title: Three-mode analysis of multimode covariance matrices.
abstract: Multimode covariance matrices, such as multitrait-multimethod matrices, contain the covariances of subject scores on variables for different occasions or conditions. This paper presents a comparison of three-mode component analysis and three-mode factor analysis applied to such covariance matrices. The differences and similarities between the non-stochastic and stochastic approaches are demonstrated by two examples, one of which has a longitudinal design. The empirical comparison is facilitated by deriving, as a heuristic device, a statistic based on the maximum likelihood function for three-mode factor analysis and its associated degrees of freedom for the three-mode component models. Furthermore, within the present context a case is made for interpreting the core array as second-order components.
authors: Department of Education, Leiden University, Leiden, The Netherlands. kroonenb@fsw.leidenuniv.nl
authors: Pieter M
authors: PM
authors: Kroonenberg
authors: Frans J
authors: FJ
authors: Oort
mesh_terms: D000704:Analysis of Variance; D006801:Humans; D008432:Mathematical Computing; D015233:Models, Statistical; D011581:Psychological Tests / Q000706:statistics & numerical data*; D011589:Psychology, Experimental / Q000706:statistics & numerical data; D011594:Psychometrics / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012984:Software