Abstract
A relatively simple technique for .assessing the convergence of sets of variables across method domains is presented. The technique, two-step principal components analysis, empirically orthogonalizes each method domain into sets of components, and then analyzes convergence among components across domains. The proposed technique is directly compared with Jackson's (1969) multi-method factor analysis (which involves an a priori orthogonalization) in the analysis of data from personality, vocational interest and aptitude domains. While Jackson's technique focuses on individual variables, and the two-step procedure focuses on the components of variable domains, both techniques produced evidence of cross-domain convergence. However, Jackson's method was found t o have several undesirable mathematical and interpretational consequences, while the two-step procedure appears to be a promising technique for the systematic, empirical analysis of multitrait-multimethod matrices.
Keywords
OrthogonalizationPrincipal component analysisConvergence (economics)A priori and a posterioriVariable (mathematics)Domain (mathematical analysis)Simple (philosophy)Computer scienceMathematicsAlgorithmArtificial intelligence
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Publication Info
- Year
- 1974
- Type
- article
- Volume
- 9
- Issue
- 4
- Pages
- 479-496
- Citations
- 39
- Access
- Closed
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Cite This
Stephen L. Golding,
Edward Seidman
(1974).
ANALYSIS OF MULTITRAIT-MULTIMETHOD MATRICES: A TWO STEP PRINCIPAL COMPONENTS PROCEDURE.
Multivariate Behavioral Research
, 9
(4)
, 479-496.
https://doi.org/10.1207/s15327906mbr0904_7
Identifiers
- DOI
- 10.1207/s15327906mbr0904_7
- PMID
- 26754603