This chapter considers factor analysis technique and outlines a technique known as confirmatory factor analysis, which is useful when the researcher has a clear expectation about what factor structure should emerge. The basic idea behind hierarchical factor analysis is very simple. It is possible to factor analyse the correlations between the rotated primary ability factors. Factor analysing the correlations between people's scores on several such tests might reveal that they all intercorrelate and form a single visualisation factor. Estimating the correlations between factors with precision becomes particularly important when performing hierarchical analyses, as the correlations between the factors will form the correlation matrix to be analysed at the next-highest level of the hierarchy. The chapter performs the Hendrickson–White transformation and shows the loadings of the variables on the two second-order factors. The Schmid–Leiman technique is conceptually very similar to the Hendrickson–White methodology. Psychologists have rediscovered bifactor models–originally due to Holzinger and Swineford.