ISSN:
1860-0980
Keywords:
principal components analysis
;
common factor analysis
;
alternating least squares algorithms
;
identification
;
uniqueness
;
partitioning of fit
Source:
Springer Online Journal Archives 1860-2000
Topics:
Psychology
Notes:
Abstract Multitrait-Multimethod (MTMM) matrices are often analyzed by means of confirmatory factor analysis (CFA). However, fitting MTMM models often leads to improper solutions, or non-convergence. In an attempt to overcome these problems, various alternative CFA models have been proposed, but with none of these the problem of finding improper solutions was solved completely. In the present paper, an approach is proposed where improper solutions are ruled out altogether and convergence is guaranteed. The approach is based on constrained variants of components analysis (CA). Besides the fact that these methods do not give improper solutions, they have the advantage that they provide component scores which can later on be used to relate the components to external variables. The new methods are illustrated by means of simulated data, as well as empirical data sets.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF02294039
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