On the generalizability of factors: The influence of changing contexts of variables on different methods of factor extraction

The influence of changing contexts of variables on results is often mentioned as a main problem of exploratory factor analysis limiting the generalizability of factors. In the present study, the influence of changing contexts of variables on results of different methods of factor extraction (principal component analysis, principal axis factor analysis, alpha factoring, and maximum likelihood factor analysis) was investigated by means of artificial data. In the first simulation study four factor solutions with pronounced simple structure were created on the basis of artificial data both with 200 and 1000 cases. These four factor solutions represented the context of variables in which a factor was identified. In the second simulation study, a context of variables was created, which completely dissolved one of the four factors composed by four marker variables in the previous study. These data were then analyzed by means of principal component analysis, principal axis factor analysis, alpha factor analysis, and maximum likelihood factor analysis. The factor was less dissolved in principal axis factor analysis, alpha factor analysis, and maximum likelihood factor analysis than in principal component analysis. Moreover, a slight overextraction may also be favorable for the identification of a dissolved factor. On the basis of the results, some recommendations were given in order to perform factor extraction in a way which maximizes the generalizability of factors.