Some Mathematical Properties of the Matrix Decomposition Solution in Factor Analysis

Kohei Adachi () and Nickolay T. Trendafilov
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Kohei Adachi: Osaka University
Nickolay T. Trendafilov: Open University

Psychometrika, 2018, vol. 83, issue 2, No 8, 407-424

Abstract: Abstract A new factor analysis (FA) procedure has recently been proposed which can be called matrix decomposition FA (MDFA). All FA model parameters (common and unique factors, loadings, and unique variances) are treated as fixed unknown matrices. Then, the MDFA model simply becomes a specific data matrix decomposition. The MDFA parameters are found by minimizing the discrepancy between the data and the MDFA model. Several algorithms have been developed and some properties have been discussed in the literature (notably by Stegeman in Comput Stat Data Anal 99:189–203, 2016), but, as a whole, MDFA has not been studied fully yet. A number of new properties are discovered in this paper, and some existing ones are derived more explicitly. The properties provided concern the uniqueness of results, covariances among common factors, unique factors, and residuals, and assessment of the degree of indeterminacy of common and unique factor scores. The properties are illustrated using a real data example.

Keywords: exploratory factor analysis; model identifiability; covariances between factors and residuals; higher rank approximation; factor indeterminacy (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11336-017-9600-y

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