The paper shows how different versions of factor analysis applied to the same data can lead to different results. In contrast, factor analyses of data which differ can lead to the same results. These are two major limitations of the technique. Two alternative analysis procedures are described. Firstly, any clusters in the data can readily be seen from the correlation matrix if the correlations are rounded to one or two digits and the variables are ordered according to the average size of their correlations. Secondly, since correlations only reflect the strength of a relationship and not its nature, a further alternative is to establish the actual relationships. This shows up the same kinds of clustering as factor or correlational analysis do at their best, but also adds real hut simple quantification. It leads to a depth of understanding and relative ease of communication which seem impressive.
- This could also be of interest