods, Maximum Liklihood Factor Analysis, principal component analysis, and rescaled image analysis, is investigated. Random samples are drawn from a "population" of variables. The sample pattern is then compared with the corresponding population pattern. The results suggest that none of the three methods can be considered superior with respect to stability.
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