Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger and Swineford ("Psychometrika" 47:41-54, 1937). The bi-factor model has a general factor, a number of group factors, and an explicit bi-factor structure. Jennrich and Bentler ("Psychometrika" 76:537-549, 2011) introduced an exploratory form of bi-factor…

Gives a set of minimally sufficient axioms to define and distinguish common factor theory, image theory, and component theory and analyzes claims that have been made for image theory as a device for improving factor theory. (Author/RC)

Explores the relationship between the higher-order factor model and the hierarchical factor model and shows that the Schmid-Leiman transformation process (J. Schmid and J. Leiman, 1957) produces constrained hierarchical factor solutions. Shows that the two models are not mathematically equivalent unless appropriate direct effects are added. (SLD)

Five techniques that combine the ideals of rotation of matrices of factor loadings to optimal agreement and rotation to simple structure are compared on the basis of empirical and contrived data. Combining a generalized Procrustes analysis with Varimax on the main of the matched loading matrices performed well on all criteria. (SLD)

Procedures developed by Joreskog for studying similarities and differences in factor structures between different groups were applied to data from a study by Thurstoen to investigate the sampling stability of a hypothesized isolated configuration. The hypothesis of an isolated configuration was rejected but not by much. (Author/RC)

Component loss functions (CLFs) are used to generalize the quartimax criterion for orthogonal rotation in factor analysis. These replace the fourth powers of the factor loadings by an arbitrary function of the second powers. Criteria of this form were introduced by a number of authors, primarily Katz and Rohlf (1974) and Rozeboom (1991), but there…

Applications to the Promax Rotation are discussed, and it is shown that these procedures solve Thurstone's hitherto intractable "invariant" box problem as well as other more common problems based on real data. (Author/RC)

The oblimax, promax, maxplane, and Harris-Kaiser techniques are compared. (Author)